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Metric-centered and technology-independent architectural views for software comprehension

The maintenance of applications is a crucial activity in the software industry. The high cost of this process is due to the effort invested on software comprehension since, in most of cases, there is no up-to-...

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Back to the future: origins and directions of the “Agile Manifesto” – views of the originators

In 2001, seventeen professionals set up the manifesto for agile software development. They wanted to define values and basic principles for better software development. On top of being brought into focus, the ...

Investigating the effectiveness of peer code review in distributed software development based on objective and subjective data

Code review is a potential means of improving software quality. To be effective, it depends on different factors, and many have been investigated in the literature to identify the scenarios in which it adds qu...

On the benefits and challenges of using kanban in software engineering: a structured synthesis study

Kanban is increasingly being used in diverse software organizations. There is extensive research regarding its benefits and challenges in Software Engineering, reported in both primary and secondary studies. H...

Challenges on applying genetic improvement in JavaScript using a high-performance computer

Genetic Improvement is an area of Search Based Software Engineering that aims to apply evolutionary computing operators to the software source code to improve it according to one or more quality metrics. This ...

Actor’s social complexity: a proposal for managing the iStar model

Complex systems are inherent to modern society, in which individuals, organizations, and computational elements relate with each other to achieve a predefined purpose, which transcends individual goals. In thi...

Investigating measures for applying statistical process control in software organizations

The growing interest in improving software processes has led organizations to aim for high maturity, where statistical process control (SPC) is required. SPC makes it possible to analyze process behavior, pred...

An approach for applying Test-Driven Development (TDD) in the development of randomized algorithms

TDD is a technique traditionally applied in applications with deterministic algorithms, in which the input and the expected result are known. However, the application of TDD with randomized algorithms have bee...

Supporting governance of mobile application developers from mining and analyzing technical questions in stack overflow

There is a need to improve the direct communication between large organizations that maintain mobile platforms (e.g. Apple, Google, and Microsoft) and third-party developers to solve technical questions that e...

Working software over comprehensive documentation – Rationales of agile teams for artefacts usage

Agile software development (ASD) promotes working software over comprehensive documentation. Still, recent research has shown agile teams to use quite a number of artefacts. Whereas some artefacts may be adopt...

Development as a journey: factors supporting the adoption and use of software frameworks

From the point of view of the software framework owner, attracting new and supporting existing application developers is crucial for the long-term success of the framework. This mixed-methods study explores th...

Applying user-centered techniques to analyze and design a mobile application

Techniques that help in understanding and designing user needs are increasingly being used in Software Engineering to improve the acceptance of applications. Among these techniques we can cite personas, scenar...

A measurement model to analyze the effect of agile enterprise architecture on geographically distributed agile development

Efficient and effective communication (active communication) among stakeholders is thought to be central to agile development. However, in geographically distributed agile development (GDAD) environments, it c...

A survey of search-based refactoring for software maintenance

This survey reviews published materials related to the specific area of Search-Based Software Engineering that concerns software maintenance and, in particular, refactoring. The survey aims to give a comprehen...

Guest editorial foreword for the special issue on automated software testing: trends and evidence

Similarity testing for role-based access control systems.

Access control systems demand rigorous verification and validation approaches, otherwise, they can end up with security breaches. Finite state machines based testing has been successfully applied to RBAC syste...

An algorithm for combinatorial interaction testing: definitions and rigorous evaluations

Combinatorial Interaction Testing (CIT) approaches have drawn attention of the software testing community to generate sets of smaller, efficient, and effective test cases where they have been successful in det...

How diverse is your team? Investigating gender and nationality diversity in GitHub teams

Building an effective team of developers is a complex task faced by both software companies and open source communities. The problem of forming a “dream”

Investigating factors that affect the human perception on god class detection: an analysis based on a family of four controlled experiments

Evaluation of design problems in object oriented systems, which we call code smells, is mostly a human-based task. Several studies have investigated the impact of code smells in practice. Studies focusing on h...

On the evaluation of code smells and detection tools

Code smells refer to any symptom in the source code of a program that possibly indicates a deeper problem, hindering software maintenance and evolution. Detection of code smells is challenging for developers a...

On the influence of program constructs on bug localization effectiveness

Software projects often reach hundreds or thousands of files. Therefore, manually searching for code elements that should be changed to fix a failure is a difficult task. Static bug localization techniques pro...

DyeVC: an approach for monitoring and visualizing distributed repositories

Software development using distributed version control systems has become more frequent recently. Such systems bring more flexibility, but also greater complexity to manage and monitor multiple existing reposi...

A genetic algorithm based framework for software effort prediction

Several prediction models have been proposed in the literature using different techniques obtaining different results in different contexts. The need for accurate effort predictions for projects is one of the ...

Elaboration of software requirements documents by means of patterns instantiation

Studies show that problems associated with the requirements specifications are widely recognized for affecting software quality and impacting effectiveness of its development process. The reuse of knowledge ob...

ArchReco: a software tool to assist software design based on context aware recommendations of design patterns

This work describes the design, development and evaluation of a software Prototype, named ArchReco, an educational tool that employs two types of Context-aware Recommendations of Design Patterns, to support us...

On multi-language software development, cross-language links and accompanying tools: a survey of professional software developers

Non-trivial software systems are written using multiple (programming) languages, which are connected by cross-language links. The existence of such links may lead to various problems during software developmen...

SoftCoDeR approach: promoting Software Engineering Academia-Industry partnership using CMD, DSR and ESE

The Academia-Industry partnership has been increasingly encouraged in the software development field. The main focus of the initiatives is driven by the collaborative work where the scientific research work me...

Issues on developing interoperable cloud applications: definitions, concepts, approaches, requirements, characteristics and evaluation models

Among research opportunities in software engineering for cloud computing model, interoperability stands out. We found that the dynamic nature of cloud technologies and the battle for market domination make clo...

Game development software engineering process life cycle: a systematic review

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. Multidisc...

Correlating automatic static analysis and mutation testing: towards incremental strategies

Traditionally, mutation testing is used as test set generation and/or test evaluation criteria once it is considered a good fault model. This paper uses mutation testing for evaluating an automated static anal...

A multi-objective test data generation approach for mutation testing of feature models

Mutation approaches have been recently applied for feature testing of Software Product Lines (SPLs). The idea is to select products, associated to mutation operators that describe possible faults in the Featur...

An extended global software engineering taxonomy

In Global Software Engineering (GSE), the need for a common terminology and knowledge classification has been identified to facilitate the sharing and combination of knowledge by GSE researchers and practition...

A systematic process for obtaining the behavior of context-sensitive systems

Context-sensitive systems use contextual information in order to adapt to the user’s current needs or requirements failure. Therefore, they need to dynamically adapt their behavior. It is of paramount importan...

