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  • Published: 09 November 2016

Game development software engineering process life cycle: a systematic review

  • Saiqa Aleem   ORCID: orcid.org/0000-0002-3385-0613 1 ,
  • Luiz Fernando Capretz 2 &
  • Faheem Ahmed 3  

Journal of Software Engineering Research and Development volume  4 , Article number:  6 ( 2016 ) Cite this article

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Software game is a kind of application that is used not only for entertainment, but also for serious purposes that can be applicable to different domains such as education, business, and health care. Multidisciplinary nature of the game development processes that combine sound, art, control systems, artificial intelligence (AI), and human factors, makes the software game development practice different from traditional software development. However, the underline software engineering techniques help game development to achieve maintainability, flexibility, lower effort and cost, and better design. The purpose of this study is to assesses the state of the art research on the game development software engineering process and highlight areas that need further consideration by researchers. In the study, we used a systematic literature review methodology based on well-known digital libraries. The largest number of studies have been reported in the production phase of the game development software engineering process life cycle, followed by the pre-production phase. By contrast, the post-production phase has received much less research activity than the pre-production and production phases. The results of this study suggest that the game development software engineering process has many aspects that need further attention from researchers; that especially includes the postproduction phase.

1 Introduction

With the rapid advancement of computer technology, the significance of software engineering in our daily lives is increasing. It affects every aspect of our lives today, including working, living, learning, and education. A new and popular mode of entertainment and an important application of technology are software games, which have become increasingly accepted by people of all ages. In today’s culture, technology is easily accessible and has become more convenient; more and more people like to play games and are also becoming motivated to design their own games. Salen and Zimmerman ( 2003 ) defined “game is a software application in which one or more players make decisions by controlling game objects and resources, in the pursuit of its goal”. Software games are software applications that are installed on hardware devices such as video game consoles, computers, handheld devices, and Personal Digital Assistants (PDAs). Software games have now become a worldwide creative industry, but because of the multidisciplinary activities required, their development is a very complex task.

The multidisciplinary nature of the processes that combine sound, art, control systems, artificial intelligence (AI), and human factors, also makes the software game development practice different from traditional software development. However, despite the high complexity of the software engineering development process, the game industry is making billions of dollars in profit and creating many hours of fun (PWC, 2011–2014 outlook). The software game market throughout the world has grown by over 7–8 % annually and has reached sales of around $5.5 billion in 2015 (SUPERDATA 2015 ). Newzoo Game Market ( 2015 ) has also reported that the world-wide digital game market will reach $113.3 billion by 2018.

Creation of any game involves cross-functional teams including designers, software developers, musicians, script writers, and many others. Also, Entertainment Software Association (ESA) ( 2014 ); 2015 ) reports highlighted the latest trends about the software game industry. Therefore, game development careers have currently become highly challenging, dynamic, creative, and profitable (Liming and Vilorio, 2011 ). The ability to handle complex development tasks and achieve profitability does not happen by chance, but rather a common set of good practices must be adopted to achieve these goals. The game industry can follow the good and proven practices of traditional software engineering, but only a clear understanding of these practices can enhance the complex game development engineering process.

The computer game domain covers a great variety of player modes and genres (Gredler, 1995 ; Gredler, 2003 ; Rieber, 2005 ). The complexity of software games has posed many challenges and issues in software development engineering process because it involves diverse activities in creative arts disciplines (storyboarding, design, refinement of animations, artificial intelligence, video production, scenarios, sounds, marketing, and, finally, sales) in addition to technological and functional requirements (Keith, 2010 ). This inherent diversity leads to a greatly fragmented domain from the perspectives of both underlying theory and design methodology. The software game literature published in recent years has focused mainly on technical issues. Issues of game production, development, and testing reflect only the general software-engineering state of the art. Pressman ( 2001 ) states that a game is a kind of software that entertains its users, but game development software engineering faces many challenges and issues if only a traditional software-development process is followed (Kanode and Haddad, 2009 ; Petrillo et al., 2009 ). Some studies have proposed a Game Development Software Engineering (GDSE) process life cycle that provides guidelines for the game development software engineering process (Hendrick, 2014 ; Blitz game studio, 2014 ; McGrath, 2014 ; Chandler, 2010 ; Ramadan and Widyani 2013 ). However, the proposed GDSE process life cycle development phases do not ensure a quality development process.

A GDSE process is different from a traditional software development engineering process, and all phases of the proposed GDSE process life cycle can be combined into three main phases: pre-production, production, and post-production. The pre-production phase includes testing the feasibility of target game scenarios, including requirements engineering marketing strategies; the production phase involves planning, documentation, and game implementation scenarios with sound and graphics. The last phase post-production involves testing, marketing, and game advertising. Because of high competition and extreme market demand, game development companies sometimes reduce their development process so they can be first to market (Kaitilla, 2014 ). This reduction of the development process definitely affects game quality. Because of these types of complex project-management tasks, the game development software engineering process diverges from traditional software development. Therefore, it becomes important now to investigate the challenges or issues faced by game development organizations in developing good quality games. This systematic literature review is the first step towards identifying the research gaps in the GDSE field.

1.1 Related work

Managing GDSE process life cycle has become a much harder process than anyone could have initially imagined, and because of the fragmented domain, no clear picture of its advancement can be found in the literature. A systematic literature review provides a state of the art examination of an area and raises open research questions in a field, thus saving a great deal of time for those starting research in the field. However, to the best of the authors’ knowledge, no systematic literature review has been reported for GDSE process life cycle. Many researchers have adopted the systematic literature review approach to explore different aspects in software games. Boyle et al. ( 2012 ) conducted a systematic literature review to explore the engagement factor in entertainment games from a player’s perspective. In this study, 55 papers were selected to perform the systematic literature review. The study highlighted the different aspects of engagement factors with entertainment games; these include subjective feelings of enjoyment, physiological responses, motives, game usage, player loyalty, and the impact of playing games on a player’s life. Connolly et al. ( 2012 ) explored 129 papers to report the impacts and outcomes of computer and serious games with respect to engagement and learning by using the systematic literature review approach.

Another study also reported the importance of engagement in digital games by using a systematic literature review approach. Osborne-O’Hagan et al. ( 2014 ) performed a systematic literature review on software development processes for games. A total of 404 studies were analyzed from industry and academia and different software development adoption models used for game development were discussed. The findings of the study were that qualitative studies reported more agile practices than the hybrid approach. The quantitative studies used an almost hybrid approach. We also noted that lightweight agile practices such as Scrum, XP, and Kanban – are suitable where innovation and time to market is important. A risk-driven spiral approach is appropriate for large projects. Only one systematic study was performed related to research on software engineering practices in the computer game domain rather than GDSE process life cycle (Ampatzoglou and Stamelos 2010 ).

This study mainly review the existing evidence in the literature concerning the GDSE process research and suggest areas for further investigation by identifying possible gaps in current research. Furthermore, the aim of this study is to cover the state of the art for the GDSE process life cycle, and to accomplish this, an evidence-based research paradigm has been used. In the software engineering field, possible use of an evidence-based paradigm have been proposed by Dyba et al. ( 2005 ) and Kitchenham et al. (2004). The Systematic Literature Review (SLR) research paradigm constitutes the first step in an evidence-based paradigm research process, and its guidelines for performing systematic research are thoroughly described by Brereton et al. ( 2007 ) and Kitchenham ( 2004 ).

The rest of the paper is organized as follows: Section 2 provides the research background and Section 3 describes the methodology used for the systematic literature review as described by Breton et al. (2007). Section 4 presents the statistics for the primary studies, Section 5 answers various research questions, Section 6 discuss the external threats to validity, and, finally, Section 7 concludes the presentation.

2 Background

In the software development industry, software games are gaining importance because they are not only used for entertainment, but also for serious purposes that can be applicable to different domains such as education, business, and health care. Serious games are designed to have an impact on the target audience similar to entertainment games but they are combined seemingly with a practical dimension too. Both have to be attractive and appealing to a broad target audience (Alvarez & Michaud, 2008 ). Especially for serious games, along with their applicability to different domains, their revenue has also been increasing. Games software earned three times more revenue than any other software product in 2012 (Nayak, 2013 ).

Robin ( 2009 ) defines a development method as a systematized procedure to achieve the goal of producing a working product within budget and on schedule. A number of methodologies used for game development and design (Castillo 2008 ). The first is the waterfall method, which is also commonly used in traditional software development. Unlike game projects, once the pre-production phase is completed, production phase activities are performed in a “waterfall” manner. First, the activities are segregated based on functionalities and assets, and then they are assigned to their respective teams. The requirements team spent a significant amount of time in functionality definition and front-end activities, which implies a late implementation of level and mechanisms (Schwaber & Beedle, 2002 ). However, in the waterfall method, it is difficult to reverse any activity (Flood, 2003 ).

The second development methodology is the agile method that is commonly used for game development. These methods are highly iterative and not documentation-centric. The production phase is divided into small iterations and focusses on the most crucial features. During the beginning phase of each iteration, the whole team meets and sets clear objectives. At the end of each iteration, results are communicated to clients. These methods support different team cycles and dynamics through daily meetings. The most used agile methodologies in game development are extreme programming (XP), rapid prototyping, and Scrum (Godoy & Barbosa, 2010 ).

The unified development process (Kruchten, 2000 ) is another traditional SE method, which focusses more on analyzing requirements and converting them into functional software components. The requirement analysis document includes a definition of the game concept, use cases, and assets definitions (Schwaber & Beedle, 2002 ). The method includes five disciplines: requirements, analysis, design, implementation, and testing. The unified process is based on a philosophy of four key elements: iterative and incremental, use case-driven, architecture-centric, and risk-driven.

Kanode and Haddad ( 2009 ) stated that an important, but incorrect, assumption was made that GDSE follows the waterfall method. More recently, researchers have agreed that it must follow the incremental model (Munassar and Govardhan 2010 ) because it combines the waterfall method with an iterative process. A major concern, reported by Petrillo et al. ( 2009 ), was that very poor development methodologies are commonly used by developers for software creation in the game industry. The GDSE appears as a question in many forms attempting to determine what types of practices are used. However, there is no single answer to this question. Few researchers have explored GDSE practices and then tried to answer questions like the phases of the GDSE process life cycle. Blitz game studios ( 2014 ) proposed six phases for the GDSE process life cycle: Pitch (initial design and game concept), Pre-production (game design document), Main production (implementation of game concepts), Alpha (internal testers), Beta (third-party testers), and the Master phase (game launch). Hendrick ( 2014 ) proposed a five-phase GDSE process life cycle consisting of Prototype (initial design prototype), Pre-production (design document), Production (asset creation, source code, integration aspects), Beta (user feedback), and, finally, the Live phase (ready to play). McGrath ( 2014 ) divided the GDSE process life cycle into six phases: Design (initial design and game design document), Develop/redevelop (game engine development), Evaluate (if not passed, then redevelop), Test (internal testing), Review release (third-party testing), and Release (game launch). Another GDSE process life cycle proposed by Chandler ( 2010 ) consisted of four phases: Pre-production (design document and project planning), Production (technical and artistic), Testing (bug fixing), and, finally, the Post-production phase (post-mortem activities). The latest GDSE process life cycle in 2013 proposed by Ramadan and Widyani ( 2013 ) was based on the four GDSE process life cycles previously described. They proposed six phases: Initiation (rough concept), Pre-production (creation of game design and prototype), Production (formal details, refinement, implementation), Testing (bug reports, refinement testing, change requests), Beta (third-party testers), and Release (public release).

In traditional software engineering, the development phase usually involves activities such as application design and its implementation; the production phase is when the software actually runs and is ready for use. However, in the GDSE process lifecycle, the production phase includes the development process, which is the pre-production phase of the traditional software engineering process, and the production phase of traditional software engineering is actually the post-production phase of the GDSE process life cycle (Bethke, 2003 ). Therefore, the GDSE process life cycle is different from the traditional software engineering process, and many researchers have studied the challenges faced by this domain (Kanode and Haddad, 2009 ). The most prominent observation made in these studies is that to address the challenges faced by the GDSE process life cycle, more rigorous software engineering strategies must be used. Most researchers have explicitly compared the software engineering process with the GDSE process, but none of them has studied complete GDSE process life cycle and research topics under this domain in detail. This study will provide evidence on these topics and their differences from the traditional software engineering process. In this paper, the GDSE process phases were divided into three phases for basic understanding: Preproduction, Production, and Post-production. Efforts were made to classify these further based on studies found in the literature. The primary contribution of this paper is that it is the first SLR that addresses these GDSE process life cycle research topics and highlights the topics that need further attention by researchers.

In this work, the conceptual description of the SLR process presented by Kitchenham ( 2004 ) was used to investigate the research intensity for each phase of the GDSE process life cycle. Conceptually, SLR provides an opportunity for researchers to collect empirical evidence from the existing literature about a formulated research question. Although most authors followed the general SLR guidelines provided by Kitchenham ( 2004 ), there were slight variations in the description and presentation of the conceptual process layout. The generic SLR guidelines stated by Kitchenham ( 2004 ) are further elaborated here, and the overall process is described as a set of activities The research process has been adopted for this study described by Kitchenham and Charters ( 2007 ). There are mainly three phases of the review and the steps associated with each phase are shown in Fig.  1 .

3.1 Planning phase (Step 1–4)

This study started by selecting a topic, at which point the study objectives were also clearly defined and the boundaries of the domain delineated.

3.1.1 Selection of topic and research questions

Selecting a topic for SLR is of crucial importance because many factors such as individual or community interest, research gaps, and research impact contribute to shaping research questions on the topic. Our understanding of the GDSE process life cycle is continuously evolving (Kitchenham et al., 2010 ), and many areas in this field lack generalized evidence. It is critically important for the game industry to identify a quality-driven GDSE process. Several studies have investigated different phases of the GDSE process life cycle, but they do not offer systematic, comprehensive, and thorough methodological research specific to this topic.

In this review, studies from 2000 to 2015 will be explored to answer the following research questions:

Research Question (RQ1): What is the intensity of research activity on the GDSE process life cycle?

RQ2: What topics are being researched in the pre-production, production, and post-production phases?

RQ3: What research approaches are being used by researchers in the software game domain?

RQ4: What empirical research methods are being used in the software game domain?

The number of publications has been identified by the research group to address RQ1. A graphical representation has been used to represent the increase or decrease in the number of publications per year as a measure of research activity. To address RQ2, RQ3, and RQ4, each study selected has been affiliated to a research topic, to a certain approach, and to a specific methodology used for the research. Details of this classification into corresponding categories are discussed in section 3.2.4 .

3.1.2 Review team & protocol establishment

A multidisciplinary team is needed to perform a high-quality scientific SLR. To enhance the thoroughness and minimize the potential bias of a study, an SLR is normally undertaken by more than one reviewer. The SLR team for this review was made up of three people. Two people were designated as principal reviewers (Second expert report by American institute 2011). One person was also selected as the project leader to handle additional administrative tasks such as team communication, points of contact, meeting arrangements and documentation, task assignment and follow-up, and quality assurance. Table  1 details the tasks required for the SLR process and reviewer’s involvement and total time duration.

In order to ensure the review could be replicated and to reduce researcher bias a review protocol and it’s evaluation procedure was developed at step 3 and 4. The final review protocol is discussed in the following sections 3.2.1 to 3.2.4 (Steps 5–9 incl.).

3.2 Conducting phase (Step 5–9)

3.2.1 search strategy.

In the SLR, the search procedure is based on an online search. The search strategy for an SLR is a plan to construct search terms by identifying populations, interventions, and outcomes. Key terms are combined together to created different groups in order to form search strings. Each group comprise of terms that are either different forms of the same word, synonyms, or terms that have similar or related semantic meaning within the domain. Table  2 depicts the followed approach.

In order to retrieve different sets of relevant literature, four groups are designed. The main objective of this grouping is to find the literature that is the intersection of the groups as shown in Fig.  2 .

