Transforming Software Development with Generative AI: Empirical Insights on Collaboration and Workflow
Open accessIntegrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Most tasks for which GenAI is being adopted are mundane.
“Indeed, most tasks for which GenAI is being adopted are mundane [127].”
From The Impact of Generative AI on Creativity in Software Development: A Research Agenda (2025) · cites this paper as [127]
Developers may prefer assistance from GenAI over collaboration with colleagues.
“Ulfsnes et al. also noted potential drawbacks, such as developers preferring assistance from GenAI over collaboration with colleagues.”
From Human-AI Collaboration in Software Development: A Mixed-Methods Study of Developers' Use of GitHub Copilot and ChatGPT (2025) · cites this paper as [22]
The use of GenAI tools enhanced the job satisfaction of software developers by minimizing repetitive tasks and allowing more time for fulfilling activities.
“Ulfsnes et al. [22] who observed that the use of GenAI tools enhanced the job satisfaction of software developers by minimizing repetitive tasks and allowing more time for fulfilling activities.”
From Human-AI Collaboration in Software Development: A Mixed-Methods Study of Developers' Use of GitHub Copilot and ChatGPT (2025) · cites this paper as [22]
Cites
(2017) · Cited by 9,643 · see citing works
Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory
Wuetherick, B. (2010) · Canadian Journal of University Continuing Education · doi:10.21225/d5g01t · Cited by 8,327 · see citing works
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · see citing works
Snowball Sampling: Problems and Techniques of Chain Referral Sampling
Biernacki, P., & Waldorf, D. (1981) · Sociological Methods & Research · doi:10.1177/004912418101000205 · Cited by 5,563 · see citing works
Brynjolfsson, E., Li, D., & Raymond, L. (2023) · National Bureau of Economic Research · doi:10.3386/w31161 · Cited by 995 · see citing works
Dell'Acqua, F., McFowland III, E., Mollick, E. R., Lifshitz-Assaf, H., Kellogg, K., Rajendran, S., Krayer, L., Candelon, F., & Lakhani, K. R. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4573321 · Cited by 794 · see citing works
Continuous software engineering: A roadmap and agenda
Fitzgerald, B. F., & Stol, K.-J. (2015) · Journal of Systems and Software · doi:10.1016/j.jss.2015.06.063 · Cited by 742 · see citing works
A teamwork model for understanding an agile team: A case study of a Scrum project
Moe, N. B., Dingsøyr, T., & Dybå, T. (2009) · Information and Software Technology · doi:10.1016/j.infsof.2009.11.004 · Cited by 543 · see citing works
Self-citationUsing task context to improve programmer productivity
Kersten, M., & Murphy, G. C. (2006) · Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering · doi:10.1145/1181775.1181777 · Cited by 452 · see citing works
How are Java software developers using the Eclipse IDE?
Murphy, G. C., Kersten, M., & Findlater, L. (2006) · IEEE Software · doi:10.1109/ms.2006.105 · Cited by 413 · see citing works
Learning from examples to improve code completion systems
Bruch, M., Monperrus, M., & Mezini, M. (2009) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/1595696.1595728 · Cited by 383 · see citing works
On the naturalness of software
Hindle, A., Barr, E. T., Gabel, M., Su, Z., & Devanbu, P. (2016) · Communications of the ACM · doi:10.1145/2902362 · Cited by 307 · see citing works
Ross, S., Martinez, F., Houde, S., Muller, M. J., & Weisz, J. D. (2023) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3581641.3584037 · Cited by 272 · see citing works
Investigating Explainability of Generative AI for Code through Scenario-based Design
Sun, J., Liao, Q. V., Muller, M., Agarwal, M., Houde, S., Talamadupula, K., & Weisz, J. D. (2022) · 27th International Conference on Intelligent User Interfaces · doi:10.1145/3490099.3511119 · Cited by 219 · see citing works
The SPACE of Developer Productivity: There's more to it than you think.
Forsgren, N., Storey, M.-A., Maddila, C., Zimmermann, T., Houck, B., & Butler, J. (2021) · Queue · doi:10.1145/3454122.3454124 · Cited by 164 · see citing works
Taking Flight with Copilot: Early insights and opportunities of AI-powered pair-programming tools
Bird, C., Ford, D., Zimmermann, T., Forsgren, N., Kalliamvakou, E., Lowdermilk, T., & Gazit, I. (2022) · Queue · doi:10.1145/3582083 · Cited by 158 · see citing works
Pythia: AI-assisted Code Completion System
Svyatkovskiy, A., Zhao, Y., Fu, S., & Sundaresan, N. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330699 · Cited by 157 · see citing works
Today was a good day: The daily life of software developers
Meyer, A. N., Barr, E. T., Bird, C., & Zimmermann, T. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2904957 · Cited by 123 · see citing works
What Do We Know about Knowledge Management? Practical Implications for Software Engineering
Dingsøyr, T., Bjørnson, F. O., & Shull, F. J. (2009) · IEEE Software · doi:10.1109/ms.2009.82 · Cited by 59 · see citing works
Chatterjee, P., Kong, M., & Pollock, L. (2019) · Journal of Systems and Software · doi:10.1016/j.jss.2019.110454 · Cited by 51 · see citing works
Bosch, J., & Olsson, H. H. (2021) · Journal of Software Evolution and Process · doi:10.1002/smr.2333 · Cited by 42 · see citing works
Berntzen, M. N., Stray, V. G., Moe, N. B., & Hoda, R. (2023) · Empirical Software Engineering · doi:10.1007/s10664-023-10349-0 · Cited by 40 · see citing works
Self-citationWhite, J., Hays, S., Fu, Q., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2303.07839 · Cited by 39 · see citing works
Liu, M.-L., Hsieh, M.-W., Hsiao, C., Lin, C., & Yang, C. (2018) · Review of Managerial Science · doi:10.1007/s11846-018-0301-4 · Cited by 36 · see citing works
How agile teams make Objectives and Key Results (OKRs) work
Stray, V. G., Gundelsby, J. H., Ulfsnes, R., et al. (2022) · doi:10.1145/3529320.3529332 · Cited by 31 · see citing works
Self-citationA Global View on the Hard Skills and Testing Tools in Software Testing
Florea, R., & Stray, V. G. (2019) · doi:10.1109/icgse.2019.00035 · Cited by 28 · see citing works
Self-citationToward an Agile Product Management: What Do Product Managers Do in Agile Companies?