Distinguishing extended finite state machine configurations using predicate abstraction

Extended Finite State Machines (EFSMs) provide a powerful model for the derivation of functional tests for software systems and protocols. Many EFSM based testing problems, such as mutation testing, fault diag...

Extending statecharts to model system interactions

Statecharts are diagrams comprised of visual elements that can improve the modeling of reactive system behaviors. They extend conventional state diagrams with the notions of hierarchy, concurrency and communic...

On the relationship of code-anomaly agglomerations and architectural problems

Several projects have been discontinued in the history of the software industry due to the presence of software architecture problems. The identification of such problems in source code is often required in re...

An approach based on feature models and quality criteria for adapting component-based systems

Feature modeling has been widely used in domain engineering for the development and configuration of software product lines. A feature model represents the set of possible products or configurations to apply i...

Patch rejection in Firefox: negative reviews, backouts, and issue reopening

Writing patches to fix bugs or implement new features is an important software development task, as it contributes to raise the quality of a software system. Not all patches are accepted in the first attempt, ...

Investigating probabilistic sampling approaches for large-scale surveys in software engineering

Establishing representative samples for Software Engineering surveys is still considered a challenge. Specialized literature often presents limitations on interpreting surveys’ results, mainly due to the use o...

Characterising the state of the practice in software testing through a TMMi-based process

The software testing phase, despite its importance, is usually compromised by the lack of planning and resources in industry. This can risk the quality of the derived products. The identification of mandatory ...

Self-adaptation by coordination-targeted reconfigurations

A software system is self-adaptive when it is able to dynamically and autonomously respond to changes detected either in its internal components or in its deployment environment. This response is expected to ensu...

Templates for textual use cases of software product lines: results from a systematic mapping study and a controlled experiment

Use case templates can be used to describe functional requirements of a Software Product Line. However, to the best of our knowledge, no efforts have been made to collect and summarize these existing templates...

F3T: a tool to support the F3 approach on the development and reuse of frameworks

Frameworks are used to enhance the quality of applications and the productivity of the development process, since applications may be designed and implemented by reusing framework classes. However, frameworks ...

NextBug: a Bugzilla extension for recommending similar bugs

Due to the characteristics of the maintenance process followed in open source systems, developers are usually overwhelmed with a great amount of bugs. For instance, in 2012, approximately 7,600 bugs/month were...

Assessing the benefits of search-based approaches when designing self-adaptive systems: a controlled experiment

The well-orchestrated use of distilled experience, domain-specific knowledge, and well-informed trade-off decisions is imperative if we are to design effective architectures for complex software-intensive syst...

Revealing influence of model structure and test case profile on the prioritization of test cases in the context of model-based testing

Test case prioritization techniques aim at defining an order of test cases that favor the achievement of a goal during test execution, such as revealing failures as earlier as possible. A number of techniques ...

A metrics suite for JUnit test code: a multiple case study on open source software

The code of JUnit test cases is commonly used to characterize software testing effort. Different metrics have been proposed in literature to measure various perspectives of the size of JUnit test cases. Unfort...

Designing fault-tolerant SOA based on design diversity

Over recent years, software developers have been evaluating the benefits of both Service-Oriented Architecture (SOA) and software fault tolerance techniques based on design diversity. This is achieved by creat...

Method-level code clone detection through LWH (Light Weight Hybrid) approach

Many researchers have investigated different techniques to automatically detect duplicate code in programs exceeding thousand lines of code. These techniques have limitations in finding either the structural o...

The problem of conceptualization in god class detection: agreement, strategies and decision drivers

The concept of code smells is widespread in Software Engineering. Despite the empirical studies addressing the topic, the set of context-dependent issues that impacts the human perception of what is a code sme...

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• Jahns V (2022) Data Fabric and Datafication ACM SIGSOFT Software Engineering Notes 10.1145/3561846.3561854 47 :4 (30-31) Online publication date: 28-Sep-2022 https://dl.acm.org/doi/10.1145/3561846.3561854
• Forti S (2022) Trending Topics in Software Engineering (1) ACM SIGSOFT Software Engineering Notes 10.1145/3561846.3561847 47 :4 (6-6) Online publication date: 28-Sep-2022 https://dl.acm.org/doi/10.1145/3561846.3561847

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Agile software requirements engineering challenges-solutions—a conceptual framework from systematic literature review.

1. Introduction

2. methodology, 2.1. literature search strategy, 2.2. data analysis, 3. results from slr, 3.1. agile requirements engineering challenges, 3.2. agile requirements engineering solutions, 3.3. discussion—three-dimensional classification of challenges and solutions, 3.3.1. dimension of organisation/business challenges, 3.3.2. dimensions of project management challenges, 3.3.3. dimensions of agile methodology challenges, 3.3.4. dimensions of agile methodology solutions, 4. a framework of orchestrating agile challenges, 5. conclusions, author contributions, data availability statement, conflicts of interest.

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Share and Cite

Hoy, Z.; Xu, M. Agile Software Requirements Engineering Challenges-Solutions—A Conceptual Framework from Systematic Literature Review. Information 2023 , 14 , 322. https://doi.org/10.3390/info14060322

Hoy Z, Xu M. Agile Software Requirements Engineering Challenges-Solutions—A Conceptual Framework from Systematic Literature Review. Information . 2023; 14(6):322. https://doi.org/10.3390/info14060322

Hoy, Zoe, and Mark Xu. 2023. "Agile Software Requirements Engineering Challenges-Solutions—A Conceptual Framework from Systematic Literature Review" Information 14, no. 6: 322. https://doi.org/10.3390/info14060322

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📚 A curated list of papers for Software Engineers

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Papers for software engineers.

A curated list of papers that may be of interest to Software Engineering students or professionals. See the sources and selection criteria below.

Von Neumann's First Computer Program. Knuth (1970) . Computer History; Early Programming

• The Education of a Computer. Hopper (1952) .
• Recursive Programming. Dijkstra (1960) .
• Programming Considered as a Human Activity. Dijkstra (1965) .
• Goto Statement Considered Harmful. Dijkstra (1968) .
• Program development by stepwise refinement. Wirth (1971) .
• The Humble Programmer. Dijkstra (1972) .
• Computer Programming as an Art. Knuth (1974) .
• The paradigms of programming. Floyd (1979) .
• Literate Programming. Knuth (1984) .

Computing Machinery and Intelligence. Turing (1950) . Early Artificial Intelligence

• Some Moral and Technical Consequences of Automation. Wiener (1960) .
• Steps towards Artificial Intelligence. Minsky (1960) .
• ELIZA—a computer program for the study of natural language communication between man and machine. Weizenbaum (1966) .
• A Theory of the Learnable. Valiant (1984) .

A Method for the Construction of Minimum-Redundancy Codes. Huffman (1952) . Information Theory

• A Universal Algorithm for Sequential Data Compression. Ziv, Lempel (1977) .
• Fifty Years of Shannon Theory. Verdú (1998) .