Selection of relevant studies

The search strategy was implemented by applying the “AND” and “OR”, where the “OR” operator is used within the Group and the “AND” is used between the groups. According to Table  2 , the following search string will capture the structure:

( Group 1: [Software game] OR [Digital game] OR [Video game] OR [Computer game] OR [Online Game] OR [Serious games] OR [Educational Games] OR [Learning Games])

( Group 2: [Development] OR [Advancement] OR [Steps] OR [Evolve] OR [Project])

( Group 3: [Life cycle] OR [Design] OR [Implementation] OR [Requirements Engineering] OR [Testing] OR [Evaluation] OR [Maintenance])

( Group 4: [Process] OR [Progression] OR [Method] OR [Model]).

Therefore, “ Software game development lifecycle process ”, “ Computer game development design process ” and “ video game testing process” are some examples of the search strings and similar way different search strings were formed in order to capture all relevant studies.”

To ensure that all relevant research concerning this area of study was reviewed, journals and conferences from 2000 to 2015 were covered, using as sources IEEE Explorer, ACM Digital Library, Science Direct Elsevier, Taylor & Francis, Google Scholar, and Wiley Publications. If the information required, as indicated on the form shown in Table  3 , was not explicitly present in the potential study, then that paper was peer-reviewed by all team members and, after discussion, validated for correctness. Otherwise, each paper was reviewed by one reviewer. Each study involved some general information and some specific information, as indicated on the form.

3.2.2 Pilot selection & data extraction

The research study selection and data extraction was based on the following coverage criteria:

Inclusion criteria for study

For SLR, articles and research papers from 2000 to 2015 were included, and to evaluate their suitability, the following criteria were analyzed:

The study should be thoroughly reviewed by at least one of the reviewers.

Only the following types of studies were considered: case studies, theoretical papers, and empirical analysis surveys.

The full text of the article should be available.

If any article identifies any challenges and problems in software games, that article is included as a review.

Studies that describe motivation for game application.

Study exclusion criteria

The following criteria were used to determine articles to be excluded:

Articles published on company Web sites.

Articles not relevant to the research questions.

Articles not describing any phase of the game development life cycle.

Study selection

This procedure involved two phases. In the first phase, an initial selection was made on the basis of the inclusion criteria and after reading the title, abstract, and conclusion of each article. In the second phase, if a particular article met the criteria, then the whole article was studied. One hundred forty-eight papers were identified after final selection, as shown in Fig.  3 . Table  4 shows the results found in each data source and Additional file 1 : Appendix A contains a full list of selected publications.

Study selection process

3.2.3 Quality criteria

In this research, quality guidelines were defined based on a quality instrument that was used to assign a quality score to each article as a basis for data analysis and synthesis. The quality instrument consisted of four sections: a main section containing a generic checklist applicable to all studies, and three other sections specific to the type of study.

The checklist was based upon SLR guidelines (Kitchenham, 2004 ) and was derived from Kitchenham ( 2004 ) and Second expert report by American institute (2011). The detailed checklist is shown in Table  5 . Some of the checklist items could be answered by “yes” or “no” and they also included a “partial” option. A value of 1 was assigned to “yes,” 0 to “no,” and 0.5 to “partial”; then the sum of the checklist values was used to assign a quality score to the study to assess document quality.

3.2.4 Data synthesis

For data synthesis the topics, research approaches and methods are classified and their classification details are listed below:

Classification of topics in the GDSE Life Cycle

This section includes a classification of the topics covered by each study with respect to the pre-production, production, and post-production phase issues involved. The 2012 ACM classification system was used for classification, which is the same method used by Cai and Card ( 2008 ). The proposed classification system has been adopted by many journals and conferences specifically for software engineering topics. The same classification was used here to classify the papers under study, and these were further fabricated based on studies found in the GDLC domain. Table  6 presents the selected classification schema.

Research approaches and methods classification

Research articles can be characterized based on their method and approach, as described by Glass et al. ( 2002 ). The main categories for scientific approach are descriptive (a system, tool, or method; a literature review can also be considered as descriptive studies), exploratory (performed where a problem was not clearly defined), and empirical (findings based on observation of its subjects). To evaluate new methods or techniques, three major empirical research methods are used: surveys, case studies, and experiments (Wohlin et al., 2000 ). Table  7 describes the three major empirical research types; Dyba and Dingsoyr ( 2008 ) also used the same type of empirical classification.

The data collected were statistically analyzed as follows:

To address RQ1, the number of studies published per year, whether journal articles or conference publications, and the number of publications on the GDLC hosted by each digital library.

To address RQ2, the major topics of the GDLC that were investigated in the software game domain.

To address RQ3 and RQ4, the research approach or method used by number of studies.

From Section  3.2.4 , data were tabulated and are presented in Additional file 2 : Appendix B.

3.3 Documenting (Step 10–12)

This step of the SLR describe conclusion, possible threats and limitations to the validity of this study. Authors believe that there is a chance that the word game was not part of the title of some studies, but that nevertheless they discussed game development. These studies may, therefore, have been excluded from the primary dataset by the search procedure. There are other threats that are also linked to a systematic literature review such as generalization and subjective evaluation (Shadish et al., 2002 ).

There are limitations to our results, although significant amounts of effort and time was spent to select the papers that were studied. More specifically, our search was limited to the academic databases. It is obvious from the results of RQ1 that developers prefer to submit their work on the blogs or forums. However, posts for different game forums and blogs cannot be included in a systematic literature review because they don’t fulfil the quality criteria used for the selection of papers. In addition, the exclusion of less-known journals and conferences from the Web of Science and the Scopus index might have led to a different dataset.

Another limitation of the study is the exclusion of Human-Computer Interaction (HCI) filed studies. In the phase of screening out, we found studies from HCI field such as (Plass-Oude Boss et al. ( 2010 )) for games but they didn’t focus on software engineering perspective. In short, we didn’t consider studies from HCI because they take non-functional requirements, and usability features into account. These methods help developers to evaluate software and they considered as an integral part of game development. However, due to the limited scope of the study, we excluded studies from HCI field.

Finally, the classification scheme might have altered the results if they were classified by a scheme, such as the waterfall model, instead of the ACM classification scheme. Despite these limitations, the results of our systematic literature review will be useful to game development organizations and developers of software games.

4 Results and Discussion

This section presents the results of statistical analysis of the data set discusses the findings concerning the RQs formulated in Section 3.1 . The characteristics of the data set are tabulated for better understanding. To trace the categories of each mapped study, the interested reader is referred to the Additional file 2 : Appendix B. A total 148 studies were collated and analyzed as part of this review. To identify GDSE process life cycle domain specific characteristics, the findings of this review will be compared to results from similar studies done by Cai and Card ( 2008 ), Glass et al. ( 2002 ), and Dyba and Dingsoyr ( 2008 ).

4.1 RQ1 What is the intensity of research activity on the GDSE process life cycle?

Table  8 clearly shows that GDSE process life cycle research intensity has increased during the last few years. Figure  4 showed an increase in GDSE process life cycle over time. The y -axis represents the number of publications in the form of a fraction and is calculated by taking year (i) ’s number of publications as the numerator and year (0) ’s number of publications as the denominator. From Table  8 , 2007 was taken as year (0) , and the first data point of the graph was calculated for year (1) i.e., 2008. Figure  4 shows the results up to 2015. Years are given on the x- axis.

Increase in GDSE process life cycle research activity

Figure  4 illustrates that during the last few years, research activity in the GDSE process life cycle domain has continuously increased and the number of publications in the GDSE domain has increased at a polynomial growth rate since 2005. During 2013, 2014 and 2015 the drop in research activity is noted. It seems obvious that most of the work related to GDSE research activity was not published on the selected sources for this study. During 2014, most of the research activities were seen on the game development associations/groups web sites, like DIGRA association and Gamastura, or game developers personal blogs.

Moreover, Fig.  5 shows the list of countries most active in GDSE process life cycle topics research. Looking at research activity based on countries, China now dominates GDSE process life cycle research, but its research into the game domain started only in 2010. In four years, China has come to dominate this area of research. Before 2010, the United States and the United Kingdom were dominant.

Research activity per country

Authors from North and South America have played a dominant role since 2004 and are still contributing in this area. Contributors in Europe also started research into the GDSE domain in 2007, but the Asian continent has dominated the GDSE domain since 2010. It can be visualized in Fig.  6 . The most popular venue for GDSE research publication is IEEE; it seems that IEEE accounts for the main bulk of publications (approximately 63 %), followed by Elsevier, Springer, and ACM.

Research activity by continent

4.2 RQ 2: What topics are being researched in the pre-production. Production and post production phase?

This section addresses the identification of main research topics in the GDSE process life cycle domain. Table  9 clearly suggests that most research has been conducted in the production phase, followed by the pre-production phase. On the other hand, the post-production phase has not attracted much research interest. These GDSE process life cycle topics are somewhat different than in software engineering because of two factors: first, the GDSE domain has special needs and priorities, and second, it is a young domain which requires more fundamental research in the area of requirements, development, and coding tools. When the GDSE domain becomes mature, then other areas in the field, like testing and verification, will attract the interest of researchers.

As mentioned earlier in Section 2 , games have specific characteristics, which the conventional software development process cannot completely address. In the past years, research on GDSE process life cycle topics has become more active because, unlike other software products, games provide entertainment and user enjoyment, and developers need to give more importance to these aspects. As a result, research about the pre-production phase has increased. The implementation phase is shorter than in the traditional software implementation process because of the short time to market. This production-phase research intensity has attracted the interest of many researchers, and maximum research activity has been reported because the GDSE domain requires efficient development and coding techniques. McShaffry ( 2003 ) also highlighted the importance of the production phase to counteract poor internal quality. There is much less research activity in the post-production phase than in the pre-production and production phases.

Figure  7 presents the growth of each GDSE process life cycle research topic since 2000. It is apparent that in the pre-production phase, the most researched topic is management of the game development process, followed in this order by production-phase development platforms, programming, and implementation topics. In the post-production phase, the marketing area attracted the largest amount of research interest. The state of the art research is the description of actual primary studies, and, therefore, they are mapped according to the research topics they addressed (Budgen et al., 2008 ). Next, a short description of each GDSE topic is presented along with a full reference list. A full reference list of all the studies included is presented in Additional file 1 : Appendix A.

GDSE process life cycle research topics

4.2.1 Pre-production phase

In the pre-production phase, most of the studies categorized under this topic address management issues during the GDSE process life cycle. The overall management of the game development process combines both an engineering process and creation of artistic assets. Ramadan and Widyani [S1] compared various game development strategies from a management perspective, and most studies like [S3], [S6], [S7], and [S8] have proposed frameworks for game development. Game development guidelines can be followed to manage GDSE process life cycle. The presence of agile practices in the game development processes is also highlighted by some studies. Tschang [S4] and Petrillo et al. [S17] highlighted the issues in the game development process and their differences from traditional software development practices. Management of development-team members and their interaction is critically important in this aspect.

Some studies [S10] and [S11] have provided data analytics and empirical analysis of the game development process and issues of interdisciplinary team involvement. Best management practices in the game development process must consider certain elements such as staying on budget, timing, and producing the desired output. To assess game quality, five usability and quality criteria (functional, internally complete, balanced, fun, and accessible) can be used, but a process maturity model specific to the game development process is still needed to measure these processes for better management and high performance.

Requirements specification

One of the main differences between the traditional software development process and GDSE process life cycle is the requirements phase. The game development process requires consideration of many factors such as emotion, game play, aesthetics, and immersive factors. In four studies, the authors have discussed the requirements engineering perspective to highlight its importance for the whole game-software development process. They discussed emotional factors, language ontology, elicitation, feedback, and emergence [S19], [S20], [S21], and [S22]. In particular, game developers must understand these basic non-functional requirements along with the game play requirements and incorporate them while developing games. The main challenges in requirements identification are a) communication between diverse background stakeholders, b) non-functional requirements incorporation with game play requirements, such as media and technology integration, and c) validation of non-functional requirement such as fun, which is very complex because it is totally dependent on the target audience. Callele et al. [S20] further fabricated a set of requirements based on emotional criteria, game-playing criteria (cognitive factors and mechanics), and sensory requirements (visual, auditory, and haptic). The requirements specification phase must address both the functional and non-functional requirements of game development.

Game system description language

Many description languages are currently used by developers, such as the UML model, agent-based methodologies, and soft-system methodologies. Quanyin et al. [S32] proposed the UML model for mobile games. They performed experiments and reported that it would be a good model for further development of games on the Android operating system. Shaker et al. [S33] extracted features of the Super Mario Brothers game from different levels, frequency sequences of level elements, and statistical design levels. Then, they analyzed the relationship between a player’s experience and the level design parameters of platform games using feature analysis modelling. Tylor et al. [S28] proposed a soft system methodology for initial identification of game concepts in the development process. The proposed approach can be used instead of a popular description language because it provides an overview of the game. Chan and Yuen [S30] and Rodriguez et al. [S31] proposed an ontology knowledge framework for digital game development and serious games modelling using the AOSE methodology. A system description language for games must be both intelligible to human beings and formal enough to support comparison and analysis of players and system behaviors. In addition, it must be production-independent, adequately describe the overall game process, and provide clear guidelines for developers.

Reusability

The existence of reusability of software (Capretz and Lee 1992 ) and development platforms in game development has been reported by some researchers, but to gain its full advantages, commonality and variability analysis must be done in the pre-production phase. This category addresses reuse techniques for game development software (Ahmed and Capretz, 2011 ). Neto et al. [S34] performed a survey that analyzed game development software reuse techniques and their similarity to software product lines. Reuse techniques in game development could reduce cost and time and improve quality and productivity. For reuse techniques, commonality and variability analysis is very important, similar to a software product line. Szegletes and Forstner [S36] proposed a reusable framework for adaptive game development. The architecture of the proposed framework consisted of loosely coupled components for better flexibility. They tested their framework by developing educational games. The requirements of the new game must be well aligned with the reusable components of the previously developed game.

Game design document

The Game Design Document (GDD) is an important deliverable in the pre-production phase. It consists of a coherent description of the basic components, their interrelationships, directions, and a shared vocabulary for efficient development. Westera et al. [S37] addressed the issue of design complexity in serious games by proposing a design framework. Furthermore, Salazar et al. [S38] highlighted the importance of a game design document for game development and provided an analysis of many available game design documents from the literature. They also compared their findings with traditional software requirement specifications and concluded that a poor game design document can lead to poor-quality product, rework, and financial losses in the production and post-production phases. Hsu et al. [S40] pointed out the issues of level determination in games and trade-off decisions about them. They proposed an approach to solve the trade-off decision problem, which is based on a neural network technique and uses a genetic algorithm to perform design optimization. Khanal et al. [S41] presented design research for serious games for mobile platforms, and Cheng et al. [S42] provided design research for integrating GIS spatial query information into serious games. Finally, Ibrahim and Jaafar [S43] and Tang and Hanneghan [S44] worked on a game content model for game design documents. Currently, GDD suffers from formalism and incomplete representation; to address this issue, the formal development of GDD is very important. A comprehensive GDD (focused on the game’s basic design and premises) results in good game quality.

Game prototyping

Game prototyping in the pre-production phase helps the developer to clarify the fundamental mechanics of the final game. Game prototyping in the preproduction phases is considered important because it is used to convey game and play mechanics and also helps in evaluating a game player’s experience. Reyno and Cubel [S49] proposed automatic prototyping for game development based on a model-driven approach. An automatic transformation generates the software prototype code in C++. De Silva et al. [S48] proposed community-driven game prototyping. The developer can approach the well-established community and focus on the technical stuff rather than starting from scratch. They used this approach for massive, multi-player online game development. Guo et al. [S50], Kanev and Sugiyam [S51], and Piesoto et al. [S52] proposed analysis of rapid prototyping for Pranndo’s history-dependent games, 3D interactive computer games, and game development frameworks respectively. Prototypes also help to identify missing functionality, after which developers can easily incorporate quick design changes. Model-driven or rapid-prototyping approaches can be used to develop game prototypes.