Tkalich, A., Ulfsnes, R., & Moe, N. B. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-031-08169-9_11 · Cited by 24 · see citing works
Self-citationAttractive Workplaces: What Are Engineers Looking for?
Moe, N. B., Stray, V. G., Šmite, D., et al. (2023) · IEEE Software · doi:10.1109/ms.2023.3276929 · Cited by 20 · see citing works
Self-citationThe Social Developer: The Future of Software Development [Guest Editors' Introduction]
Mens, T., Cataldo, M., & Damian, D. (2019) · IEEE Software · doi:10.1109/ms.2018.2874316 · Cited by 19 · see citing works
Team autonomy and digital transformation
Ravn, J. E., Moe, N. B., Stray, V. G., & Seim, E. A. (2022) · AI & Society · doi:10.1007/s00146-022-01406-1 · Cited by 17 · see citing works
Self-citationDebugging with Stack Overflow: Web Search Behavior in Novice and Expert Programmers
Li, A. D. R., Endres, M., & Weimer, W. R. (2022) · IEEE/ACM International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET) · doi:10.1109/icse-seet55299.2022.9794240 · Cited by 14 · see citing works
Talamadupula, K. (2021) · AI Matters · doi:10.1145/3465074.3465080 · Cited by 11 · see citing works
Pair Programming Practiced in Hybrid Work
Tkalich, A., Moe, N. B., Andersen, N. H., Stray, V. G., & Barbala, A. (2023) · Proceedings of the ... ACM-IEEE International Symposium on Empirical Software Engineering and Measurement/International Symposium on Empirical Software Engineering and Measurement · doi:10.1109/esem56168.2023.10304797 · Cited by 9 · see citing works
Self-citationDAnTE: A Taxonomy for the Automation Degree of Software Engineering Tasks
Melegati, J., & Guerra, E. (2024) · Springer eBooks · doi:10.1007/978-3-031-55642-5_3 · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Ironies of Generative AI: Understanding and Mitigating Productivity Loss in Human-AI Interaction
Simkute, A., Tankelevitch, L., Kewenig, V., Scott, A. E., Sellen, A., & Rintel, S. (2024) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2024.2405782 · Cited by 89 · see citing works
Codepori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology
Rasheed, Z., Malik, S., Kemell, K.-K., Muhammad, W., Saari, M., Systä, K., & Abrahamsson, P. (2024) · doi:10.2139/ssrn.4979510 · Cited by 27 · see citing works
The Impact of Generative AI on Creativity in Software Development: A Research Agenda
Jackson, V., Vasilescu, B., Russo, D., Ralph, P., Prikladnicki, R., Izadi, M., D’Angelo, S., Inman, S., Andrade, A., & van der Hoek, A. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3708523 · Cited by 23 · see citing works
Stray, V., Barbala, A., & Wivestad, V. T. (2025) · Proceedings of the 33rd ACM International Conference on the Foundations of Software Engineering · doi:10.1145/3696630.3730566 · Cited by 7 · see citing works
Self-citationDeveloper Productivity With and Without GitHub Copilot: A Longitudinal Mixed-Methods Case Study
Stray, V., Brandtzæg, E. G., Wivestad, V., Barbala, A., & Moe, N. B. (2026) · Proceedings of the Annual Hawaii International Conference on System Sciences · doi:10.24251/hicss.2026.880 · Cited by 9 · see citing works
Self-citationPanchanadikar, R., & Freeman, G. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790869 · Cited by 3 · see citing works
Panchanadikar, R., Hu, Y., & Freeman, G. (2026) · Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3805689.3812248 · Cited by 1 · see citing works
- Authors
- Ulfsnes, R., Moe, N. B., Stray, V., & Skarpen, M.
- Affiliations
- SINTEF (NO)
- Norwegian University of Science and Technology (NO)
- Corresponding author
- Ulfsnes, Rasmus
- Venue
- Generative AI for Effective Software Development
- Publisher
- Springer Nature Switzerland
- Published
- 2024
- Pages
- 219-234
- OpenAlex
- W4399204263
- Cited by
- 62
- References
- 34
- Citations by year
- 2026: 26 · 2025: 30 · 2024: 6
- FWCI
- 45.14
- Type
- book chapter
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Springer eBooks (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- Norges Forskningsråd (321477, 309344)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- generative AI, software development, ChatGPT, Copilot, developer workflows, knowledge workers, learning loops, agile teams, developer motivation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review