Engineering a Sort Function. Bentley, McIlroy (1993) . Data Structures; Algorithms

• On the Shortest Spanning Subtree of a Graph and the Traveling Salesman Problem. Kruskal (1956) .
• A Note on Two Problems in Connexion with Graphs. Dijkstra (1959) .
• Quicksort. Hoare (1962) .
• Space/Time Trade-offs in Hash Coding with Allowable Errors. Bloom (1970) .
• The Ubiquitous B-Tree. Comer (1979) .
• Programming pearls: Algorithm design techniques. Bentley (1984) .
• Programming pearls: The back of the envelope. Bentley (1984) .
• Making data structures persistent. Driscoll et al (1986) .

A Design Methodology for Reliable Software Systems. Liskov (1972) . Software Design

• On the Criteria To Be Used in Decomposing Systems into Modules. Parnas (1971) .
• Information Distribution Aspects of Design Methodology. Parnas (1972) .
• Designing Software for Ease of Extension and Contraction. Parnas (1979) .
• Programming as Theory Building. Naur (1985) .
• Software Aging. Parnas (1994) .
• Towards a Theory of Conceptual Design for Software. Jackson (2015) .

Programming with Abstract Data Types. Liskov, Zilles (1974) . Abstract Data Types; Object-Oriented Programming

• The Smalltalk-76 Programming System Design and Implementation. Ingalls (1978) .
• A Theory of Type Polymorphism in Programming. Milner (1978) .
• On understanding types, data abstraction, and polymorphism. Cardelli, Wegner (1985) .
• SELF: The Power of Simplicity. Ungar, Smith (1991) .

Why Functional Programming Matters. Hughes (1990) . Functional Programming

• Recursive Functions of Symbolic Expressions and Their Computation by Machine. McCarthy (1960) .
• The Semantics of Predicate Logic as a Programming Language. Van Emden, Kowalski (1976) .
• Can Programming Be Liberated from the von Neumann Style? Backus (1978) .
• The Semantic Elegance of Applicative Languages. Turner (1981) .
• The essence of functional programming. Wadler (1992) .
• QuickCheck: A Lightweight Tool for Random Testing of Haskell Programs. Claessen, Hughes (2000) .
• Church's Thesis and Functional Programming. Turner (2006) .

An Incremental Approach to Compiler Construction. Ghuloum (2006) . Language Design; Compilers

• The Next 700 Programming Languages. Landin (1966) .
• Programming pearls: little languages. Bentley (1986) .
• The Essence of Compiling with Continuations. Flanagan et al (1993) .
• A Brief History of Just-In-Time. Aycock (2003) .
• LLVM: A Compilation Framework for Lifelong Program Analysis & Transformation. Lattner, Adve (2004) .
• A Unified Theory of Garbage Collection. Bacon, Cheng, Rajan (2004) .
• A Nanopass Framework for Compiler Education. Sarkar, Waddell, Dybvig (2005) .
• Bringing the Web up to Speed with WebAssembly. Haas (2017) .

No Silver Bullet: Essence and Accidents of Software Engineering. Brooks (1987) . Software Engineering; Project Management

• How do committees invent? Conway (1968) .
• Managing the Development of Large Software Systems. Royce (1970) .
• The Mythical Man Month. Brooks (1975) .
• On Building Systems That Will Fail. Corbató (1991) .
• The Cathedral and the Bazaar. Raymond (1998) .
• Out of the Tar Pit. Moseley, Marks (2006) .

Communicating sequential processes. Hoare (1978) . Concurrency

• Solution Of a Problem in Concurrent Program Control. Dijkstra (1965) .
• Monitors: An operating system structuring concept. Hoare (1974) .
• On the Duality of Operating System Structures. Lauer, Needham (1978) .
• Software Transactional Memory. Shavit, Touitou (1997) .

The UNIX Time- Sharing System. Ritchie, Thompson (1974) . Operating Systems

• An Experimental Time-Sharing System. Corbató, Merwin Daggett, Daley (1962) .
• The Structure of the "THE"-Multiprogramming System. Dijkstra (1968) .
• The nucleus of a multiprogramming system. Hansen (1970) .
• Reflections on Trusting Trust. Thompson (1984) .
• The Design and Implementation of a Log-Structured File System. Rosenblum, Ousterhout (1991) .

A Relational Model of Data for Large Shared Data Banks. Codd (1970) . Databases

• Granularity of Locks and Degrees of Consistency in a Shared Data Base. Gray et al (1975) .
• Access Path Selection in a Relational Database Management System. Selinger et al (1979) .
• The Transaction Concept: Virtues and Limitations. Gray (1981) .
• The design of POSTGRES. Stonebraker, Rowe (1986) .
• Rules of Thumb in Data Engineering. Gray, Shenay (1999) .

A Protocol for Packet Network Intercommunication. Cerf, Kahn (1974) . Networking

• Ethernet: Distributed packet switching for local computer networks. Metcalfe, Boggs (1978) .
• End-To-End Arguments in System Design. Saltzer, Reed, Clark (1984) .
• An algorithm for distributed computation of a Spanning Tree in an Extended LAN. Perlman (1985) .
• The Design Philosophy of the DARPA Internet Protocols. Clark (1988) .
• TOR: The second generation onion router. Dingledine et al (2004) .
• Why the Internet only just works. Handley (2006) .
• The Network is Reliable. Bailis, Kingsbury (2014) .

New Directions in Cryptography. Diffie, Hellman (1976) . Cryptography

• A Method for Obtaining Digital Signatures and Public-Key Cryptosystems. Rivest, Shamir, Adleman (1978) .
• How To Share A Secret. Shamir (1979) .
• A Digital Signature Based on a Conventional Encryption Function. Merkle (1987) .
• The Salsa20 family of stream ciphers. Bernstein (2007) .

Time, Clocks, and the Ordering of Events in a Distributed System. Lamport (1978) . Distributed Systems

• Self-stabilizing systems in spite of distributed control. Dijkstra (1974) .
• The Byzantine Generals Problem. Lamport, Shostak, Pease (1982) .
• Impossibility of Distributed Consensus With One Faulty Process. Fisher, Lynch, Patterson (1985) .
• Implementing Fault-Tolerant Services Using the State Machine Approach: A Tutorial. Schneider (1990) .
• Practical Byzantine Fault Tolerance. Castro, Liskov (1999) .
• Paxos made simple. Lamport (2001) .
• Paxos made live - An Engineering Perspective. Chandra, Griesemer, Redstone (2007) .
• In Search of an Understandable Consensus Algorithm. Ongaro, Ousterhout (2014) .