Design tools

Game design tools are used to help game developers create descriptions of effects and game events in detail without high-level programming skills. Cho and Lee [S56] and Segundo et al. [S57] proposed an event design tool for rapid game development and claimed that it does not require any kind of programming skill. These tools also enable reuse of existing components and reduce the total time of the game-creation process.

Risk management

In the game development domain, risk management factors do not receive much discussion by researchers. Risk management is very important from a project management point of view. Identifying risk factors in the game development process is also important. In game development, the project manager is the game producer and must bring together management, technical, and aesthetic aspects to create a successful game. The study by Schmalz et al. [S58] is the only study highlighting the issue of risk management in video development projects. They identified two risk factors during the development process: failure of development strategy and absence of the fun factor. In game development, important risk factors can be the development strategy, the fun factor or extent of originality, scheduling, budgeting, and others, but very low priority has been given by game developers to formal analysis of risk factors.

4.2.2 Production phase

Asset creation.

Asset creation in the production phase is the foundation stage where game developers create the various assets and then use them in the game implementation phase. In the production phase, the first step is to create assets for the game. One of these assets is audio creation. Migneco et al. [S63] developed an audio-processing library for game development in Flash. It includes common audio-processing routines and sound-interaction Web games. Minovic et al. [S65] proposed an approach based on the model drive method for user interface development, and Pour et al. [S64] presented a brain computer interface technology that can control a game on a mobile device using EEG Mu rhythms. For audio processing, open-source libraries are available, especially for games. Audio and interface design are examples of game assets.

Storyboard production

Storyboard production is the most important phase of game production; it involves development of game scenarios for level solutions and incorporation of artificial intelligence planning techniques for representing the various features of games through a traditional white board or flow chart. Pizzi et al. [S59] proposed a rational approach that elaborated game-level scenario solutions using knowledge representation and also incorporated AI techniques to explore alternative solutions by direct interaction with generated storyboards. Finally, Anderson [S61] presented a classification of scripting systems for serious and entertainment games, and Cai and Chen [S62] explored scene editor software for game scenes. Their approach was based on the OGRE.Net framework and C++ technology. Various scripting editors based on different technologies are available for game developers to produce storyboards. Some of this software helps to develop and edit scenes at different game levels, and other software helps by generating game levels automatically based on a description.

Development platforms

The studies classified under this category proposed various types of platforms for game development. Development platforms provide a ready-made architecture for server–client connectivity and help developers create games quickly. Open-source development platforms are available, but developers must customize them according to the required functionality. Peres et al. [S69] used a scrum methodology for game development, especially for multiple platforms, and implemented interfaces with social networking Web sites such as Twitter and Facebook. Jieyi et al. [S70] proposed a platform for quick development of mobile 3D games. First, the platform implemented the game template in two environments such as the Nokia series 60 platform and the Symbian OS. The second part of the process involved analysis of the entire game structure and extraction of game parameters for later customization. Finally, the tool could be used for game customization. Lin et al. [S] developed intelligent multimedia mobile games from embedded platforms. The proposed communication protocol was able to control the embedded platform to achieve the game usability and amusement. Mao et al. [S78] presented a logical animation platform for game design and development, and Alers and Barakova [S81] developed a multi-agent platform for an educational children’s game. Suomela et al. [S77] highlighted the important aspects of multi-user application platforms used for rapid game development. Some researchers have proposed a development platform similar to that described above that provides connectivity along with client customization and unnecessary updating of game servers.

Formal language description

Game semantics can be classified under formal language description for programming languages; only two studies were reported under this classification. The formal language description of game semantics provided a way to gain insight into the design of programming languages for game development. Mellies [S99] proposed a denotational prepositional linear logic for asynchronous games, and Calderon and McCusker [S100] presented their analysis of game semantics using coherence spaces. Very little work has been reported in this area, and very few game semantic descriptions of languages have been published.

Programming

Code complexity is increasing, especially in game development, because of the incorporation of complex modules, AI techniques, and a variety of behaviors. The most common programming languages used in game development are object-oriented structured languages such as Java, C, and C++. Studies classified under this category explored the programming aspect of game development. El Rhalibi et al. [S82] proposed a development environment based on Java Web Start and JXTA P2P technologies called Homura and NetHomura. It extends the JME game engine by facilitating content libraries, providing a new interface, and also providing a software suite that supports advanced graphical functionalities within IDE. The other two studies, done by Meng et al. [S84] and Chen and Xu [S85], also explored programming languages such as C++, DirectX, and Web GL and also Web Socket technologies for game development. Three studies by Yang et al. [S87], Yang and Zhang [S88], and Wang and Lu [S89] explored collision detection algorithms from a game logic aspect for software games, proposed A* search, and AI optimization-based algorithms.

Wang et al. [S83] proposed a framework for developing games based on J2ME technology. Zhang et al. [S92] also explored the effects of object-oriented technology on performance, executable file size, and optimization techniques for mobile games and suggested that object-oriented technology should be used with great care because the structured programming in game development is highly competitive. Bartish and Thevathayan [S86] and Fahy and Krewer [S90] analyzed the use of agents, finite state machines, and open-source libraries for the overwhelmingly complex process of multi-platform game development. Optimization techniques can be used with object-oriented programming to avoid unnecessarily redundant classes and inheritance, and to handle performance bottlenecks. These languages can be used across different development environments such as Android, iOS, Windows, and Linux. Researchers have proposed various approaches and tools for efficient game development. The integration of various development artefacts into games can also be done by generative programming, which also helps to achieve efficient development.

Game engine

A game engine is a kind of special software framework that is used in the production phase for creating and developing games. Game engines consist mainly of a combination of core functionalities such as sound, a physics engine or collision detection, AI, scripting, animation, networking, memory management, and scene graphs. Hudlicka [S108] identified a set of requirements for a game engine, including identification of the player’s emotions and the social interactions among game characters. This is the only study that has highlighted the important functionalities that an affective game engine must support. Another study by Wu et al. [S109] focused on game script engine development based on J2ME. It divided script engines into two types. The first type is the high-level script engine that includes packaging and refining of the script engine. The second type, the low-level script engine includes feature packages associated only with API. Four studies [S102], [S105], [S106], and [S107] explored the development of game engines on mobile platforms. Finally, Anderson et al. [S109] proposed a game engine selection tool. Recently, developers have been using previously developed or open-source game engines to economize on the game development process. Various researchers have proposed script-based, design pattern-based, and customizable game engines. In the GDSE process life cycle, game engines automate the game creation process and help a developer to develop a game in a shorter time.

Implementation

The foundations of game theory are used in game development because it is a branch of decision theory that describes interdependent decisions. Most studies in this category described different aspects of game implementation technologies on various types of platforms. They considered improving programming skills, 2D/3D animations and graphics, sound engineering, project management, logic design, story-writing interface design, and AI techniques. Various kinds of game implementation technologies can be found in the literature. Vanhatupa [S117] presented a survey of implementation technologies especially for browser games. The technologies explored in these studies are mainly server applications (application runtime, server-side scripting, and user interface and communication), client applications, databases, and architecture. The same study also described the accessories that can be used for implementation: application platforms, game engines, and various types of plug-ins. Abd El-Sattar [S112] proposed an interactive computer-based game framework for the implementation process. The framework includes steps from design through implementation that are based on game theory foundations and focus mainly on game models, Nash equilibrium, and strategies of play. The proposed framework includes architectural design and specifications, a proposed game overview, a game start-up interface and difficulty scaling, game modelling, the game environment and player control, and a free-style combat system.

Four studies [S113], [S114], [S119] and [S120] focused mainly on a development framework for mobile devices. Su et al. [S96] proposed a framework describing implementation of various main modules such as pressure movement, a thread pool based on the I/O completion port, and a message module. They also claimed that their proposed framework addressed the problems of traditional frameworks such as the single-server exhaustion problem, synchronization, and thread-pooling issues. Jhingut et al. [S114] discussed 3D mobile game implementation technologies from both single-player and multi-player perspectives. They also evaluated two game APIs: MDP 2.0 and M3G API. Finally, Kao et al. [S120] proposed a client framework for mobile devices that used a message-based communication protocol and reserved platform-specific data as much as possible. A few researchers have proposed agent-based frameworks as explored above for effective communication and synchronization between system components.

4.2.3 Post-production phase

Quality assurance.

Process validation plays an important role in assessing game quality. Collection and evaluation of process data from the pre-production phase through to the post-production phase either provide evidence that the overall development process produces a good-quality game as a final product or reveal that it cannot. Only two studies were reported under this classification. Stacey et al. [S122] used a story-telling strategy to assess the game development process. They carried out a two-year case study on a four-person development team. Astrachan et al. [S126] tried to validate the game creation process by analyzing the development process and design decisions made during development. The scope of studies done under this category was limited. The case studies were done for small teams and were limited to only one phase. In the game development process, quality assurance and process validation are critical components, and standard methodologies are lacking. More exploration is needed to provide deeper insights. QA for games needs more research attention because very little work has been reported.

Beta testing

Beta testing in games is used to evaluate overall game functionality using external testers. Beta testing is a kind of first public release for testing purposes by users. Game publishers often find it effective because bugs are identified by users that were missed by developers. If any desired functionality is missing, it must be addressed at this stage. This testing is performed before final game release. Under this classification, only four studies [S127], [S128], [S129], and [S130] were reported. Hable and Platzer [S129] evaluated their proposed development framework for mobile game platforms. Omar et al. [S128] evaluated educational computer games and identified two evaluation techniques: Playability Heuristic for Educational Games (PHEG) for expert evaluators, and Playability Assessment of Educational Games (PAEG) for real-world users. The proposed AHP-based Holistic Online Evaluation System for Educational Computer Games (AHP_HeGES) online evaluation tool can be used in the evaluation process. Very little work was reported in this category.

Heuristic-based testing

Heuristics are a kind of design guideline and can be used as an evaluation tool by game design developers or users. Basically, heuristics can be used in software engineering to test the interface. In games, evaluation must extend beyond the interface because other playability experiences also need evaluation such as the game story, play, and mechanics. Six studies [S132], [S133], [S134], [S146], [S147], and [S148] fell under this classification. Al-Azawi et al. [S132] proposed a heuristic testing-based framework for game development. The proposed framework divides testing by two types of user: experts and real-world users. Experts evaluate playability, game usability, and game quality factors. Users evaluate the game as a positive or negative experience. Omar and Jaafar [S133] and Al-Azawi et al. [S134] proposed a framework for the evaluation phase in the game development process. Heuristic testing can be done during the development process and repeated from the early design phase. It is perfect for game testing because after the game is implemented, if anything goes wrong, it will be too expensive to fix and will affect the project schedule. This topic also needs attention by researchers.

Empirical testing

Empirical testing approaches for the game-testing phase have been explored by only a few researchers. The approaches described by these researchers have focused only on final-product quality and usability. Only two studies were reported under this classification [S135] and [S136]. Escudeiro and Escudeiro [S135] used a Quantitative Evaluation Framework (QEF) to evaluate serious mobile games and reported that QEF frameworks are very important in validating educational games and final-product quality. Choi [S136] analyzed the effectiveness of usability-expert evaluation and testing for game development. Experimental results showed the importance of the validation process in game development. The scope of the studies done under this category was very limited, and other aspects of final-product testing have not been explored by researchers.

Testing tools

Development of testing tools has not been addressed by many researchers. Only one study [S137] was reported under this classification. Cho et al. [S137] proposed testing tools for black-box and scenario-based testing. They used their tool on several online games to verify its effectiveness. Tools for game testing facilitate the testing process. The proposed scope of study was also limited, and available testing tools have focused only on evaluation of online games.

After a game has been developed, the final step is marketing. Marketing of games includes a marketing strategy and a marketing plan. The marketing strategy is directly related to the choice of users and the types of games that are in demand. The marketing plan is something that a publisher can give to a distributor to execute on the publisher’s behalf. Some studies have been done from the perspective of game-user satisfaction that provide the baseline for the factors that game developers must take into account for new game development. Yee et al. [S142] described a game motivation scale based on a three-factor model that can be used to assess game trends. Three studies [S139], [S143], and [S144] empirically investigated the perspective of game-user satisfaction and loyalty. No study in the literature has directly captured a marketing strategy and a marketing plan for games.

4.3 RQ 3: What research approaches are being used by researchers in digital game domain?

Table  10 shows that most GDSE process life cycle studies have used an exploratory research approach. Figure  8 shows a comparison between the three research approaches used in the GDSE process life cycle domain. Figure  9 shows a comparison among the empirical research methods used in the GDSE process life cycle domain. The results suggest that surveys are most frequently used in GDSE domain research.

GDSE process life cycle research approaches

Empirical research approaches

These results were to be expected because the GDSE domain has only been growing since 2005; before 2010 more studies follow the descriptive approach because the field was young. After 2010, more studies have followed the exploratory approach because the domain has been maturing. More specifically, exploratory and descriptive approaches seem now to be equally used in the GDSE process life cycle domain.

4.4 RQ4: What empirical research methods are being used in the software games domain?

Table  11 depicts the results of the RQ4. The experimental empirical method is less used in the GDSE process life cycle domain, as mentioned by Wohlin et al. ( 2000 ), because carrying out formal experiments requires significant experience. The case-study method has also been used infrequently by researchers. The reason for this could be that case studies require project data obtained through various types of observations or measurements, and no research database or repository is available for the GDSE process life cycle domain. Finally, the survey method was more common than the other two methods. This is reasonable because the GDSE domain is still immature and researchers are trying to produce knowledge by questioning game users, experts, and others.

5 Conclusions

The GDSE process proved to be incredibly challenging as game technology including game platforms and engines changes rapidly and coding modules are used very rarely in the another game project. However, recent success of digital game industry enforces further stress along with game development challenges and highlights the need of good practices adoption for game development process. In order to find out the specific area in game development software engineering process for improvement, assessment of process activities needs to be performed. However, due to relatively young history and empirical nature of the field, there has not been any development strategies or set of best practices to carry out game development fully explored. This systematic literature review helps to identify the research gaps in game development life cycle.

The main objective of this research was to provide an insight into the GDSE process life cycle domain because, in the past, researchers have pointed out that it is different from the traditional software development process. To achieve this objective, a systematic literature review was performed, which confirmed the first step of the evidence-based paradigm. The results also confirmed that the GDSE process life cycle domain is different from the traditional software engineering development process and that research activity is growing day by day, attracting the interest of more researchers. This observation provided an evidence for developers they need to look for other important activities on top of software development process. This paper describes the various topics in the GDSE domain and highlights the main research activities related to the GDSE process life cycle. The research topics identified in the GDSE were a combination of different disciplines and together they complete the game development process.

The most heavily researched topics were from the production phase, followed by the pre-production phase. On the other hand, in the post-production phase, less research activity was reported. In the pre-production phase, the management topic accounted for the most publications, whereas in the production phase, the development platform, programming, and the implementation phase attracted the most researchers. The production phase has attracted more research because game developers focus more on implementation and programming because of the limited game-development time period. The post-production phase includes process validation, testing, and marketing topics. Very little research activity was observed in this area because the quality aspect of game development is not yet a mature field. These results highlighted that researcher’s need to pay attention especially in the phase of post-production.

In addition to research topics, more researchers used exploratory research methods; as for empirical research methods, surveys were carried out by more researchers than case studies and experiments. Overall, the findings of this study are important for the development of good-quality digital games. Rapid and continual changes in technology and intense competition not only affect the business, but also have a great impact on development activities. To deal with this strong competition and high pressure, game development organizations and game developers must continually assess their activities and adopt an appropriate evaluation methodology. The result of the study highlighted that use of a proper assessment methodology will help the organization identify its strengths and weaknesses and provide guidance for improvement. However, the fragmented nature of the GDSE process requires a comprehensive evaluation strategy, which has not yet been entirely explored. Finally, this kind of research work provides a baseline for other studies in the GDSE process life cycle domain and highlights research topics that need more attention in this area. The findings of this study will help researchers to identify research gaps in GDSE process life cycle and highlights areas for further research contributions. This study also is a part of a larger project aiming to propose a digital game maturity assessment model (Aleem et al. 2016a ). The identified important dimensions are developer’s perspective (Aleem et al. 2016b ), the consumer, the business (Aleem et al. 2016c ), and the process itself. It also reinforces the assertion that the GDSE process life cycle domain is a complex scientific domain comparable to the software engineering development process, and it needs more attention and consideration of different factors in game development software engineering process.