Designing for Usability: Key Principles and What Designers Think. Gould, Lewis (1985) . Human-Computer Interaction; User Interfaces

• As We May Think. Bush (1945) .
• Man-Computer symbiosis. Licklider (1958) .
• Some Thoughts About the Social Implications of Accessible Computing. David, Fano (1965) .
• Tutorials for the First-Time Computer User. Al-Awar, Chapanis, Ford (1981) .
• The star user interface: an overview. Smith, Irby, Kimball (1982) .
• Design Principles for Human-Computer Interfaces. Norman (1983) .
• Human-Computer Interaction: Psychology as a Science of Design. Carroll (1997) .

The anatomy of a large-scale hypertextual Web search engine. Brin, Page (1998) . Information Retrieval; World-Wide Web

• A Statistical Interpretation of Term Specificity in Retrieval. Spärck Jones (1972) .
• World-Wide Web: Information Universe. Berners-Lee et al (1992) .
• The PageRank Citation Ranking: Bringing Order to the Web. Page, Brin, Motwani (1998) .

Dynamo, Amazon’s Highly Available Key-value store. DeCandia et al (2007) . Internet Scale Data Systems

• The Google File System. Ghemawat, Gobioff, Leung (2003) .
• MapReduce: Simplified Data Processing on Large Clusters. Dean, Ghemawat (2004) .
• Bigtable: A Distributed Storage System for Structured Data. Chang et al (2006) .
• ZooKeeper: wait-free coordination for internet scale systems. Hunt et al (2010) .
• The Hadoop Distributed File System. Shvachko et al (2010) .
• Kafka: a Distributed Messaging System for Log Processing. Kreps, Narkhede, Rao (2011) .
• CAP Twelve Years Later: How the "Rules" Have Changed. Brewer (2012) .
• Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases. Verbitski et al (2017) .

On Designing and Deploying Internet Scale Services. Hamilton (2007) . Operations; Reliability; Fault-tolerance

• Ironies of Automation. Bainbridge (1983) .
• Why do computers stop and what can be done about it? Gray (1985) .
• Recovery Oriented Computing (ROC): Motivation, Definition, Techniques, and Case Studies. Patterson et al (2002) .
• Crash-Only Software. Candea, Fox (2003) .
• Building on Quicksand. Helland, Campbell (2009) .

Thinking Methodically about Performance. Gregg (2012) . Performance

• Performance Anti-Patterns. Smaalders (2006) .
• Thinking Clearly about Performance. Millsap (2010) .

Bitcoin, A peer-to-peer electronic cash system. Nakamoto (2008) . Decentralized Distributed Systems; Peer-to-peer systems

• Operational transformation in real-time group editors: issues, algorithms, and achievements. Sun, Ellis (1998) .
• Kademlia: A Peer-to-Peer Information System Based on the XOR Metric. Maymounkov, Mazières (2002) .
• Incentives Build Robustness in BitTorrent. Cohen (2003) .
• Conflict-free Replicated Data Types. Shapiro et al (2011) .
• IPFS - Content Addressed, Versioned, P2P File System. Benet (2014) .
• Ethereum: A Next-Generation Smart Contract and Decentralized Application Platform. Buterin (2014) .
• Local-First Software: You Own Your Data, in spite of the Cloud. Kleppmann et al (2019) .

A Few Useful Things to Know About Machine Learning. Domingos (2012) . Machine Learning

• Statistical Modeling: The Two Cultures. Breiman (2001) .
• The Unreasonable Effectiveness of Data. Halevy, Norvig, Pereira (2009) .
• ImageNet Classification with Deep Convolutional Neural Networks. Krizhevsky, Sutskever, Hinton (2012) .
• Playing Atari with Deep Reinforcement Learning. Mnih et al (2013) .
• Generative Adversarial Nets. Goodfellow et al (2014) .
• Deep Learning. LeCun, Bengio, Hinton (2015) .
• Attention Is All You Need. Vaswani et al (2017) .
• Von Neumann's First Computer Program. Knuth (1970) .
• Computing Machinery and Intelligence. Turing (1950) .
• A Method for the Construction of Minimum-Redundancy Codes. Huffman (1952) .
• Engineering a Sort Function. Bentley, McIlroy (1993) .
• A Design Methodology for Reliable Software Systems. Liskov (1972) .
• Programming with Abstract Data Types. Liskov, Zilles (1974) .
• Why Functional Programming Matters. Hughes (1990) .
• An Incremental Approach to Compiler Construction. Ghuloum (2006) .
• No Silver Bullet: Essence and Accidents of Software Engineering. Brooks (1987) .
• Communicating sequential processes. Hoare (1978) .
• The UNIX Time- Sharing System. Ritchie, Thompson (1974) .
• A Relational Model of Data for Large Shared Data Banks. Codd (1970) .
• A Protocol for Packet Network Intercommunication. Cerf, Kahn (1974) .
• New Directions in Cryptography. Diffie, Hellman (1976) .
• Time, Clocks, and the Ordering of Events in a Distributed System. Lamport (1978) .
• Designing for Usability: Key Principles and What Designers Think. Gould, Lewis (1985) .
• The anatomy of a large-scale hypertextual Web search engine. Brin, Page (1998) .
• Dynamo, Amazon’s Highly Available Key-value store. DeCandia et al (2007) .
• On Designing and Deploying Internet Scale Services. Hamilton (2007) .
• Thinking Methodically about Performance. Gregg (2012) .
• Bitcoin, A peer-to-peer electronic cash system. Nakamoto (2008) .
• A Few Useful Things to Know About Machine Learning. Domingos (2012) .

This list was inspired by (and draws from) several books and paper collections:

• Papers We Love
• Ideas That Created the Future
• The Innovators
• The morning paper
• Distributed systems for fun and profit
• Readings in Database Systems (the Red Book)
• Fermat's Library
• Classics in Human-Computer Interaction
• Awesome Compilers
• Distributed Consensus Reading List
• The Decade of Deep Learning

A few interesting resources about reading papers from Papers We Love and elsewhere:

• Should I read papers?
• How to Read an Academic Article
• How to Read a Paper. Keshav (2007) .
• Efficient Reading of Papers in Science and Technology. Hanson (1999) .
• On ICSE’s “Most Influential Papers”. Parnas (1995) .

Selection criteria

• The idea is not to include every interesting paper that I come across but rather to keep a representative list that's possible to read from start to finish with a similar level of effort as reading a technical book from cover to cover.
• I tried to include one paper per each major topic and author. Since in the process I found a lot of noteworthy alternatives, related or follow-up papers and I wanted to keep track of those as well, I included them as sublist items.
• The papers shouldn't be too long. For the same reasons as the previous item, I try to avoid papers longer than 20 or 30 pages.
• They should be self-contained and readable enough to be approachable by the casual technical reader.
• They should be freely available online.
• Examples of this are classic works by Von Neumann, Turing and Shannon.
• That being said, where possible I preferred the original paper on each subject over modern updates or survey papers.
• Similarly, I tended to skip more theoretical papers, those focusing on mathematical foundations for Computer Science, electronic aspects of hardware, etc.
• I sorted the list by a mix of relatedness of topics and a vague chronological relevance, such that it makes sense to read it in the suggested order. For example, historical and seminal topics go first, contemporary internet-era developments last, networking precedes distributed systems, etc.