In short, this study presents a systematic literature review of the GDLC topics. Overall, the findings of this study are important for the development of good-quality digital games because they highlight the areas that needs research attention. The results of this study have shown that the fragmented nature of the GDLC process requires a comprehensive evaluation strategy, which has not yet been entirely explored. Finally, this kind of research work provides a baseline for other studies in the GDLC domain and highlights research topics that need more attention in this area. The findings of this study will also help researchers to identify research gaps in the GDLC and highlight areas for further research contributions.

Abbreviations

Game Design Document

Game Development Software Engineering (GDSE)

Quantitative Evaluation Framework

Systematic Literature Review

Ahmed, F., Capretz, L. F., 2011. A business maturity model of software product line engineering. Information Systems Frontiers, Springer, 13, 4, 543–560, DOI: 10.1007/s10796-010-9230-8

Aleem S, Fernando Capretz L, Ahmed F (2016). A Digital Game Maturity Model (DGMM), Entertainment Computing 17, 55-73. http://dx.doi.org/10.1016/j.entcom.2016.08.004

Aleem S, Capretz LF, Ahmed F (2016a) Critical Success Factors to Improve the Game Development Process from a Developer’s Perspective. J Comput Sci Technol 31(5):925–950

Article   Google Scholar  

Aleem S, Capretz LF, Ahmed F, (2016c). Empirical investigation of key business factors for digital game performance, Entertainment Computing, Vol. 13,pp. -25-36, http://dx.doi.org/ 10.1016/j.entcom.2015.09.001

Alvarez, J. Michaud, L., (2008). Serious Games: Advergaming, Edugaming, Training, and More, IDATE

Ampatzoglou A, Stamelos I (2010) Software engineering research for computer games: a systematic review. J Inf Softw Technol Elsevier 52(9):888–901.

Bethke E (2003). Game Development and Production. Wordware game developer's library. Wordware Pub, Plano. ISBN 978-0-585-44833-6

Blitz game studio, (2014). Project Lifecycle. Retrieved May 1, 2014 from http://www.blitzgamesstudios.com/blitz_academy/game_dev .

Boyle EA, Connolly TM, Hainey T, Boyle JM (2012) Engagement in digital entertainment games: A systematic review. Comput Hum Behav 28:771–780

Brereton P, Kitchenham B, Budgen D, Turner M, Khalil M (2007) Lessons from applying the systematic literature review process within the software engineering domain. J Syst Softw 80(4):571–583

Budgen D, Turner M, Brereton P, Kitchenham B (2008). Using mapping studies in software engineering. In: Proceedings of Psychology of Programming Interest Group (PPIG). Lancaster University, Lancaster. pp. 195–204

Cai KY, Card D (2008) An analysis of topics in software engineering. J Syst Softw 81(6):1051–1058

Capretz LF, Lee PA (1992) Reusability and life cycle issues within an Object-Oriented Design methodology (refereed). In: Ege R, Singh M, Meyer B (eds) Technology of Object-Oriented Languages and Systems. Prentice Hall, Englewood Cliffs, pp 139–150. ISBN 0-13-042441-2

Google Scholar  

Castillo T, Novak J, (2008). Game Development Essentials: Game Level Design. Delmar Cengage Learning. ISBN: 9781401878641

Chandler HM (2010) Game Production Handbook. Johns and Bartletts, Sudbury

Connolly TM, Boyle EA, MacArthur E, Hainey T, Boyle JM (2012) A systematic literature review of empirical evidence on computer games and serious games. Comput Educ 59:661–686

Dyba T, Dingsoyr T (2008) Empirical studies of agile software development: a systematic review. Information and Software Technology 50(9-10):833–859

Dyba T, Kitchenham BA, Jorgensen M (2005) Evidence-based software engineering for practitioners. Software Magazine. IEEE Computer Society 22(1):58–65

Entertainment Software Association (ESA), (2014). Essential facts about the Computer and Video Game Industry. Entertainment Software Association Available at: http://www.theesa.com/wp-content/uploads/2014/10/ESA_EF_2014.pdf . Accessed on 15 Oct 2015.

Entertainment Software Association (ESA), (2015). Essential facts about the Computer and Video Game Industry. Entertainment Software Association. Available at: http://www.theesa.com/wp-content/uploads/2015/04/ESA-Essential-Facts-2015.pdf . Accessed on 15 Oct 2015.

Flood K (2003) Game Unified Process: GameDev., Available at: http://www.gamedev.net/page/resources/_/technical/generalprogramming/game-unified-process-r1940 . Accessed June 12, 2015

Glass RL, Vessey I, Ramesh V (2002) Research in software engineering: an analysis of the literature. Inf Softw Technol 44(8):491–506

Godoy A, Barbosa E F, (2010). Game-Scrum: An approach to agile game development, Proceedings of SBGames 2010 Computing Track (I. S. F. SC, ed.), Sao Carlos, pp. 292–295, November 8–10, pp. 292–295.

Gredler M. E (1995). Designing and evaluating games and simulations. Behavioral Science. Wiley Online Library, 40, 1 (1995), 76–77

Gredler M. E (2003). Games and simulations and their relationship to learning. Handbook of Research on Educational Communications and Technology, Lawrence Erlbaum, Inc: Mahwah, NJ pp. 571–581.

Hendrick A (2014). Project Management for Game Development. Retrieved 20 May 2014, from http://mmotidbits.com/2009/06/

Kaitilla C (2014). How to learn Ouya Gamdev. Retrieved December 20, 2014, from http://gamedevelopment.tutsplus.com/articles/how-to-learn-ouya-gamedev--gamedev-9197 .

Kanode C M., Haddad H M (2009). Software engineering challenges in game development. In Proceedings of the 2009 Sixth International Conference on Information Technology: New Generations, (April 27–29, 2009), 260–265

Keith C (2010) Agile game development with Scrum. Addison-Wesley, Boston

Kitchenham B (2004). Procedures for performing systematic literature reviews. Joint Technical Report. Computer Science Department, Keele University, July 2004, 33 pages.

Kitchenham B, Charters S, (2007). Guidelines for performing systematic literature reviews in software engineering, Software Engineering Group, Keele University and Department of Computer Science, University of Durham, United Kingdom, Technical Report EBSE-2007-01, 2007

Kitchenham, B., Sjoberg, D.I.K., Brereton, P., Budgen, D., Dyba, T., Host, M., Pfahl, D., Runeson, P., 2010. Can we evaluate the quality of software engineering experiments? In Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement, 1–8

Kruchten P (2000) The Rational Unified Process: An Introduction, 2nd edn. Addison Wesley Longman, Reading

Liming D, Vilorio D (2011). Work for play: Careers in video game development, Occupational Outlook Quarterly. Available at: http://www.bls.gov/careeroutlook/2011/fall/art01.pdf . Accessed on: 30 Sept 2015.

McGrath J (2014). The game development lifecycle: A theory for the extension of the agile project methodology. http://blog.dopplerinteractive.com/2011_04_01_archive.html . Accessed 1 May 2014

McShaffry M (2003) Game coding complete. Paraglyph Press, AZ, USA

Munassar N, Govardhan A (2010) A Comparison Between Five Models Of Software Engineering. International Journal of Computer Science Issues 7(5):94–101

Nayak M (2013). A look at the $66 billion video-games industry, Reuters, Retrieved June 2013 from http://in.reuters.com/article/2013/06/10/gameshow-e-idINDEE9590DW20130610 . Accessed 12 Sept 2014

Newzoo Game Market Research, 2015. Global Report: U.S. and China take half of $113 bn game market in 2018. Available at: http://www.newzoo.com/insights/us-and-china-take-half-of-113bn-games-market-in-2018/ . Accessed 2 Oct 2015

Osbourne-O'Hagan A, Coleman G, O'Connor RV (2014) Software development processes for games: a systematic literature review. In: 21st European Conference on Systems, Software and Services Process Improvement EuroSPI, Luxembourg, 25-27 June 2014

Petrillo F, Pimenta M, Trindade F, Dietrich C (2009) What went wrong? A survey of problems in game development. Computers in Entertainment. ACM Digit Library 7(1(13)):1–22

Plass-Oude Boss, D., Reuderink, B., Van De Laar, B.L.A., Gurkok, H, Muhl, C., Poel, M., Heylen, D.K.J., Nijholt, A. (2010), Human-Computer Interaction for BCI Games: Usability and User Experience. In Proceedings of the International Conference on CYBERWORLDS, A. Sourin (eds), IEEE Computer Society, Los Alamitos, 277–281

Pressman RS (2001) Software engineering: a practitioner approach, 5th edn. Wiley, New York

PWC global media and entertainment outlook 2011–2014, 2011. Available at http://www.pwc.com/gx/en/global-entertainment-mediaoutlook/territory-segments-digital-forecast-overview.jhtml . Accessed on 28 Jul 2013.

Ramadan R., Widyani Y, (2013). Game development life cycle guidelines. In Proceedings of 5th International Conference on Advanced Computer Science and Information Systems (ICACIS). IEEE Computer Society, Jakarta, Indonesia, (September 28–29, 2013) 95–100.

Rieber LP (2005) Multimedia learning in games, simulations and microworlds. Cambridge Handbook of Multimedia Learning. Cambridge University Press, UK, pp 549–567

Book   Google Scholar  

Robin S, (2009). Introduction to game development, 2nd edition. Charles River Media. ISBN-10: 1584506792

Salen K, Zimmerman E (2003). Rules of Play: Game Design Fundamentals. MIT Press, ACM Digital Library. p. 80. ISBN 0-262-24045-9

Schwaber K, Beedle M (2002) Agile Software Development With Scrum. Prentice-Hall, Upper Saddle River

MATH   Google Scholar  

Shadish WR, Cook TD, Campbell DT (2002). Experimental and Quasi-experimental Designs for Generalized Causal Inference. Houghton Mifflin Company, Boston

SUPERDATA 2015 Digital Good Measurement Blog. Worldwide digital games market. Available at: https://www.superdataresearch.com/blog/us-digital-games-market/ . Accessed 30 Dec 2015.

Wohlin C, Runeson P, Host M, Ohlsson MC, Regnell B, Wesslen A (2000) Experimentation in Software Engineering. Kluwer Academic Publishers, Boston/Dordrecht/London

Book   MATH   Google Scholar  

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SA designed the study and performed the review methodology, collected the data, analyzed the data and drafted the manuscript. LC helped to conceive the study and provided guidance to carry out the quality assessments of paper, reviewed the drafted manuscript and fine-tune the final draft. FA helped in study design, provided guidance to present the analysis and helped to draft the manuscript. All authors read and approved the final manuscript.

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Saiqa Aleem

Department of Electrical & Computer Engineering, University of Western Ontario, London, ON, N6A 5B9, Canada

Luiz Fernando Capretz

Department of Computing Science, Thompson Rivers University, Kamloops, BC, V2C 0C8, Canada

Faheem Ahmed

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Aleem, S., Capretz, L.F. & Ahmed, F. Game development software engineering process life cycle: a systematic review. J Softw Eng Res Dev 4 , 6 (2016). https://doi.org/10.1186/s40411-016-0032-7

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research papers on game development

The MIT Game Lab has a long history of innovative research that spans game culture to design practice. Below are some highlights of our work. See specific pages in the pull down menu for more detailed information on some of them.

Games & Colonialism

2017-: mikael jakobsson (co-pi), mary flanagan (co-pi).

What does the history of colonialism-themed board games look like, and what can it tell us about the situation today? What does it mean to present these historical moments in such a lavish form and then let these artifacts serve as centerpieces to gather around for social interaction at board game cafes, meetups, and conventions? This greater project includes Playing Oppression , a forthcoming book to be published by MIT Press; Orderly Adventures, in which we play and analyze games with colonialist themes; and Creating Counter-Colonial Games, a series of workshops to prototype games through cultural engagement with people affected by the colonialist endeavor.

Diversity and Inclusion in Esports and Gaming

2015-: t.l. taylor.

Launched in 2015, AnyKey was co-founded by Dr. T.L. Taylor and Dr. Morgan Romine (with support from Intel and ESL) with the goal of building a more inclusive and accessible esports world for all. Since that inception, AnyKey has become the leading advocacy organization for inclusion and diversity in competitive gaming & live streaming. It now operates as a non-profit and Dr. Taylor has transitioned from being the Director of Research to Chair of the Advisory Board.

Playful Augmented Reality Audio Design Exploration

2018-2019: mikael jakobsson & philip tan.

The focus of this project was to explore the potential of audio augmented reality (AR) technology through design research methodology, particularly exploratory prototyping. Going into this, we understood that location-based audio AR allows the potential for telling stories using the players lived world, through innovative use of the affordances of mobile phone devices, particularly GPS. We also considered audio AR as a means of playing with sound and music. Utilizing the accelerometers of the Bose AR glasses and connected mobile device, body movement can be linked to the players’ own music collection or a music generation engine.

Our work culminated in the discovery of what we are calling locomotion-based gameplay, a modification to the assumptions that occur when considering location- based gameplay. From our explorative work, locomotion-based gameplay arises from the affordances and limitations of current audio AR technology. It considers a person’s movement through space as important, more so than their precise location. Locomotion also implies whole body movement through gestures including the nod of a head and the tap of a toe, not just the vector of movement on a map. These gestures are ephemeral and contain multiple meanings dependent on context and mood. We believe more work in discovering this style of gameplay would be fruitful, for purposes of art and entertainment, for education and tourism, and other currently unforeseen use cases.

Intimate Worlds: Reading for Intimate Affects in Contemporary Video Games

2016-2018: kaelan doyle-myerscough (s.m., comparative media studies, 2018).

When we think of pleasures to be found in video games, we often talk about power, control, agency, and fun. But to center these pleasures is to privilege certain stories, players, actions and possibility spaces. This thesis uses the framework of intimacy to closely examine three games for their capacity to create pleasure in vulnerability, the loss of control, dependence on others, and precarity.

Drawing from Deleuzian affect theory and feminist, queer and posthuman theorists, I read for intimate affects in the formal, aesthetic, proprioceptive and structural elements of Overwatch , The Last Guardian and The Legend of Zelda: Breath of the Wild . Ultimately, I argue two points: that video games have a unique capacity to generate intimate affects, and that my games of choice push us to rethink our assumptions about what constitutes intimacy more broadly.

When All You Have is a Banhammer: The Social and Communicative Work of Volunteer Moderators

2016-2018: claudia lo (s.m., comparative media studies, 2018).

The popular understanding of moderation online is that moderation is inherently reactive, where moderators see and then react to content generated by users, typically by removing it; in order to understand the work already being performed by moderators, we need to expand our understanding of what that work entails. Drawing upon interviews, participant observation, and my own experiences as a volunteer community moderator on Reddit, I propose that a significant portion of work performed by volunteer moderators is social and communicative in nature. Even the chosen case studies of large-scale esports events on Twitch, where the most visible and intense tasks given to volunteer moderators consists of reacting and removing user-generated chat messages, exposes faults in the reactive model of moderation. A better appreciation of the full scope of moderation work will be vital in guiding future research, design, and development efforts in this field.

Recasting Player Two

2016-2017: mikael jakobsson, claudia lo, kaelan doyle myerscough, richard eberhardt & dozens of game designers from near and far.