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Top 10 Software Engineer Research Topics for 2024

Home Blog Programming Top 10 Software Engineer Research Topics for 2024

Software engineering, in general, is a dynamic and rapidly changing field that demands a thorough understanding of concepts related to programming, computer science, and mathematics. As software systems become more complicated in the future, software developers must stay updated on industry innovations and the latest trends. Working on software engineering research topics is an important part of staying relevant in the field of software engineering. 

Software engineers can do research to learn about new technologies, approaches, and strategies for developing and maintaining complex software systems. Software engineers can conduct research on a wide range of topics. Software engineering research is also vital for increasing the functionality, security, and dependability of software systems. Going for the Top Software Engineering Certification course contributes to the advancement of the field's state of the art and assures that software engineers can continue to build high-quality, effective software systems.

What are Software Engineer Research Topics?

Software engineer research topics are areas of exploration and study in the rapidly evolving field of software engineering. These research topics include various software development approaches, quality of software, testing of software, maintenance of software, security measures for software, machine learning models in software engineering, DevOps, and architecture of software. Each of these software engineer research topics has distinct problems and opportunities for software engineers to investigate and make major contributions to the field. In short, research topics for software engineering provide possibilities for software engineers to investigate new technologies, approaches, and strategies for developing and managing complex software systems. 

For example, research on agile software development could identify the benefits and drawbacks of using agile methodology, as well as develop new techniques for effectively implementing agile practices. Software testing research may explore new testing procedures and tools, as well as assess the efficacy of existing ones. Software quality research may investigate the elements that influence software quality and develop approaches for enhancing software system quality and minimizing the faults and errors. Software metrics are quantitative measures that are used to assess the quality, maintainability, and performance of software. 

The research papers on software engineering topics in this specific area could identify novel measures for evaluating software systems or techniques for using metrics to improve the quality of software. The practice of integrating code changes into a common repository and pushing code changes to production in small, periodic batches is known as continuous integration and deployment (CI/CD). This research could investigate the best practices for establishing CI/CD or developing tools and approaches for automating the entire CI/CD process.

List of Software Engineer Research Topics in 2024

Here is a list of Software Engineer research topics:

• Artificial Intelligence and Software Engineering
• Natural Language Processing 
• Applications of Data Mining in Software Engineering
• Data Modeling
• Verification and Validation
• Software Project Management
• Software Quality
• Software Models

Top 10 Software Engineer Research Topics

Let's discuss the top Software Engineer Research Topics in a detailed way:

1. Artificial Intelligence and Software Engineering

a. Intersections between AI and SE

The creation of AI-powered software engineering tools is one potential research area at the intersection of artificial intelligence (AI) and software engineering. These technologies use AI techniques that include machine learning, natural language processing, and computer vision to help software engineers with a variety of tasks throughout the software development lifecycle. An AI-powered code review tool, for example, may automatically discover potential flaws or security vulnerabilities in code, saving developers a lot of time and lowering the chance of human error. Similarly, an AI-powered testing tool might build test cases and analyze test results automatically to discover areas for improvement. 

Furthermore, AI-powered project management tools may aid in the planning and scheduling of projects, resource allocation, and risk management in the project. AI can also be utilized in software maintenance duties such as automatically discovering and correcting defects or providing code refactoring solutions. However, the development of such tools presents significant technical and ethical challenges, such as the necessity of large amounts of high-quality data, the risk of bias present in AI algorithms, and the possibility of AI replacing human jobs. Continuous study in this area is therefore required to ensure that AI-powered software engineering tools are successful, fair, and responsible.

b. Knowledge-based Software Engineering

Another study area that overlaps with AI and software engineering is knowledge-based software engineering (KBSE). KBSE entails creating software systems capable of reasoning about knowledge and applying that knowledge to enhance software development processes. The development of knowledge-based systems that can help software engineers in detecting and addressing complicated problems is one example of KBSE in action. To capture domain-specific knowledge, these systems use knowledge representation techniques such as ontologies, and reasoning algorithms such as logic programming or rule-based systems to derive new knowledge from already existing data. 

KBSE can be utilized in the context of AI and software engineering to create intelligent systems capable of learning from past experiences and applying that information to improvise future software development processes. A KBSE system, for example, may be used to generate code based on previous code samples or to recommend code snippets depending on the requirements of a project. Furthermore, KBSE systems could be used to improve the precision and efficiency of software testing and debugging by identifying and prioritizing bugs using knowledge-based techniques. As a result, continued research in this area is critical to ensuring that AI-powered software engineering tools are productive, fair, and responsible.

2. Natural Language Processing

a. Multimodality

Multimodality in Natural Language Processing (NLP) is one of the appealing research ideas for software engineering at the nexus of computer vision, speech recognition, and NLP. The ability of machines to comprehend and generate language from many modalities, such as text, speech, pictures, and video, is referred to as multimodal NLP. The goal of multimodal NLP is to develop systems that can learn from and interpret human communication across several modalities, allowing them to engage with humans in more organic and intuitive ways. 

The building of conversational agents or chatbots that can understand and create responses using several modalities is one example of multimodal NLP in action. These agents can analyze text input, voice input, and visual clues to provide more precise and relevant responses, allowing users to have a more natural and seamless conversational experience. Furthermore, multimodal NLP can be used to enhance language translation systems, allowing them to more accurately and effectively translate text, speech, and visual content.

b. Efficiency

The development of multimodal NLP systems must take efficiency into account. as multimodal NLP systems require significant computing power to process and integrate information from multiple modalities, optimizing their efficiency is critical to ensuring that they can operate in real-time and provide users with accurate and timely responses. Developing algorithms that can efficiently evaluate and integrate input from several modalities is one method for improving the efficiency of multimodal NLP systems. 

Overall, efficiency is a critical factor in the design of multimodal NLP systems. Researchers can increase the speed, precision, and scalability of these systems by inventing efficient algorithms, pre-processing approaches, and hardware architectures, allowing them to run successfully and offer real-time replies to consumers. Software Engineering training will help you level up your career and gear up to land you a job in the top product companies as a skilled Software Engineer. 

3. Applications of Data Mining in Software Engineering

a. Mining Software Engineering Data

The mining of software engineering data is one of the significant research paper topics for software engineering, involving the application of data mining techniques to extract insights from enormous datasets that are generated during software development processes. The purpose of mining software engineering data is to uncover patterns, trends, and various relationships that can inform software development practices, increase software product quality, and improve software development process efficiency. 

Mining software engineering data, despite its potential benefits, has various obstacles, including the quality of data, scalability, and privacy of data. Continuous research in this area is required to develop more effective data mining techniques and tools, as well as methods for ensuring data privacy and security, to address these challenges. By tackling these issues, mining software engineering data can continue to promote many positive aspects in software development practices and the overall quality of product.

b. Clustering and Text Mining

Clustering is a data mining approach that is used to group comparable items or data points based on their features or characteristics. Clustering can be used to detect patterns and correlations between different components of software, such as classes, methods, and modules, in the context of software engineering data. 

On the other hand, text mining is a method of data mining that is used to extract valuable information from unstructured text data such as software manuals, code comments, and bug reports. Text mining can be applied in the context of software engineering data to find patterns and trends in software development processes

4. Data Modeling

Data modeling is an important area of research paper topics in software engineering study, especially in the context of the design of databases and their management. It involves developing a conceptual model of the data that a system will need to store, organize, and manage, as well as establishing the relationships between various data pieces. One important goal of data modeling in software engineering research is to make sure that the database schema precisely matches the system's and its users' requirements. Working closely with stakeholders to understand their needs and identify the data items that are most essential to them is necessary.

5. Verification and Validation

Verification and validation are significant research project ideas for software engineering research because they help us to ensure that software systems are correctly built and suit the needs of their users. While most of the time, these terms are frequently used interchangeably, they refer to distinct stages of the software development process. The process of ensuring that a software system fits its specifications and needs is referred to as verification. This involves testing the system to confirm that it behaves as planned and satisfies the functional and performance specifications. In contrast, validation is the process of ensuring that a software system fulfils the needs of its users and stakeholders. 

This includes ensuring that the system serves its intended function and meets the requirements of its users. Verification and validation are key components of the software development process in software engineering research. Researchers can help to improve the functionality and dependability of software systems, minimize the chance of faults and mistakes, and ultimately develop better software products for their consumers by verifying that software systems are designed correctly and that they satisfy the needs of their users.

6. Software Project Management

Software project management is an important component of software engineering research because it comprises the planning, organization, and control of resources and activities to guarantee that software projects are finished on time, within budget, and to the needed quality standards. One of the key purposes of software project management in research is to guarantee that the project's stakeholders, such as users, clients, and sponsors, are satisfied with their needs. This includes defining the project's requirements, scope, and goals, as well as identifying potential risks and restrictions to the project's success.

7. Software Quality

The quality of a software product is defined as how well it fits in with its criteria, how well it performs its intended functions, and meets the needs of its consumers. It includes features such as dependability, usability, maintainability, effectiveness, and security, among others. Software quality is a prominent and essential research topic in software engineering. Researchers are working to provide methodologies, strategies, and tools for evaluating and improving software quality, as well as forecasting and preventing software faults and defects. Overall, software quality research is a large and interdisciplinary field that combines computer science, engineering, and statistics. Its mission is to increase the reliability, accessibility, and overall quality of software products and systems, thereby benefiting both software developers and end consumers.

8. Ontology

Ontology is a formal specification of a conception of a domain used in computer science to allow knowledge sharing and reuse. Ontology is a popular and essential area of study in the context of software engineering research. The construction of ontologies for specific domains or application areas could be a research topic in ontology for software engineering. For example, a researcher may create an ontology for the field of e-commerce to give common knowledge and terminology to software developers as well as stakeholders in that domain. The integration of several ontologies is another intriguing study topic in ontology for software engineering. As the number of ontologies generated for various domains and applications grows, there is an increasing need to integrate them in order to enable interoperability and reuse.

9. Software Models

In general, a software model acts as an abstract representation of a software system or its components. Software models can be used to help software developers, different stakeholders, and users communicate more effectively, as well as to properly evaluate, design, test, and maintain software systems. The development and evaluation of modeling languages and notations is one research example connected to software models. Researchers, for example, may evaluate the usefulness and efficiency of various modeling languages, such as UML or BPMN, for various software development activities or domains. 

Researchers could also look into using software models for software testing and verification. They may investigate how models might be used to produce test cases or to do model checking, a formal technique for ensuring the correctness of software systems. They may also examine the use of models for monitoring at runtime and software system adaptation.

The Software Development Life Cycle (SDLC) is a software engineering process for planning, designing, developing, testing, and deploying software systems. SDLC is an important research issue in software engineering since it is used to manage software projects and ensure the quality of the resultant software products by software developers and project managers. The development and evaluation of novel software development processes is one SDLC-related research topic. SDLC research also includes the creation and evaluation of different software project management tools and practices. 

Researchers may also check the implementation of SDLC in specific sectors or applications. They may, for example, investigate the use of SDLC in the development of systems that are more safety-critical, such as medical equipment or aviation systems, and develop new processes or tools to ensure the safety and reliability of these systems. They may also look into using SDLC to design software systems in new sectors like the Internet of Things or in blockchain technology.

Why is Software Engineering Required?

Software engineering is necessary because it gives a systematic way to developing, designing, and maintaining reliable, efficient, and scalable software. As software systems have become more complicated over time, software engineering has become a vital discipline to ensure that software is produced in a way that is fully compatible with end-user needs, reliable, and long-term maintainable.

When the cost of software development is considered, software engineering becomes even more important. Without a disciplined strategy, developing software can result in overinflated costs, delays, and a higher probability of errors that require costly adjustments later. Furthermore, software engineering can help reduce the long-term maintenance costs that occur by ensuring that software is designed to be easy to maintain and modify. This can save money in the long run by lowering the number of resources and time needed to make software changes as needed.

2. Scalability

Scalability is an essential factor in software development, especially for programs that have to manage enormous amounts of data or an increasing number of users. Software engineering provides a foundation for creating scalable software that can evolve over time. The capacity to deploy software to diverse contexts, such as cloud-based platforms or distributed systems, is another facet of scalability. Software engineering can assist in ensuring that software is built to be readily deployed and adjusted for various environments, resulting in increased flexibility and scalability.

3. Large Software

Developers can break down huge software systems into smaller, simpler parts using software engineering concepts, making the whole system easier to maintain. This can help to reduce the software's complexity and makes it easier to maintain the system over time. Furthermore, software engineering can aid in the development of large software systems in a modular fashion, with each module doing a specific function or set of functions. This makes it easier to push new features or functionality to the product without causing disruptions to the existing codebase.

4. Dynamic Nature

Developers can utilize software engineering techniques to create dynamic content that is modular and easily modifiable when user requirements change. This can enable adding new features or functionality to dynamic content easier without disturbing the existing codebase. Another factor to consider for dynamic content is security. Software engineering can assist in ensuring that dynamic content is generated in a secure manner that protects user data and information.