The game development industry is currently on a mission to include “non-gamers” in local co-op games. Within the development community and among players, these games are said to have a “girlfriend mode.” Developers often cast player one as an expert player in their own image, while player two is a projection of antiquated gender stereotypes who has less agency and control over their play experience. This type of interaction would be better described as mansplaining in motion. This project consists of a series of workshops with participants from the game development community, where we not just discuss and spread awareness of what is problematic with current games and development practices, but work together in creating better alternatives.

OpenRelativity

2012-2016: gerd kortemeyer, philip tan, zach sherin, ryan cheu, & steven schirra.

OpenRelativity is an open-source toolkit to simulate effects of special relativity by varying the speed of light, developed to help people create, test, and share experiments to explore the effects of special relativity. Developed by the MIT Game Lab, it contains open-source code for public use with the free and paid versions of the Unity engine. The toolkit was developed during the creation of the game A Slower Speed of Light.

Gender and Systems of Warm Interaction in Digital Games

2014-2016: kyrie caldwell (s.m., comparative media studies, 2016).

This thesis considers the ways in which digital game mechanics (interactive inputs) contribute to games’ worldbuilding. In particular, this work is concerned with the replication and reinforcement of problematic gender roles through game mechanics that express positive (“warm”) interactions between characters, namely healing, protection, and building relationships. Characters who are women and girls are often associated with physical weakness, nature-based magic, and nurturing (or absent) personalities, whereas characters who are men and boys often protect women through physical combat, heal through medical means, and keep an emotional distance from others. Relationships built through game mechanics rely on one-sided agency and potential that renders lovers and friends as characters who exist to support the player character in achieving the primary goals of the game. Even warm interactions in games carry negative, even potentially violent and oppressive, representations and that there is thusly a need for design interventions on the mechanical level to mitigate violence in game worlds and the reinforcement of negative real world stereotypes.

E-sports Broadcasting

2014-2015: jesse sell (s.m., comparative media studies, 2015).

Situating e-sports broadcasting within the larger sports media industrial complex, discussing e-sportscasters, and investigating the economics behind the growing e-sports industry. E-sports, often referred to as competitive or professional gaming, stands as a prime example of the merger of work and play. A growing body of literature has started focusing on this pastime turned profession. As more professionals enter the scene and audiences continue to grow, e-sports broadcasters look towards older models of broadcasting to inform their own style. This reapplication of former conventions stands in contrast to the trends in the larger sports media trajectory. E-sports broadcasting is largely informed by traditional sports broadcasting, yet remains unable to fully capture the success of the global sports industry. On-air talent, once informed solely by traditional sportscasters are now looking to their fellow e-sportscasters to create something new. Revenue streams which form the foundation of the sports industry are making their way into e-sports but not in the way that one might expect.

MIT Overseer: Improving Observer Experience in Starcraft 2

2013-2015: philip tan & nick mohr.

The MIT Overseer project aims to provide casters with real-time graphics to help them tell the story of a game while it is in progress. We are trying out several different ways of displaying what happened in the past of a single game and anticipating what might happen in the near future.

Subversive Game Design and Meaningful Conflict

2012-2013: konstantin mitgutsch & steven schirra.

Movers & Shakers is used as a research tool to explore how a social component influences experiences in serious games. In addition subversive game design elements are implemented in the game to foster the players’ thinking process and to get them out of unquestioned routines. In the game the players are challenged to give up their prior egoistic goals to reach their common goal – to save the world. In a nutshell, the game shifts from a competitive to a collaborational gameplay – once the players start communicating.

Playstyle Motivation Explorations

2012-2013: todd harper.

Across game genres and communities, there are as many styles of play as there are players, from the highly competitive “powergamer” to the MMO fan who’s content to just take in the scenery and everything in between. Fugue is a game that asks: what are some of the motivations behind these styles? Do players reflect themselves — or a desired projection of the self — through playstyle? Or does the shape and context of the game itself direct such decisions? In order to explore these questions, we created a small, controlled gamespace that gives players an opportunity to express themselves via play.

Procedural Puzzles as a Design Tool for Games

2011-2013: alec thomson, clara fernández-vara.

Puzzledice is a set of tools and programming libraries for procedurally generating puzzles for a wide variety of games. These tools, developed by Alec Thomson at the MIT Game Lab from 2011-2013, are the result of multiple iterations of research and were used to develop Stranded in Singapore during the 2011 summer session of the Singapore-MIT GAMBIT Game Lab. Puzzledice is the result of research into how general purpose procedural puzzles can be used as a tool by game designers. These tools were designed to meet the following three goals: Solvability, Generality, and Usability.

Televisual Sports Videogames

2012-2013: abe stein (s.m., comparative media studies, 2013).

Over the three decade long history of sports videogame development, design conventions have lead to the emergence of a new sports game genre: the televisual sports videogames. These games, which usually simulate major professional or college sports, look and sound like television, and they use televised sports as a reference point for players. This thesis takes a critical look at how these televisual sports videogames are situated in the broader sports media industrial complex of North America, while also considering how the televisual design of these games is meaningful for fans of sports. Specifically, the text looks at how sports videogames reflect or reinforce dominant ideologies of hegemonic sports culture. Building on critical theories in sports studies, and through critical close readings of videogame texts, this thesis explores the relationship between sports television production, and sports videogames, with a focus on features that are found in both. Features such as introductory sequences, audio commentary, in-game advertising, news tickers, and instant replay are all commonly found in both sports television and sports videogames.

Purposeful Games for Social Change

2011-2012: konstantin mitgutsch & narda alvarado.

“ Purposeful Games for Social Change ” is a list of serious games designed to foster social change/justice or to raise awareness. This list was created in order to create the Purposeful Games Framework , a tool used to assess the cohesiveness in design of serious games.

Singapore-MIT GAMBIT Game Lab

The Singapore-MIT GAMBIT Game Lab was a six-year research initiative that addressed important challenges faced by the global digital game research community and industry, with a core focus on identifying and solving research problems using a multi-disciplinary approach that can be applied by Singapore’s digital game industry. The Singapore-MIT GAMBIT Game Lab focused on building collaborations between Singapore institutions of higher learning and several MIT departments to accomplish both research and development.

Research topics explored included artificial intelligence, game design, computer graphics and animation, character design, procedurally generated content, interactive fiction, narrative design, and video game production. Game prototypes were made for these research topics during the GAMBIT summer internship program, many of which won international recognition at festivals like IndieCade and the Independent Games Festivals held at GDC and GDC China, as well as academic conferences such as Meaningful Play and Foundations of Digital Games.

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Comprehensive review and classification of game analytics

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  • Published: 01 November 2020
  • Volume 15 , pages 141–156, ( 2021 )

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research papers on game development

  • Yanhui Su   ORCID: orcid.org/0000-0002-7242-4318 1 ,
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  • Henrik Engström 1  

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As a business model, the essence of games is to provide a service to satisfy the player experience. From a business perspective, development in the game industry has led to the application of Business Intelligence (BI) becoming more and more extensive. However, related research lacks systematic examination and precise classification. This paper provides a comprehensive literature review of BI used in the game industry, focusing primarily on game analytics. This research mainly studies and discusses five aspects. First, we explore game analytics aspects in the available literature based on the traditional game value chain. Second, we find out the main purposes of using analytics in the game industry. Third, we present the problems or challenges in the game area, which can be addressed by using game analytics. Fourth, we also list different algorithms that have been used in game analytics for prediction. Finally, we summarize the research areas that have already been covered in literature but need further development. Based on the categories established after the mapping and the review findings, we also discuss the limitations of game analytics and propose potential research points for future research.

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research papers on game development

Game Analytics Research: Status and Trends

research papers on game development

Introduction

Game analytics – the basics.

Avoid common mistakes on your manuscript.

1 Introduction

The game can be recognized as a kind of service that provides game players with different experiences. With the continuous development of the game industry, more and more interdisciplinary knowledge and theories are being used. As shown in Fig.  1 , game analytics derives from Business Intelligence (BI). It reflects the combination of BI with game research [ 1 ].

figure 1

Game analytics research

Davenport and Harris [ 2 ] define BI as something that incorporates the collection, management, reporting of decision-oriented data as well as the analytical technologies and computing approaches that are performed on that data. Analytics is used for querying and reporting BI data and making advanced analytics including statistical analytics, predictive modeling, optimization, forecasting [ 2 ]. The purpose of analytics is to solve problems, make predictions in business, help decision making, promote optimization actions, and improve business performance. Game analytics is the process of identifying and communicating meaningful patterns that can be used for game decision making [ 1 ].

The purpose of this paper is to make a comprehensive literature review on the application of BI in the game industry, especially for game analytics, and provide a reasonable classification for those application areas. The rest of the paper is structured as follows: Sect.  2  provides the foundations of game analytics; Sect.  3  describes the methodology used for the comprehensive literature review; Sect.  4  presents the detailed analysis process, including identifying dimensions and codes. Section  5  discusses the results and conclusion obtained by this literature review; Sect.  6 identifies the problems and challenges in this literature review; Sect.  7 summarizes the answers to our research questions; Sect.  8 makes conclusions about our literature review and discusses the contribution to the future research.

2 Foundation

As shown in Fig.  2 , there are several branches of analytics, such as marketing analytics, risk analytics, Web analytics, and game analytics based on the previous classification [ 1 ].Game analytics has already been used in the game industry for many years. Kim et al. [ 3 ] discuss how game analytics can be used to identify in-game balancing issues. Hullett et al. [ 4 ] use it to reduce game development costs and avoid risks in game development. Moura et al. [ 5 ] apply it to visualize players’ movement paths on the map and identify the blocking points from the player side. Zoeller [ 6 ] also provides a solution to detect in-game bugs by game analytics. However, most of the research only focuses on game development and game research [ 1 ]. As for mobile game analytics, Drachen et al. [ 7 ] point out this field of research is in its infancy and the available knowledge is heavily fragmented.

figure 2

Relationship between BI and game analytics

The latest literature review is provided by Alonso-Fernández et al. [ 8 ]. They systematically summarize the research progress of applying data science and technology to serious game analytics to support decision making. First, they discussed the purpose of data science applied to the serious game. Second, they listed the related data science algorithms and analysis techniques usually used for learning analytics. Third, they discussed the research scope which mainly focused on the education side. Finally, results and conclusions are drawn, especially for why and how to apply data science and technology to serious game analytics. Besides this, Fernandes et al. [ 9 ] also provide a survey on game analytics in massively multiplayer online games. They summarize the techniques used in the analysis of massively multiplayer online games published in the past 14 years. The purpose is to outline the latest research areas, and 31 papers are selected from the IEEE and the ACM digital library for analysis.

As for the previous literature review, first, all of them mainly focused on specific games and did not provide the application and analysis for the whole value chain of the game industry. Second, the previous literature reviews were more about the summary of papers rather than giving the classification of relevant papers. Finally, previous studies lacked further discussion on the current research problems, and no specific suggestions were given for game analytics research. These limitations motivate our further research, especially for combining the game industry value chain to carry out a reasonable classification and then present the research status and trends.

Although game analytics is currently widely used in the game field, it lacks reasonable classification. Based on the previous literature review, there is no systematic research and reasonable classification about BI used in the game industry, especially for the game analytics. According to the traditional game value chain shown in Fig.  3 , the game industry starts with game developers responsible for developing the games. When the game is ready, they will find a game publisher to help with the game publishing. The publisher will publish the games through the game distributor and connect with potential players. As the traditional game value chain covers the whole game industry [ 10 ], it is reasonable to use it as the base for game analytics classification.

figure 3

Traditional game value chain

Following the traditional game value chain, we can make a preliminary classification, and it is reasonable for game analytics. At least, it should include four parts: the game development analytics, game publishing analytics, game distribution channel analytics, and game player analytics, as shown in Fig.  4 .

figure 4

Game analytics classification based on value chain

Game player analytics is vital for game analytics. The core of player analytics is to analyze the game behavior and specific preferences and guide the right direction of game development. This kind of game player analytics is based on the player segmentation, including the motivation of playing games and player game experience. The game development analytics includes verification of the gameplay, interface analytics, system analytics, process analytics, and performance analytics [ 1 ]. As for game publishing analytics, it mainly focuses on player acquisition, retention, and revenue analytics. Channel analytics primarily focuses on analyzing the distribution channel’s attributes and provides specific solutions for game promotion.

As for game metrics, it can be defined as the behavioral data source used for game analytics. El-Nasr et al. [ 1 ] present the advantages of the metric as other sources of BI, which can be used for decision making in the game industry. Metrics can be variables, features, and calculated values. The relationship between game metrics and game analytics is that game metrics are the numbers to track the game performance or development progress. Game analytics can use metrics to find out the trends and changes and support decision making.

According to the classification [ 1 ], game metrics can generally be divided into three categories, as shown in Fig.  5 , including the player metrics, process metric, and performance metrics. Usually, player metrics focus on player behavior and also customer research. The process metrics are used for game development process monitoring and management. For the performance metrics, they have a deep relationship with the game technical monitoring such as frame rate, number of bugs, and game client execution performance.

figure 5

Game metrics classification

3 Research method

A literature review is a type of review research paper that includes the classification and substantive findings and theoretical contributions to a particular topic [ 11 ]. The main driver for conducting this review was to categorize and summarize all the work done around game analytics, identify the potential gaps and commonalities in current research, and form a baseline for future research. The search strategy contained the following design decisions: Searched databases: IEEE and ACM Digital Library, Scopus, and Google Scholar. The reason to choose these databases is these expected to cover most of the researches on game analytics. As shown in Fig.  6 , the online search interest trends in Google's game analytics keywords have increased from 2005 till 2019. We can see from the overall trend that game analytics is of increasing interest from 2009 onwards.

figure 6

Online search interest trends on game analytics by Google

3.1 Research questions

The main goal of this literature review is to explore the applications of BI used in the game industry, which mainly focuses on the game analytics side. For this purpose, we have stated the following main research questions:

RQ1: What aspects of game analytics have been explored so far in the available literature?

RQ2: What are the main purposes of using analytics in the game industry?

RQ3: What problems or challenges in the game area can be addressed by using game analytics?

RQ4: What kinds of algorithms have been used in game analytics for prediction?

RQ5: What research areas have already been covered in literature, but still need further development?

3.2 Data collection

Game analytics is a broad concept used in the game industry. According to the search results, we made a table about relevant search criteria, as shown in Table 1 . We got related papers from the databases about game analytics. Then we focus on the classification and different subdivisions about the game analytics based on the game industry value chain as game analytics can be used not only in academia, but also in the game industry.

3.3 Database searched

In practice, we have queried several databases, including some of the primary databases for computer science and general scientific research. Specifically, we mainly searched: IEEE and ACM Digital Library, Scopus, and Google Scholar.

3.3.1 Search terms

To perform the searches on the databases, we focus on our three main terms of interest: Business Intelligence, game, and analytics. Based on the traditional game value chain, the terms “game analytics” can be used throughout the process, so we conducted several parallel searches. All searches are restricted to title, abstract, and keywords.

3.3.2 Study selection

After removing duplicates, we follow the four steps screening process. Step 1, we scanned the titles and abstracts of all papers and then compared them with the inclusion and exclusion criteria defined below. Step 2, after the first scan, the research was classified as possible or excluded. Step 3, we considered potential research issues and reviewed relevant papers to ensure that they provided sufficient information for our literature review. Step 4, we also make a detailed selection of each paper and only choose the highly relative paper with the game analytics.

Inclusion criteria

Journals, conference papers or books which include empirical evidence relating the game analytics.

Exclusion criteria

Publications whose full text is not available to download and publications that only focus on the serious game and also the publications not written in English.

The reason why we exclude the research on serious games as the previous literature review on this area already exists [ 8 ]. As shown in Fig.  7 , the related literature review selection process, we searched the database for 1446 papers related to game analytics. We removed duplicate papers and also papers focusing on the serious game. Then, we got 264 articles. Based on these papers, 71 papers were found to be highly correlated with game analytics through further screening of paper title abstracts and content. Then the remaining 71 papers were analyzed in detail.

figure 7

Related literature review selection process

4 Review process

4.1 identification of dimensions.