5. Better Quality Management

An organized method of quality management in software development is provided by software engineering. Developers may ensure that software is conceived, produced, and maintained in a way that fulfills quality requirements and provides value to users by adhering to software engineering principles. Requirement management is one component of quality management in software engineering. Testing and validation are another part of quality control in software engineering. Developers may verify that their software satisfies its requirements and is error-free by using an organized approach to testing.

In conclusion, the subject of software engineering provides a diverse set of research topics with the ability to progress the discipline while enhancing software development and maintenance procedures. This article has dived deep into various research topics in software engineering for masters and research topics for software engineering students such as software testing and validation, software security, artificial intelligence, Natural Language Processing, software project management, machine learning, Data Mining, etc. as research subjects. Software engineering researchers have an interesting chance to explore these and other research subjects and contribute to the development of creative solutions that can improve software quality, dependability, security, and scalability. 

Researchers may make important contributions to the area of software engineering and help tackle some of the most serious difficulties confronting software development and maintenance by staying updated with the latest research trends and technologies. As software grows more important in business and daily life, there is a greater demand for current research topics in software engineering into new software engineering processes and techniques. Software engineering researchers can assist in shaping the future of software creation and maintenance through their research, ensuring that software stays dependable, safe, reliable and efficient in an ever-changing technological context. KnowledgeHut’s top Programming certification course will help you leverage online programming courses from expert trainers.

Frequently Asked Questions (FAQs)

 To find a research topic in software engineering, you can review recent papers and conference proceedings, talk to different experts in the field, and evaluate your own interests and experience. You can use a combination of these approaches. 

You should study software development processes, various programming languages and their frameworks, software testing and quality assurance, software architecture, various design patterns that are currently being used, and software project management as a software engineering student. 

Empirical research, experimental research, surveys, case studies, and literature reviews are all types of research in software engineering. Each sort of study has advantages and disadvantages, and the research method chosen is determined by the research objective, resources, and available data. 

Eshaan Pandey

Eshaan is a Full Stack web developer skilled in MERN stack. He is a quick learner and has the ability to adapt quickly with respect to projects and technologies assigned to him. He has also worked previously on UI/UX web projects and delivered successfully. Eshaan has worked as an SDE Intern at Frazor for a span of 2 months. He has also worked as a Technical Blog Writer at KnowledgeHut upGrad writing articles on various technical topics.

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Google cloud tightens seal on air-gapped software.

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(GERMANY OUT) Frisch eingekochte Pfirsiche in Einmachgläsern mit Bügel. Einkochen ist eine Methode, ... [+] Lebensmittel durch Erhitzen und Luftabschluss haltbar zu machen. Für das Einkochen werden auch die Begriffe Einmachen, Eindünsten oder Einwecken verwendet (Photo by JOKER / Erich Haefele/ullstein bild via Getty Images)

Software breathes. Modern connected software applications breathe on a diet of connectivity and data ingestion that provides them with enough lifeblood to perform the functions they were designed for. Because our software needs to be able to work across network connections between cloud platforms and because it also needs to be able to grasp application programming interfaces that bring in additional services, we tend to keep the window open (with security bars where needed) so that software gets a good supply of oxygenated data.

However, not all cloud computing environments are equal.

There are occasions where we need to operate restricted cut-off computing instances capable of working in super-secure or remote harsh environments. Sometimes still continuously connected to the rest of the world, sometimes only occasionally connected… and sometimes not connected at all, the notion of air-gapped software relates to machines that are isolated away from other computers, networks, wireless or wired connectivity channels or the web. We might find deployments of this kind in disaster zones, polar research stations or resource depots designed to serve the long-haul trucking industry and so on.

Even More Hermetically Sealed

Newly launched this year as an even more hermetically sealed version of its datacenter-based technology of similar DNA, the Google Distributed Cloud air-gapped appliance is a physical device roughly 100-pounds in weight that can be carried by two people. Composed of both hardware and software and designed for ruggedized treatment, this technology is designed to facilitate real-time local data processing for AI use cases such as medical imaging analysis, equipment maintenance analysis, smart city applications that feature object detection and more.

“Google Distributed Cloud air-gapped is a disconnected private cloud solution that enables customers to deploy traditional virtual machine workloads, container workloads and consume Google-managed services like deep learning containers. Google Distributed Cloud (GDC) air-gapped appliance is a human-portable device that has [Google] Distributed Cloud [management software] deployed on it, for use outside of datacenters, such as in the field or in other remote sites,” explains Google Cloud , in a technical briefing document.

Google Cloud says that this air-gapped appliance provides a specific computing environment that can be used to coordinate relief efforts for disaster-affected areas, facilitate fleet management and provide support for restricted workloads such as those in government or defense.

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Best 5% interest savings accounts of 2024, sovereign cloud in a box.

The company refers to this product development as a “sovereign cloud in a box” today i.e. the cloud and box part of that statement being fairly logical, the sovereign reference meaning a cloud that adheres to the laws and regulations of a particular country, which in this instance could be any location on Earth (or indeed the universe) as the cloud here is completely self-contained.

Inside the box customers get Infrastructure-as-a-Service features such as virtual machine compute capacity as well as native support for containers. As well as storage and networking capabilities, there are also Google Cloud services spanning data transfer and data analytics. Very much positioned as a development of the age of intelligence, Google Cloud says that AI capabilities are integrated into air-gapped software so that customers can access features such as a translation interface and optical character recognition from critical and disconnected environments.

Because certification is so important in this arena, Google Cloud points out that its air-gapped appliance has achieved US Department of Defense (DoD) Impact Level 5 (IL5) accreditation, the highest level of security controls and protection required for unclassified, but sensitive information. Further, the GDC air-gapped appliance is a rugged and portable design that meets stringent accreditation requirements like MIL-STD-810H, ensuring reliable operation even in challenging scenarios.

Denied, Degraded, Intermittent & Latent

According to Sachin Gupta, VP & GM of infrastructure at Google Cloud, “Google Distributed Cloud products are designed to provide advanced AI, data and security capabilities wherever customers need them... and [provide them] in the way they want to consume them. The new air-gapped appliance is an innovative, ruggedized, hardware and software bundled solution specifically created for tactical edge or denied, degraded, intermittent and latent environments. It creates an isolated sovereign cloud in a box by being physically disconnected from the Internet. This appliance enables customers who must manage more sensitive applications and comply with strict sovereignty, residency and latency requirements.

Gupta continued, stating that, “The biggest differentiator with this offering is its advanced, built-in AI capabilities, including pre-trained Vertex APIs and Nvidia GPUs, which enable customers to leverage the power of Google Cloud’s AI technology and enhance the performance of mission-critical applications at the edge."

Google Distributed Cloud air-gapped appliance is designed to operate without any connectivity to Google Cloud or (as Gupta has noted) the public Internet itself. The appliance remains fully functional in disconnected environments, preserving the security and isolation of the infrastructure, services and the application programming interfaces that it manages.