To analyze prior literature, a comprehensive, hierarchical coding system was established. Based on the literature review, according to the traditional game value chain which is shown in Fig.  3 , the game industry starts with the game developers who are responsible for developing the games. As the traditional game value chain covers the whole game.

Industry, we therefore, used these as the starting point to determine the basic dimensions of our coding system. Following the traditional game value chain, it is reasonable for game analytics to include at least four parts: the game player analytics, game development analytics, game publishing analytics, and also game distribution analytics. With the in-depth study of our literature review, we found that there were many types of research on data visualization and game prediction in the collected literature. Therefore, we added two parts of game analytics research on data visualization and prediction based on the traditional game value chain.

4.2 Identification of codes

In the specific literature review process, we used the qualitative data analysis software MAXQDA to encode the paper, which is a useful qualitative data analysis tool. For the game analytics literature review, the whole research process is rigorous, as the code can be assigned to the text segments in the selected papers. Besides this, it also provides us a convenient method to automatically extract coded text paragraphs, so that all the research data can be synthesized. In summary, we perform a high-quality analysis of the cited papers related to game analytics and provides effective assistance for literature review and also make an in-depth analysis of all selected papers.

As shown in Fig.  8 , based on the traditional game value chain, we introduce a coding system to code all the papers collected before and get the final result. For the coding process, we use two different levels of code. The first-level coding includes game development analytics, game player analytics, game publishing analytics, distribution channel analytics, game prediction analytics, and data visualization. For the second-level coding, based on the preliminary classification of game analytics [ 1 ], we introduced the sub-codes, including gameplay, performance, process, interface, system analytics, in-game behaviors, player segmentation, acquisition, retention, revenue analytics, churn prediction, and revenue prediction for further coding.

figure 8

Research result for game analytics

5 Research results

According to our literature review and the coding system, we finally divide the game analytic research into six parts. It includes game player analytics, game development analytics, game publishing analytics, game distribution analytics, and also the game prediction, data visualization. In the following, we use these categories to structure the presentation of our literature review results.

5.1 Game player analytics

Game player analytics focuses on the player itself. Traditionally, player research uses qualitative methods as part of practices and make different surveys about the player experience, satisfaction, and engagement. Therefore, most of these studies are conducted through different interviews, in-depth questionnaires, and observations. However, in practice, most game player researchers use both qualitative and quantitative approaches. For example, Canossa et al. [ 12 ] use qualitative and quantitative research methods for game player analytics which aim to identify patterns of player behavior and point out potential frustration before players leaving a game. Besides this, by collecting game remote sensing data such as usability testing for game playability testing also provide more insight research on how players play these games and what kind of behaviors, they will make during the game experiences, such as the player in-game progression and distribution research [ 13 ]. In addition, the game player analytics with the highest playtime metrics can be used for guiding game design [ 14 ].

5.1.1 Player segmentation

As for the game players, game designers not only need to focus on the gameplay development, but also need to know who should be the potential players and what their requirements are. As discussed by Hamari and Lehdonvirta [ 15 ], the specific development needs to be carried out and meet the requirements of different game players based on the player segmentation. The recent trend is that in the early game design stage, more and more considerations will be given to the requirements from the different player segmentation side. This will make the game marketing promotion more effective [ 16 ]. The segmentation can be used to describe the differentiation to meet human requirements as accurately as possible [ 17 ]. In practice, in order to make sure the game is designed considering the requirements from specific players, segmentation is an effective way which aimed at identifying different player groups [ 18 ]. The goal of segmentation is to further classify the player groups and provides games more in line with the player requirements. In fact, players’ needs for games are diverse, so the motivations for users to play games are diverse. These researches are based on the breakdown of the player behavior and make the classification. Player segmentation can be used to target the motivation of different players during the game design process. Besides this, game providers should develop different marketing strategies for different segments of gamers [ 19 ]. Kallio et al. [ 20 ] discuss that immersion is an important indicator to guide and evaluated player behavior and motivation in games. In order to conduct more effective segmentation, player research needs to take into account. Stanton et al. [ 21 ] present the first step towards a method for self-refining games in which game systems can continually be improved by player analytics. They find out that game objectives cause players to explore only a small fraction of the entire state space. Based on that result, they make a data-driven simulation solution for players to explore more space and it also can be used for complex dynamical game systems.

5.1.2 Player behaviors

Player behaviors include in-game actions and behaviors, such as navigation, interaction with game objects and other in-game entities. Player behavior research involves specific in-game behaviors throughout the game experiences. Darken and Anderegg [ 22 ] provide a new concept that regards player behavior as simulacra. Then based on the simulacra, they provide the candidate movement models which meet the different types of players. Thawonmas et al. [ 23 ] suggest detecting game bots based on their in-game behavior, especially those related to the designed purposes of bots. This approach has the potential to distinguish between human player behaviors and automated program behaviors. Nacke et al. [ 24 ] focus on a quantitative study of player behaviors in a social game called Health Seeker . Through analyzing, they make a conclusion that having the well-connected in-game social network and also the in-game interaction can improve player performance in solving game missions. Besides this, Bauckhage et al. [ 25 ] point out how to use cluster analysis for game behavioral data analysis. They target the game data scientists and present a tutorial that focuses on the application of clustering techniques to the game player behavioral data. They emphasize the potential of cluster analysis, which can be used for game design and development. However, they also point out that the application of clustering techniques to player behavioral analysis is still in its infancy.

As a player, it is easy to generate thousands of behavioral measures during the game playing session. As every time a player inputs to the game system, it brings the reactions and responses. However, accurate measures of player activities include many actions that need to be calculated immediately. For example, players in some famous games such as World of Warcraft (WOW), measurement of player behaviors could involve in many data such as the position of player’s character, health, mana, stamina, character name, level, equipment, and also the currency. Usually, this information can be collected from the game client and also the game servers. El-Nasr et al. [ 1 ] point out that analyzing behavioral data from games can be challenging, especially for massively multiplayer games. Each of these games has thousands of simultaneously active players spread across hundreds of instances in the same virtual environment. Drachen and Canossa [ 26 ] point out that player behavior analysis is based on instrumentation data, automated, detailed, quantitative information about the player behavior within the virtual environment of digital games. Hadiji et al. [ 27 ] show the ability to model, understand, and predict future player behavior has a crucial value, allowing developers to obtain data-driven insights to inform design, development, and marketing strategies. Drachen et al. [ 28 ] improve player modeling using self-organization and provide an initial study on identifying different player behaviors in a commercial game. Wallner et al. [ 29 ] use lag sequential analytics (LSA) which uses of statistical methods to aid the analytics of behavioral streams of players. Their methods provide an effective way to do the player behavior analysis, especially when faced with large behavioral streams of players.

In brief, player analytics is the foundation of game analytics. It not only can guide the game design process based on the player requirements but also can discover potential problems in game development. Player research is also vital for game publishing, which can give clear guidance about game optimizations. It also can be used to improve game retention, deliver more game revenue, and extend the game life cycle.

5.2 Game development analytics

Game analytics has many applications in game development, mainly to monitor the process of game development. It includes some technical performance and indicators of game development, such as bugs and crash monitors. Hullett et al. [ 30 ] explore how data can be used for driving game design decisions in game development. They define a mixture of qualitative and quantitative data sources and present a case study and show how data collected from the launched game can guide the game development. Game development analytics originally focus on the analytics of core gameplay, interactive analytics, and in-game system analytics [ 1 ]. However, for the game development analytics research which needs to cover the whole game development process. A new classification should be provided as it not only includes the analytics of core gameplay, interface analytics, but also includes game system analytics, process analytics, and performance analytics, as shown in Fig.  9 , game development analytics.

figure 9

Game development analytics classification

5.2.1 Gameplay analytics

Gameplay is the core of a game that is used for representing how this game is played. It relates to the user's real behavior as a player, such as in-game interaction, items trade, and navigation in the game environment. Gameplay analytics is significant to evaluate the game design and player experience. Usually, the gameplay is used to collect feedback on potential unclear elements in the game, issues with the game controls, and more general feedback on the enjoyability of the game [ 31 ]. Analysis of gameplay data is crucial for evaluating design decisions and refining a game experience [ 32 ]. Medler et al. [ 33 ] present how a visual game analytic tool can be developed to analyze the player gameplay process. They develop analytic tools that can monitor millions of players after the game is launched. Mirza-Babaei et al. [ 34 ] provide new user research methods that have been applied to capture interactions and behaviors from players across the gameplay experience and find out the potential problems for the gameplay design. Emmerich and Masuch [ 35 ] discuss the gameplay metrics used to measure player behaviors. Besides this, they also present the conceptualization, application, and evaluation of three social gameplay metrics that aim at measuring the social presence, player cooperation, and leadership, respectively. Drachen and Canossa [ 36 ] point out that gameplay metrics are instrumentation data that can measure player behavior and game interaction. Their research focuses on utilizing game metrics for gameplay analytics and guiding the development of commercial game products.

5.2.2 Interface analytics

Interface analytics includes all interactions which player performs with the game interface and menus. It is usually be tracked by setting different game variables, such as mouse sensitivity, finger touch pressure, and also monitor brightness. The data analytics of the interface is based on the premise that all the menu and button settings can be recorded. Only through the recorded data will the click volume of the interface icon and the validity of the design be effectively analyzed. Interface analytics has a deep relationship with how players interact with the game UI and also the in-game interface and system. Xu et al. [ 37 ] analyze the bottlenecks in game design conventional practices and develop a single match module. This single match module can be used to familiarize players with interface interactive analytics.

5.2.3 System analytics

System analytics covers all the actions from game engines and also the sub-systems, such as Artificial Intelligence (AI) system, in-game events, and Non-Player Character (NPC) actions. System analytics can be used to measure the effectiveness of the system design. It also can give guidance to the game developer about how to design the game system effectively. At present, system analytics focuses on in-game systems research and give guidance about game development. Weber and Mateas [ 38 ] focus on the in-game system analytics in the game StarCraft . Their research becomes a component of an AI system that makes StarCraft better based on data analysis. System analytics also can help to improve the in-game system based on data analysis.

5.2.4 Process analytics

Game process analytics focuses on the game development process and gives monitoring about the game development and provides guidance about the detailed game development process, such as using the agile development method to manage the development process. Process analysis can effectively help developers improve game development efficiency, find out potential development problems, and polish them instantly. Hullett et al. [ 30 ] focus on the collection and analytics of game data, which can be used to inform the game development process's potential problems and guide the improvement. They made a summary of the right and wrong in the development process. By process analytics, developers can better anticipate and avoid problems in their game development. However, with the development of different games, the category of game genres continues to increase. So, we also need to create related metrics to measure the game development process for different kinds of games.

5.2.5 Performance analytics

Performance analytics relates to the performance of game technical and software-based infrastructure behind a game itself. It includes the frame rate, the stability of the client execute, bandwidth, game build quality, and the number of game bugs found by QA testing. For example, Wang et al. [ 39 ] measure and analyze  WOW's performance as a representative of online games and focus on the application of different levels of packet statistics, such as the game delay and bandwidth consumption.

In brief, from the game industry side, effective data analytics during game development can help developers optimize games and verify the core gameplay, polish the interaction design, and improve the player experience. It can help developers make the right decisions, improve game development efficiency, and reduce the cost. However, in the real case, how to obtain the necessary game development data, how to effectively avoid mistakes in the direction of game development based on data analytics is still in need of further research.

5.3 Game publishing analytics

The initial game analytics focused on game development and game ontology research [ 1 ]. However, the application of game analytics in game publishing is limited and lacks systematic studies. Effective game analytics can help with the success of game release and marketing promotion and optimize the game in a targeted manner, extend the life cycle, and increase revenue. Moreira et al. [ 40 ] use the ARM (acquisition, retention, and monetization), funnel model as the basic analysis for game publishing. According to the ARM funnel model, game publishing analytics can be divided into three parts. As shown in Fig.  10 , data analytics in the game publishing process include game acquisition analytics, retention analytics, and game revenue analytics.

figure 10

Game publishing analytics classification

5.3.1 Acquisition analytics

Acquisition analytics focuses on how to save the cost of attracting new users. It also pays attention to how many new players enter the game, how many players finish the tutorial, and how much money they spend on user acquisition. In order to acquire more players, game developers usually first invest in the development and then authorize their games for publishing on target platforms. The publisher often needs to get users by buying ads or by viral distribution on social networks. Dheandhanoo et al. [ 41 ] describe an analysis-based approach to measure user acquisition effectiveness in marketing promotion. Although their analysis shows that marketing campaigns have been improved based on data analysis, there are still many potential ways to enhance user acquisition results and adjust marketing campaigns to make them more suitable for different players.

5.3.2 Retention analytics

Retention rate is a vital indicator of measuring the stickiness of games. This benchmark not only measures how players are engaged in the game, but also can be used for evaluating the game quality. The concept of retention rate comes from the marketing research side, which is provided by Hennig-Thurau and Klee [ 42 ]. They develop a conceptual foundation for investigating the customer retention process, with the use of the concepts of customer satisfaction and relationship quality. At first, the retention rate is the factor in analyzing users’ awareness of a brand. Then the concept of retention rate is applied to the game, especially in the analytics of player’s retention in games. Debeauvaisand et al. [ 43 ] analyze mechanisms of player retention in massively multiplayer games and focus on how to improve the game retention. Three key metrics are introduced which include weekly playtime, stop rate, and how long respondents have been playing. The analytics shows how the game can efficiently wield a powerful retention system. Their research also utilizes several metrics to measure the retention, including hours of play per week, stop rate, and also the length of time. However, the metrics are only the first step towards player retention in hardcore games. So, for other kinds of games, there is still a need for different kinds of game metrics to measure retention such as casual games which should have different metrics compared to hardcore games.

Demediuk et al. [ 44 ] focus on player retention research in  League of Legends  (LOL) by using survival analytics. Their study aims to understand the influence of specific behavior characteristics on the possibility of players playing the game. Survival analytics is the practical approach as it provides the ratio and assesses the features of the player who is at risk of leaving the game. The final results show that the duration between matches is a reliable indicator of retention. However, this paper does not discuss the effects of other factors on retention. As retention is a complex problem, there are all kinds of reasons which may lead the players to leave the game. So, for different games, retention is also different.

Park et al. [ 45 ] focus on the critical factors of player retention for different levels in online multiplayer games. They mainly discuss multiplayer game retention based on 51,104 various individual log analytics. They focus on exploring and analyzing the key factors influencing the players’ retention rate and the critical issue retained throughout the entire game stage. They find out that the key indicators retained varied with the game levels. The achievements of players within the game features are significant to become senior players. However, once the players arrive at the highest game levels, social networking features are vital for game retention. This finding pointed out that social networks positively affected retention when individuals form interactions with partners of appropriate standards. Yee [ 46 ] summarizes three motivational components that have a great relationship with retention: achievement, social, and immersion. Andersen et al. [ 47 ] found out that music and sound effects have little effect on player retention, but animations attract users to play more. They also discussed that minor gameplay modification affects player retention more than aesthetic variations, but how to generalize and apply these results to other game genres requires further research.

In order to improve game retention, social factors can be recognized as an effective way to improve the retention rate. Krause et al. [ 48 ] investigate the potential of gamification with social game elements for increasing retention. Players in the experiment show a significant increase of 25% in retention and 23% higher average scores when the interface is gamified. Besides this, Kayes et al. [ 49 ] analyze what factors led a blogger to continue participating in the community and releasing new content. The conclusion shows that users who face fewer constraints and have more opportunities in the community are more retained than others. In addition, the game events also influence the retention and the difference in how game users respond to events by character level, item purchasing frequency, and game-playing time band affect the retention [ 50 ]. Lucas et al. [ 51 ] analyzed successful mobile games to use persuasive mechanics for user retention. They link the persuasive mechanics to a base mechanic and corresponding psychological theory. The results can be seen as an addition to a toolkit for mobile game developers.