The air-gapped appliance consists of a chassis that holds three blades and a computing switch. Users need to supply their own laptop computer to use as an admin workstation for installing the software and performing upgrades.

Google Cloud On The Moon?

If we do start using our moon as a base for the human race to set up camp, this would (in theory) be the sort of computing appliance we might think about deploying. Google hasn’t commented on its product’s suitability for interstellar or celestial deployment, but we can assume that update will come next.

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How to cite ChatGPT

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We, the APA Style team, are not robots. We can all pass a CAPTCHA test , and we know our roles in a Turing test . And, like so many nonrobot human beings this year, we’ve spent a fair amount of time reading, learning, and thinking about issues related to large language models, artificial intelligence (AI), AI-generated text, and specifically ChatGPT . We’ve also been gathering opinions and feedback about the use and citation of ChatGPT. Thank you to everyone who has contributed and shared ideas, opinions, research, and feedback.

In this post, I discuss situations where students and researchers use ChatGPT to create text and to facilitate their research, not to write the full text of their paper or manuscript. We know instructors have differing opinions about how or even whether students should use ChatGPT, and we’ll be continuing to collect feedback about instructor and student questions. As always, defer to instructor guidelines when writing student papers. For more about guidelines and policies about student and author use of ChatGPT, see the last section of this post.

Quoting or reproducing the text created by ChatGPT in your paper

If you’ve used ChatGPT or other AI tools in your research, describe how you used the tool in your Method section or in a comparable section of your paper. For literature reviews or other types of essays or response or reaction papers, you might describe how you used the tool in your introduction. In your text, provide the prompt you used and then any portion of the relevant text that was generated in response.

Unfortunately, the results of a ChatGPT “chat” are not retrievable by other readers, and although nonretrievable data or quotations in APA Style papers are usually cited as personal communications , with ChatGPT-generated text there is no person communicating. Quoting ChatGPT’s text from a chat session is therefore more like sharing an algorithm’s output; thus, credit the author of the algorithm with a reference list entry and the corresponding in-text citation.

When prompted with “Is the left brain right brain divide real or a metaphor?” the ChatGPT-generated text indicated that although the two brain hemispheres are somewhat specialized, “the notation that people can be characterized as ‘left-brained’ or ‘right-brained’ is considered to be an oversimplification and a popular myth” (OpenAI, 2023).

OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

You may also put the full text of long responses from ChatGPT in an appendix of your paper or in online supplemental materials, so readers have access to the exact text that was generated. It is particularly important to document the exact text created because ChatGPT will generate a unique response in each chat session, even if given the same prompt. If you create appendices or supplemental materials, remember that each should be called out at least once in the body of your APA Style paper.

When given a follow-up prompt of “What is a more accurate representation?” the ChatGPT-generated text indicated that “different brain regions work together to support various cognitive processes” and “the functional specialization of different regions can change in response to experience and environmental factors” (OpenAI, 2023; see Appendix A for the full transcript).

Creating a reference to ChatGPT or other AI models and software

The in-text citations and references above are adapted from the reference template for software in Section 10.10 of the Publication Manual (American Psychological Association, 2020, Chapter 10). Although here we focus on ChatGPT, because these guidelines are based on the software template, they can be adapted to note the use of other large language models (e.g., Bard), algorithms, and similar software.

The reference and in-text citations for ChatGPT are formatted as follows:

• Parenthetical citation: (OpenAI, 2023)
• Narrative citation: OpenAI (2023)

Let’s break that reference down and look at the four elements (author, date, title, and source):

Author: The author of the model is OpenAI.

Date: The date is the year of the version you used. Following the template in Section 10.10, you need to include only the year, not the exact date. The version number provides the specific date information a reader might need.

Title: The name of the model is “ChatGPT,” so that serves as the title and is italicized in your reference, as shown in the template. Although OpenAI labels unique iterations (i.e., ChatGPT-3, ChatGPT-4), they are using “ChatGPT” as the general name of the model, with updates identified with version numbers.

The version number is included after the title in parentheses. The format for the version number in ChatGPT references includes the date because that is how OpenAI is labeling the versions. Different large language models or software might use different version numbering; use the version number in the format the author or publisher provides, which may be a numbering system (e.g., Version 2.0) or other methods.

Bracketed text is used in references for additional descriptions when they are needed to help a reader understand what’s being cited. References for a number of common sources, such as journal articles and books, do not include bracketed descriptions, but things outside of the typical peer-reviewed system often do. In the case of a reference for ChatGPT, provide the descriptor “Large language model” in square brackets. OpenAI describes ChatGPT-4 as a “large multimodal model,” so that description may be provided instead if you are using ChatGPT-4. Later versions and software or models from other companies may need different descriptions, based on how the publishers describe the model. The goal of the bracketed text is to briefly describe the kind of model to your reader.

Source: When the publisher name and the author name are the same, do not repeat the publisher name in the source element of the reference, and move directly to the URL. This is the case for ChatGPT. The URL for ChatGPT is https://chat.openai.com/chat . For other models or products for which you may create a reference, use the URL that links as directly as possible to the source (i.e., the page where you can access the model, not the publisher’s homepage).

Other questions about citing ChatGPT

You may have noticed the confidence with which ChatGPT described the ideas of brain lateralization and how the brain operates, without citing any sources. I asked for a list of sources to support those claims and ChatGPT provided five references—four of which I was able to find online. The fifth does not seem to be a real article; the digital object identifier given for that reference belongs to a different article, and I was not able to find any article with the authors, date, title, and source details that ChatGPT provided. Authors using ChatGPT or similar AI tools for research should consider making this scrutiny of the primary sources a standard process. If the sources are real, accurate, and relevant, it may be better to read those original sources to learn from that research and paraphrase or quote from those articles, as applicable, than to use the model’s interpretation of them.

We’ve also received a number of other questions about ChatGPT. Should students be allowed to use it? What guidelines should instructors create for students using AI? Does using AI-generated text constitute plagiarism? Should authors who use ChatGPT credit ChatGPT or OpenAI in their byline? What are the copyright implications ?

On these questions, researchers, editors, instructors, and others are actively debating and creating parameters and guidelines. Many of you have sent us feedback, and we encourage you to continue to do so in the comments below. We will also study the policies and procedures being established by instructors, publishers, and academic institutions, with a goal of creating guidelines that reflect the many real-world applications of AI-generated text.

For questions about manuscript byline credit, plagiarism, and related ChatGPT and AI topics, the APA Style team is seeking the recommendations of APA Journals editors. APA Style guidelines based on those recommendations will be posted on this blog and on the APA Style site later this year.

Update: APA Journals has published policies on the use of generative AI in scholarly materials .

We, the APA Style team humans, appreciate your patience as we navigate these unique challenges and new ways of thinking about how authors, researchers, and students learn, write, and work with new technologies.

American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). https://doi.org/10.1037/0000165-000

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