In short, the retention rate is a key indicator, especially for game publishing, which is an effective way to measure the game quality. It is also the critical benchmark to the game success in the market from the industry side. However, as for different types of games, how to make a set of unified metrics for measuring the retention still need to be studied further. Besides this, due to the analytics process, the acquired data will increase geometrically with the increase of the player’s information. How to effectively reduce the complexity of data analytics is also an important research direction.

5.3.3 Revenue analytics

With the rapid development of the mobile game, it takes up the largest share in the game industry. As most of the mobile games are free, players can download at any time. Hence, for freemium games, the revenue is mainly from game items, such as In-App Purchase (IAP) or advertising. Drachen et al. [ 52 ], through a case study of more than 200,000 players, analyze the relationship between the social features and the revenue in freemium casual mobile games. According to their research, classifier and regression models evaluate the impact of social interaction in casual games for the whole player’s life-cycle value. The final results show that social activities are not associated with the trend towards advanced players, but social activities will improve the game revenue.

As for freemium games, there is a big difference between the payment players who pay for IAP and the Non-payment players. Non-payment players consist of the majority of freemium players, which leads to highly uneven purchases in mobile games [ 1 ]. The key challenge for mobile game developers is to reduce the churn rate and increase players, not only by improving the retention rate, but also by considering the changes from the junior players to senior players. A related goal is to increase player’s life-cycle value (LTV) due to the significant increase in user acquisition costs for mobile applications in recent years. Considering the user acquisition costs and the market promotion fee continue to increase, the research to improve the game revenue is essential for game developers and publishers from the game industry side.

Alomari et al. [ 53 ] extract 31 features by a decision tree. The ten most important features for game success are found, which include the inviting friends’ feature, skill tree, leaderboard, Facebook, time skips, request friend help, event offers, customizable, soft currency, unlock new content. The results benefit game developers in increasing their revenue. The study also concludes that the highly related factor to revenue is the daily active user. Besides this, other features will also play a significant role in game success, such as culture, lifestyle, and loyalty to game brand and promotion. Hsu et al. [ 54 ] present a novel and intuitive market concept called indicator products used to analyze in-game purchases. Such kinds of researches benefit game designers and game researchers to observe player behaviors and improve game revenue.

5.4 Distribution channel analytics

In a broad sense, channels are the specific path to connect the games with players together. Most of the distribution channels aggregate a large number of users and form a platform for games such as the App Store and Google Play. Krafft et al. [ 55 ] provide four insights in marketing channels research, including the different marketing channel relationships, channel structures, popular topics, and market strategies. They also discuss the potential four trends that benefit from understanding the changes from channels' side, including service economies, globalization, reliance on technologies, and big data for channel decisions. Similarly, Cramer et al. [ 56 ] emphasize the contributions from researchers both from industry and academia side with experiences about the deployment and distribution. This research provides an overview of the challenges and methods to solve the distribution channels’ potential issues, such as the markets, new devices, and services. Latif et al. [ 57 ] calculate the IAP purchase rate of free and paid applications from the channel Google Play side. This research benefits the game developers and gives guidance for their development phases.

Channel distribution is vital for game success, especially for mobile games. It is common to reach potential players, such as the App Store and Google Play, which play an essential role in the distribution of mobile games. Channel analytics is to combine channel data with game data to provide decision support for game development and publishing. It is possible to compare different channels by player active rate, new player acquisition, retention, and the payment rate to determine the best channel for the game. Channels analytics cannot only identify the main target users, but also help build their loyalty relationship with the games.

However, at present, game channel research is listed as part of the market effect on statistics and analytics. The game attributes, such as the size of the game packages, may affect the player downloads. Different game channels have different player attributes, as well as the fact that the channels’ benchmark has an impact on the distribution of games, and the situation is complicated. So how to do the game analytics research combined with the attributes of channels, increasing the player downloads, and reducing the marketing promotion budget is the potential research gap, requiring more research to focus on this area.

5.5 Game prediction analytics

Game prediction analytics uses historical data to predict future events. Typically, historical data is used to build a mathematical model that captures essential trends and make the forecast for the future. So game analytics can be used to predict game performance in advance.

The churn prediction models have been developed at the early stage across different sectors, such as wireless communication, banking, and insurance. However, as for games, previous work on in-game prediction mainly focused on Massively Multiplayer Online Role-Playing Game (MMORPG) and Free-to-Play ( F2P ) mobile games. Tarng et al. [ 58 ] provide a prediction model for MMORPG gamers, which takes a player’s game hours as the input and predicts whether the player will leave soon. Hadiji et al. [ 27 ] develop a generic applicable churn prediction model, which does not rely on game design specific features, and it can be used for the churn prediction in F2P games. According to our literature review, there are currently different algorithms that can be used for churn prediction. Related research mainly includes Decision Trees, Random Forest, Support Vector Machines, Neural Networks, and the Hidden Markova Models [ 59 ].

Kim et al. [ 60 ] focus on churn prediction of mobile and online casual games. As churn prediction and analysis can provide essential insights and action cues on retention, its application using play log data has been primitive or very limited in the casual game area. They develop a standard churn analysis process for casual games. Runge et al. [ 61 ] predict the departure of high-value players in two F2P games by comparing different classifiers and feature sets. They also provide a quantitative definition of the high-value player segment, defined the churn event, and formulated the prediction as a binary classification problem. Borbora and Srivastava [ 62 ] focus on user behavior modeling by adopting a player lifecycle-based approach to predict churn for online games. They analyzed the activity characteristics of the churn players and compared them to regular players’ activity characteristics. The analysis results show that their method has a better prediction that can provide essential insights into the MMORPG games’ churn.

Perianez et al. [ 63 ] describe a churn detection method for social games. It provides a comprehensive analysis to predict player churn accurately. For each player, they predict the possibility of changes over time, which allows them to distinguish different levels of loyalty. The results show that disturbance prediction improves the accuracy and robustness of the traditional analysis. Besides, the social behavior of players in mobile games will also affect retention.

About the revenue prediction, Sifa et al. [ 64 ] focus on predicting future purchase activities by formulating the process as the combination of the player classification with a regression problem and provide the solution. Predicting payment players is the first step in building a revenue forecast model. The algorithm includes Decision Trees, Random Forest, Support Vector Machine, and Poisson Trees. The Random Forest provides excellent results and also the Poisson Trees according to three different observation periods: 1 day, 3 days, and 7 days. However, there is currently no standard about which day is better, but 7 days is widely used in the game industry. Xie et al. [ 65 ] focus on predicting the first purchase behaviors in two social games. They start to use the frequency of game events as data representations to predict first purchase.

The previous work on game prediction mainly focuses on the churn prediction and revenue prediction, as shown in Fig.  11 , the game prediction with different algorithms. The goal of game prediction is to investigate the relationship between accuracy and game actions, which means observing more in-game activity yields more accurate predictions. However, the predicted data results need to be analyzed in combination with the game itself to make the correct game optimization decisions. The overall prediction also needs to be adjusted according to different game system changes due to most of the prediction processes are dynamic. In short, the prediction method only infinitely close to the actual situation in theory, and the real value of the prediction is to provide sufficient correct guidance for game development and optimization.

figure 11

Game prediction with different algorithms

5.6 Game data visualization

The game data visualization is an essential part of game analytics. Through the visualization of the data, we can intuitively analyze the behavior changes of the players in the game, and easily understand the specific performance of game publishing. Drenikow et al. [ 66 ] provide a new tool to help collect and represent game test data, including a visual representation of players’ in-game data. They introduced tools for different visualizations of game prototypes. This tool can be easily used by game researchers to find out the.

potential issues. Lu et al. [ 67 ] introduce a new visual analysis system called BeXplorer . The system enables analysts to interact with collected data which also benefits for data analysis. The game developers can use it to collect and present game test data in an accessible and effective way. However, it is originally only designed to facilitate the analysis and visualization of various player behaviors in large MMORPG games. Latif et al. [ 57 ] make the visualization of the IAP purchase rate of free and paid applications, also the percentage of advertisement support in free and paid applications. This visualization benefits game developers in the development phases and the players for the game selection, especially for what kind of games they want to play.

Kang and Kim [ 68 ] introduce Spatio-temporal visualization technology that can be used for understanding the data more intuitively. They proposed a visual analysis technique to make data analysis easier. The player’s Spatio-temporal data can be used to directly show player behavior in the game world, which is convenient for game developers to improve the efficiency of the game development process. Drachen and Schubert [ 69 ] also review current work in the analysis of space–time games, define key terms, and outline current technologies and game applications. They finally summarized the current problems and challenges in this field and proposed the visualization methods of Spatio-temporal analytics and present four critical areas of spatial and Spatio-temporal analytics that benefit the game design and development as well.

6 Problems and challenges

The current practices of game analytics have attracted more and more attention. However, there are still some problems in the research at this stage. As game analytics, which can be recognized as using BI in the game industry, it can be applied throughout the whole game industry value chain. However, according to the literature review, there are still potential gaps in game analytics research. As shown in Fig.  8 , based on our coding system, the research on game publishing analytics and the distribution channel analytics are underrepresented. Related research still needs to be extended, such as how to use data analytics to drive the game publishing effectively and how to set up the correct game metrics, which can be used for measuring the new game version update and also the channel’s performance. Based on our literature review, the related research areas are full of challenges.

6.1 Game player analytics challenge

Game player analytics is based on the behavior analytics of game users. Player behavior in the game changes instantly will bring a lot of data, such as MMORPG games. There will be a large amount of player data that needs to be processed. How to improve the efficiency of player analytics through the processing of massive data is the potential research area. However, the player analytics results are established on enough data collection, which will bring two problems. On one hand, how to obtain accurate results with little data to save the data collection cost is a potential issue. On the other hand, how to ensure that the collected data can represent the player real demands is also a challenge. Besides this, the goal of player segmentation analytics is to classify the player’s groups further and provide games more in line with the player requirements. It is also worth studying how to meet the needs of different players in the same game based on player analytics.

6.2 Game development analytics challenge

At present, most game analytics research for game development focuses on ensuring that the gameplay is sufficient to meet the player requirements and also that the game development process is controllable. However, how to ensure that the in-game system design can be adjusted according to the player feedbacks after the game launched is almost ignored. The design of the in-game system needs to be dynamically changed according to the player feedback, rather than being unchanging. Hence, game analytics used for driving game development still needs in-depth research. Besides this, during the game development process, the potential problem that developers facing is that before the game is launched, it is hard to make sure the players will recognize the original game design. So, how to use data analysis to evaluate whether the design of the game system meets the player requirements and avoid some design mistakes is also worthy of doing further research.

6.3 Game publishing analytics challenge

According to the literature review, game analytics related to game publishing research is not comprehensive. At present, most research mainly focuses on the game retention and revenue analytics side. There is no detailed analytics about the entire process of game publishing. For example, keep releasing the new game content is essential for game publishing, but how to evaluate the game new version update performance is unknown. How to do the marketing based on data analytics and how to maintain the active player and deliver more revenue for the launched game still need further research. The lack of such kind of game publishing analytics makes it hard to form an effective game optimization after the game launch. It may also lead to game publishing failures. So, this part of the research is vital and valuable to pursue. Besides this, with the emergence of App Store and Google Play and similar kinds of third-party distribution channels, indie game developers who have fewer resources for the game development can submit their games to these channels and publish games themselves. Hence, how to help these indie game developers guide their game publishing by game analytics also needs further research.

6.4 Distribution channel analytics challenge

According to our literature review, at present, channel analytics is ignored by most game analytics researchers. However, for games, its distribution channels differ from other marketing channels as game channels have their own attributes. For example, players from the iOS App Store channel are quite different from the Google Play channel, which results in various performances of the same game in these two different channels. The attributes of games also have a significant influence on the distribution channels. In addition, the channel attributes and benchmarks are also essential factors that restrict game distribution from the game industry side. Channel analytics is based on big data from different channels. So how to get these data is a potential challenge for research. Besides this, according to the relationship between game analytics and also game metrics, game metrics can be used to measure the changes and evaluate the problems by game analytics. However, there is a lack of corresponding metrics and analysis, especially for the game channel side, which also brings the channel analytics problems. In order to highlight the critical role of channels for game distribution and promotion, more research needs to focus on the field of channel distribution, especially how to use data analytics to obtain target players from channels and reduce the cost of game promotion.

6.5 Game prediction analytics challenge

At present, game prediction research mainly focuses on predicting player churn and game revenue. However, the prediction of game revenue primarily focused on predicting player purchase behavior, which lacks a useful analysis of the prediction of game revenue based on historical revenue data. In practice, game developers face issues on how to do a revenue forecast for their games during the game publishing process. Based on the revenue forecast, they can make a plan for marketing promotion, such as how much marketing budget needs to be used for different channels for new user acquisition based on the Return on Investment (ROI). It is also possible to follow up on game development costs and set up benchmarks to evaluate game publishing performance. However, making the revenue forecast is hard, especially for indie game developers who have fewer resources to do game development and have little or no revenue forecast experience. The prediction of game revenue, especially how to estimate future revenue based on the historical game revenue data, deserves in-depth research.

6.6 Game data visualization challenge

At present, most game data visualization studies mainly focus on providing tools to collect and represent game data, such as a visual representation of player data, displaying all the data through visual information, and enabling analysts to interact with collected data. However, few research studies focus on the visual data provided by the game itself, such as the in-game data visualization. As the visualization of in-game data can help players become familiar with the game and enhance the game experience. In the future, how to provide players with a visualized gaming experience inside the game by game analytics is also worthy of doing in-depth research.

7 Discussion

Based on our comprehensive literature review, the detailed answers to the five questions we raised before are discussed and answered.

First, as for RQ1, regarding game analytics, the aspects have been explored in the available literature, we give the preliminary classification and the overview of different research areas based on the traditional game value chain [ 10 ]. As shown in Fig.  8 , it includes game player analytics, game development analytics, game publishing analytics, game distribution analytics, game prediction, and data visualization, which are orthogonal to the traditional game value chain. Then, we innovatively use these categories to structure the presentation of review results.

Second, as for RQ2, the main purposes of using analytics in the game industry, through our literature review, Koskenvoima and Mäntymäki [ 70 ] make the survey with small and medium-size freemium game developers about the reasons why they use game analytics. They analyze the collected data through in-depth interviews with a group of small and medium-sized game developers. Research results show that the main reason for using game analytics including three parts. First, game analysis can be used to assist the development and game design. Second, based on data analysis, the game developer can effectively reduce the risk of game development and publishing. Third, through game analytics, the developers can negotiate with investors and publishers. Flunger et al. [ 71 ] focus on analytical and predictive models for F2P games. They discuss game analytic use in the F2P game, especially for helping the game developers with game player churn prediction and player lifetime value prediction. Besides this, based on the traditional game value chain [ 10 ], we also find that game analytics in the game industry can be used to improve the efficiency of game development and to increase the game revenue and guide the game optimization and also the game publishing.

Third, as for RQ3, the problems or challenges in the game area can be addressed by using game analytics. Based on our reasonable classification and literature review, we can see that the game analytics can be used not only in the game industry value chain to solve the problem of game industry chain each link, but also can be used in the field of game data visualization and game prediction. Then combined with game industry requirements, we focus on the problems or challenges discussing in Sect.  6 . It includes player analytics, game development analytics, game publishing analytics, distribution channel analytics, and also the game prediction, and also the game data visualization problems, and challenges.

Fourth, as for RQ4 ,  the algorithms have been used in game analytics for prediction, we summarize the main algorithms which can be used for analyzing game data and making the prediction. As discussed in this paper, game analytics can predict trends, understand player churn rate, and predict game revenue. There are currently different algorithms that can be used for churn prediction. Related research mainly includes Decision Trees, Random Forest, Support Vector Machines, Neural Networks, and the Hidden Markova Models [ 59 ]. However, compared with the churn prediction, making the revenue forecast is hard. The prediction of game revenue, especially how to estimate future revenue by using time series prediction algorithms need to do in-depth research.

Finally, as for RQ5 , the research areas have already been covered in literature, but still, need further development. This paper also presents details about the potential research gaps ignored by many researchers especially for game publishing analytics and also game distribution channel analytics [ 56 ]. Besides this, how to improve the player analytics for different game contents, how to do the game development analytics and give a valuable suggestion about game optimizations, how to keep the in-game economy balance by game analytics, and how to do the publishing analytics to extend the game lifetime cycle still need to do further research.

In addition, according to our literature review, there are still such potential research gaps in game analytics. On the one hand, from the game industry side, little game analytics research can help promote the game BI knowledge sharing or standardization. As confidentiality data such as revenue and churn, and retention make the knowledge sharing difficult. That is also the potential reason why game analytics research is currently fragmented [ 7 ]. This is to be expected in the explorative phase of a new domain being established, especially for the game publishing and game distribution channels research. On the other hand, as for game analytics, after the game launched, many data statistics are recognized as the secrets of companies. So, the game industry has concerns about collaboration with academia. Therefore, game analytics used in the game industry lacks a theoretical basis. That is also the main reason for the potential gaps between the game industry and academic research.

8 Conclusion

In this paper, a comprehensive literature review and a more detailed classification of game analytics are provided. As the traditional game value chain covers the whole game industry, we innovatively adopt it as a classification criterion and use it as a starting point to determine the basic dimensions of our coding system. By following this traditional game value chain, it is reasonable for game analytics to include at least four parts: game development analytics, game publishing analytics, game distribution channel analytics, and game player analytics. With the in-depth study of our literature review, we found that many research types also focus on data visualization and game prediction in the collected literature. Therefore, we added two parts of game analytics research on data visualization and prediction as the orthogonal to the traditional game value chain. Besides this, game as a service means providing the player game content on a continuing revenue model similar to software as a service. From game as a service side, it is also meant to use BI in the game industry. It can provide a service-oriented decision system through data analysis to guide the whole game industry, especially for the game publishing analytics, which can help acquire players, maintain players, and maximize game revenue effectively.

The main contribution of this paper includes four parts. First, based on the game industry value chain, we provide a comprehensive literature review about game analytics. Second, based on the coding system, we overview the current research status and point out the potential research trends about game analytics. Third, we also discuss the main purposes of using game analytics in the game industry and the related algorithms used for game prediction. Finally, we present the research gaps and also potential reasons why these research gaps exist. This research is valuable as a baseline for future research in this area.

El-Nasr MS, Drachen A, Canossa A (2013) Game analytics: maximizing the value of player data. Springer, London

Book   Google Scholar  

Davenport TH, Harris TH (2007) Competing on analytics: the new science of winning. Harvard Business School Press, USA

Google Scholar  

Kim JH, Phillips B, Pagulayan RJ, Schuh E, Wixon D, Gunn DV (2008) Tracking real-time user experience (TRUE) a comprehensive instrumentation solution for complex systems. In: SIGCHI conference on human factors in computing systems, pp 443–452

Hullett K, Nagappan N, Schuh E, Hopson J (2011) Data analytics for game development, vol 80, pp 940–943

Moura D, Seif El-Nasr M, Shaw CD (2011) Visualizing and understanding players’ behavior in video games: discovering patterns and supporting aggregation and comparison. Game Pap, pp 2–7

Zoeller G (2013) Game development telemetry in production. In: Seif El-Nasr M, Drachen A, Canossa A (eds) Game analytics—maximizing the value of player data. Springer, New York

Drachen A, Ross N, Runge J, Sifa R (2016) Stylized facts for mobile game analytics. In: 2016 IEEE conference on computational intelligence and games (CIG), pp 1–8

Alonso-Fernández C, Calvo-Morata A, Freire M, Martínez-Ortiz I, Fernández-Manjón B (2019) Applications of data science to game learning analytics data: a systematic literature review. Comput Ed 141:103612

Article   Google Scholar  

Fernandes LV, Castanho CD, Jacobi RP (2018) A survey on game analytics in massive multiplayer online games. In: 17th Brazilian symposium on computer games and digital entertainment (SBGames), pp 21–30

Kelly C, Mishra B, Jequinto J (2015) The pulse of gaming. Accenture. https://www.accenture.com/t20150709T093434__w__/us-en/_acnmedia/Accenture/Conversion-Assets/LandingPage/Documents/3/ . Accessed 15 Jan 2019

Creswell J (2013) Research design: qualitative, quantitative, and mixed method approaches, vol 4. SAGE Publications, California

Canossa A, Sørensen JRM, Drachen A (2011) Arrrgghh: blending quantitative and qualitative methods to detect player. Proc FDG 2011:61–68

Thompson C (2007) Halo 3: how microsoft labs invented a new science of play. Wired Mag 15(9):15–19

Raharjo K, Lawrence R (2016) Using multi-arm bandits to optimize game play metrics and effective game design. Int J Comput Inf Eng 10(10):1758–1761

Hamari J, Lehdonvirta V (2010) Game design as marketing: how game mechanics create demand for virtual goods. Int J Bus Sci Appl Manag 5(1):14–29

Huotari K, Hamari J (2012) Defining gamification—a service marketing perspective. In: Proceedings of the 16th international academic mindtrek conference, Tampere, Finland, pp 3–5

Kotler P, Keller K (2006) Marketing management, 12th edn. Pearson Prentice Hall, New Jersey

Hamari J, Koivisto J (2013) Social motivations to use gamification: an empirical study of gamifying exercise. In: Proceedings of the 21st European conference on information systems. Utrecht, Netherlands, pp 5–8

Tseng FC (2011) Segmenting online gamers by motivation. Expert Syst Appl 38(6):7693–7697

Kallio KP, Mäyrä F, Kaipainen K (2011) At least nine ways to play: approaching gamer mentalities. Games Cult 6(4):327–353

Stanton M, Humberston B, Kase B, O’Brien JF, Fatahalian K, Treuille A (2014) Self-refining games using player analytics. ACM Trans Graph 33(4):1–9

Darken C, Anderegg B (2008) Game AI programming wisdom, vol 4. Charles River Media, Newton, pp 419–427

Thawonmas R, Kashifuji Y, Chen KT (2008) Detection of MMORPG bots based on behavior analysis. In: Proceedings of the 2008 international conference on advances in computer entertainment technology (ACE), USA, pp 91–94

Nacke LE, Klauser M, Prescod P (2014) Social player analytics in a facebook health game. In: Proceedings of HCI Korea (HCIK'15). Hanbit Media, Inc. South Korea, pp 180–187

Bauckhage C, Drachen A, Sifa R (2014) Clustering game behavior data. IEEE Trans Comput Intell AI Games 7(3):266–278

Drachen A, Canossa A (2011) Evaluating motion: spatial user behavior in virtual environments. Int J Arts Technol 4(3):294–314

Hadiji F, Sifa R, Drachen A, Thurau C, Kersting K, Bauckhage C (2014) Predicting player churn in the wild. In: Proceedings of the IEEE conference on computational intelligence and games (CIG), pp 1–8

Drachen A, Canossa A, Yannakakis G (2009) Player modeling using self-organization in tomb raider: underworld. In: Proceedings of IEEE computational intelligence in games (CIG), pp 1–8

Wallner G (2015) Sequential analysis of player behavior. In: Proceedings of the 2015 annual symposium on computer–human interaction in play (CHI PLAY’15). ACM, New York, NY, USA, pp 349–358

Hullett K, Nagappan N, Schuh E, Hopson J (2012) Empirical analysis of user data in game software development. In: Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement. ACM, Sweden, pp 89–98

Isbister K, Schaffer N (2008) Game usability: advancing the player experience. Morgan Kaufman Publishers, Burlington

Andersen E, Liu YE, Apter E, Boucher-Genesse F, Popovic Z (2010) Gameplay analysis through state projection. In: Proceedings of the fifth international conference on the foundations of digital games California. ACM, pp 1–8

Medler B, John M, Lane J (2011) Data cracker: developing a visual game analytic tool for analyzing online gameplay. In: CHI'11 proceedings of the SIGCHI conference on human factors in computing systems. Vancouver, BC, Canada, pp 2365–2374

Mirza-Babaei P, Wallner G, McAllister G, Nacke L (2014) Unified visualization of quantitative and qualitative playtesting data. In: CHI 2014 extended abstracts on human factors in computing systems, pp 1363–1368

Emmerich K, Masuch M (2016) Game metrics for evaluating social in-game behavior and interaction in multiplayer games. In: Proceedings of the 13th international conference on advances in computer entertainment technology, pp 1–8

Drachen A, Canossa A (2009) Towards gameplay analysis via gameplay metrics. In: Proceedings of the 13th Mind Trek, pp 202–209

Xu P, Ma X, Qu H, Li Q, Wu Z (2018) A multi-phased co-design of an interactive analytics system for MOBA game occurrences. In: Proceedings of the 2018 designing interactive systems conference, pp 1321–1332

Weber BG, Mateas M (2009) A data mining approach to strategy prediction. In: IEEE Symposium on computational intelligence and games, pp 140–147

Wang X, Kim H, Vasilakos AV, Kwon TT, Choi Y, Choi S, Jang H (2009) Measurement and analysis of world of warcraft in mobile WiMAX networks. In: Proceedings of the 8th workshop on network and system support for games, pp 1–8

Moreira ÁVM, Filho VV, Ramalho GL (2009) Understanding mobile game success: a study of features related to acquisition, retention and monetization. SBC J Interact Syst 5:2–13

Dheandhanoo T, Theppaitoon S, Setthawong P (2016) Game play analytics to measure the effect of marketing on mobile free-to-play games. In: 2nd International conference on science in information technology (ICSITech), pp 125–130

Hennig-Thurau T, Klee A (1997) The impact of customer satisfaction and relationship quality on customer retention: a critical reassessment and model development. Psychol Mark 14:737–765

Debeauvaisand T, Schiano DJ, Yee N (2011) If you build it they might stay: retention mechanisms in world of warcraft. In: Foundations of digital games conference, Bordeaux, France, pp 180–187

Demediuk S, Murrin A, Bulger D, Tamassia M(2018) Player retention in league of legends: a study using survival analysis. In: Proceedings of the Australasian computer science week multiconference, pp 43:1–43:9

Park K, Cha M, Kwak H, Chen KT (2017) Achievement and friends: Key factors of player retention vary across player levels in online multiplayer games. In: Proceedings of the 26th international conference on world wide web companion, pp 445–453

Yee N (2007) Motivations of play in online games. J Cyber Psychol Behav 9:772–775

Andersen E, Liu YE, Snider R, Szeto R, Popović Z (2011) Placing a value on aesthetics in online casual games. In: Proceedings of the 2011 annual conference on Human factors in computing systems, pp 1275–1278

Krause M, Mogalle M, Pohl H, Williams J (2015) A playful game changer: fostering student retention in online education with social gamification. In: 15 Proceedings of learning scale conference, pp 95–102

Kayes I, Zuo X, Wang D, Chakareski J (2014) To blog or not to blog: characterizing and predicting retention in community blogs. In: Proceedings of the international conference on social computing (SocialCom’14), vol 7. ACM, New York, pp 1–8

Kim K H, and Kim H K (2019) Oldie is goodie: effective user retention by in-game promotion event analysis. In: Extended abstracts of the annual symposium on computer–human interaction in play companion extended abstracts, pp 171–180

Legner L, Eghtebas C, Klinker G (2019) Persuasive mobile game mechanics for user retention. In: Extended abstracts of the annual symposium on computer–human interaction in play companion extended abstracts, pp 493–500

Drachen A, Pastor M, Liu A, Fontaine DJ, Chang Y, Runge J, Sifa R (2018) To be or not to be...social: incorporating simple social features in mobile game customer lifetime value predictions. In: ACSW 18 proceedings of the Australasian computer science week multi conference article no. 40, pp 1–10

Alomari KM, Soomro TR, Shaalan K (2016) Mobile gaming trends and revenue models. Trends in applied knowledge-based systems and data science. IEA/AIE, pp 671–683

Hsu SY, Hsu CL, Jung SY, Sun CT (2017) Indicator products for observing market conditions and game trends in MMOG. In: FDG 17. In: Proceedings of the 12th international conference on the foundations of digital games, pp 1–8

Krafft M, Goetz O, Mantrala M, Sotgiu F, Tillmanns S (2015) The evolution of marketing channel research domains and methodologies: an integrative review and future directions. J Retail 91(4):569–585

Cramer H, Rost M, Belloni N, Bentley F, Chincholle D (2010) Research in the large. Using app stores, markets, and other wide distribution channels in Ubicomp research. In: Proceedings of the 12th ACM international conference adjunct papers on Ubiquitous computing—Adjunct, pp 511–514

Latif RMA, Abdullah M T, Shah SUA, Farhan M, ljaz F, Karim A (2019) Data scraping from google play store and visualization of its content for analytics. In: Proceedings of the 2nd international conference on computing, mathematics and engineering technologies, ICoMET, pp 1–8

Tarng P Y, Chen K T, Huang P (2008) An analysis of WoW players’game hours. In: Proceedings of the 7th ACM SIGCOMM workshop on network and system support for games, pp 47–52

Drachen A, Lundquist E T, Kung Y, Rao P, Sifa R, Runge J, Klabjan D (2016). Rapid prediction of player retention in free-to-play mobile games. In: Twelfth artificial intelligence and interactive digital entertainment conference, pp 23–29

Kim S, Choi D, Lee E, Rhee W (2017) Churn prediction of mobile and online casual games using play log data. PLoS ONE 12(7):e0180735

Runge J, Gao P, Garcin F, Faltings B (2014) Churn prediction for high-value players in casual social games. In: Computational intelligence and games (CIG) IEEE conference, pp 1–8

Borbora Z H, Srivastava J (2012) User behavior modelling approach for churn prediction in online games. In: Proceedings of the IEEE international privacy security risk trust conference. IEEE, pp 51–60

Perianez A, Saas A, Guitart A, Magne C (2016) Churn prediction in mobile social games: towards a complete assessment using survival ensembles. In: Proceedings of IEEE international conference on data science and advanced analytics, pp 564–573

Sifa R, Hadiji F, Runge J, Drachen A, Kersting K, Bauckhage C (2015) Predicting purchase decisions in mobile free-to-play games. In: Proceedings of AAAI AIIDE, pp 79–85

Xie H, Devlin S, Kudenko D, Cowling P (2015) Predicting player disengagement and first purchase with event-frequency based data representation. In: Proceedings of CIG, pp 230–237

Drenikow B, Arppe D, Mirza-Babaei P, Hogue A (2014) Interactive 3D visualization of playtesting data. In: IEEE games media entertainment, pp 1–1

Lu J, Xie X, Lan J, Peng TQ, Wu Y, Chen W (2019) BeXplorer: visual analytics of dynamic interplay between communication and purchase behaviors in MMORPGs. Vis Inform 3(2):87–101

Kang SJ, Kim SK (2015) Automated spatio-temporal analysis techniques for game environment. Multimed Tools Appl 74(16):6323–6329

Drachen A, Schubert M (2013) Spatial game analytics and visualization. In: IEEE conference on computational intelligence in games (CIG), pp 1–8

Koskenvoima A, Mäntymäki M (2015) Why do small and medium-size freemium game developers use game analytics? In: Conference on e-business, e-Services and e-Society, pp 326–337

Flunger R, Mladenow A, Strauss C (2019) Game analytics on free to play. In: Younas M, Awan I, Benbernou S (eds) Big data innovations and applications. Innovate-Data. Communications in computer and information science, vol 1054. Springer, Berlin, pp 133–141

Chapter   Google Scholar  

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Acknowledgements

This research was supported by the University of Skövde, Sweden Game Arena and the Game Hub Scandinavia 2.0 (NYPS 20201849) project under the EU regional development fund Interreg Öresund-Kattegat-Skagerrak.

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Su, Y., Backlund, P. & Engström, H. Comprehensive review and classification of game analytics. SOCA 15 , 141–156 (2021). https://doi.org/10.1007/s11761-020-00303-z

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