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Copiloting the future: How generative AI transforms Software Engineering
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“Developers also express concerns about loss of agency and skill atrophy when using genAI for software development [10, 15, 28].”
From What Needs Attention? Prioritizing Drivers of Developers' Trust and Adoption of Generative AI (2025) · cites this paper as [10]
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Cites
Saturation in qualitative research: exploring its conceptualization and operationalization
Saunders, B., Sim, J., Kingstone, T., Baker, S., Waterfield, J., Bartlam, B., Burroughs, H., & Jinks, C. (2018) · Quality & Quantity · doi:10.1007/s11135-017-0574-8 · Cited by 12,033
Trust in Automation: Designing for Appropriate Reliance
Lee, J. D., & See, K. A. (2004) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/hfes.46.1.50_30392 · Cited by 6,605
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584
Chain-Of-Thought Prompting Elicits Reasoning in Large Language Models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q. V., & Zhou, D. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1800 · Cited by 4,505
A Survey on Evaluation of Large Language Models
Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., et al. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3641289 · Cited by 4,254
Artificial Intelligence and Management: The Automation–Augmentation Paradox
Raisch, S., & Krakowski, S. (2021) · Academy of Management Review · doi:10.5465/amr.2018.0072 · Cited by 2,755
Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00834-7 · Cited by 2,267
Generative Agents: Interactive Simulacra of Human Behavior
Park, J. S., O'Brien, J. C., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023) · arXiv · doi:10.48550/arxiv.2304.03442 · Cited by 2,173
Experimental evidence on the productivity effects of generative artificial intelligence
Noy, S., & Zhang, W. (2023) · Science · doi:10.1126/science.adh2586 · Cited by 2,150
A survey on large language model based autonomous agents
Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., et al. (2024) · Frontiers of Computer Science · doi:10.1007/s11704-024-40231-1 · Cited by 1,941
A Survey on Large Language Model based Autonomous Agents
Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., et al. (2023) · arXiv · doi:10.48550/arxiv.2308.11432 · Cited by 1,925
Leonardi, P. M. (2011) · MIS Quarterly · doi:10.2307/23043493 · Cited by 1,889
Brynjolfsson, E., Li, D., & Raymond, L. (2023) · National Bureau of Economic Research · doi:10.3386/w31161 · Cited by 995
Bentley University, U., Markus, M. L., Silver, M., & Fordham University (2008) · Journal of the Association for Information Systems · doi:10.17705/1jais.00176 · Cited by 927
Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems
(2024) · doi:10.1145/3613904 · Cited by 896
Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction
Agrawal, A., Gans, J. S., & Goldfarb, A. (2019) · Journal of Economic Perspectives · doi:10.1257/jep.33.2.31 · Cited by 685
Large language models for software engineering: survey and open problems
Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S., & Zhang, J. M. (2023) · 2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE) · doi:10.1109/icse-fose59343.2023.00008 · Cited by 650
Software Testing With Large Language Models: Survey, Landscape, and Vision
Wang, J., Huang, Y., Chen, C., Liu, Z., Wang, S., & Wang, Q. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2024.3368208 · Cited by 573
Large Language Models for Software Engineering: Survey and Open Problems
Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S., & Zhang, J. M. (2023) · arXiv · doi:10.48550/arxiv.2310.03533 · Cited by 523
Baird, A., & Maruping, L. M. (2021) · MIS Quarterly · doi:10.25300/misq/2021/15882 · Cited by 512
Regulating ChatGPT and other Large Generative AI Models
Hacker, P., Engel, A., & Mauer, M. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594067 · Cited by 466
Trust and reliance on AI — An experimental study on the extent and costs of overreliance on AI
Klingbeil, A., Grützner, C., & Schreck, P. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108352 · Cited by 450
Grounded Copilot: How Programmers Interact with Code-Generating Models
Barke, S., James, M. B., & Polikarpova, N. (2023) · Proceedings of the ACM on Programming Languages · doi:10.1145/3586030 · Cited by 436
The Crowdless Future? Generative AI and Creative Problem Solving
Boussioux, L., N. Lane, J., Zhang, M., Jacimovic, V., & Lakhani, K. R. (2023) · doi:10.2139/ssrn.4533642 · Cited by 343
An empirical evaluation of GitHub copilot's code suggestions
Nguyen, N., & Nadi, S. (2022) · Proceedings of the 19th International Conference on Mining Software Repositories · doi:10.1145/3524842.3528470 · Cited by 313
Generative AI for Software Practitioners
Ebert, C., & Louridas, P. (2023) · IEEE Software · doi:10.1109/ms.2023.3265877 · Cited by 293
The Impact of AI on Developer Productivity: Evidence from GitHub Copilot
Peng, S., Kalliamvakou, E., Cihon, P., & Demirer, M. (2023) · arXiv · doi:10.48550/arxiv.2302.06590 · Cited by 287
Wang, D., Churchill, E., Maes, P., Fan, X., Shneiderman, B., Shi, Y., & Wang, Q. (2020) · Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3334480.3381069 · Cited by 271
The Trustworthy Computing Security Development Lifecycle
Lipner, S. (2005) · 20th Annual Computer Security Applications Conference · doi:10.1109/csac.2004.41 · Cited by 234
Data Saturation: The Mysterious Step In Grounded Theory Method
Aldiabat, K., & Le Navenec, C.-L. (2018) · The Qualitative Report · doi:10.46743/2160-3715/2018.2994 · Cited by 222
Large Language Model in Creative Work: The Role of Collaboration Modality and User Expertise
Chen, Z., & Chan, J. (2024) · Management Science · doi:10.1287/mnsc.2023.03014 · Cited by 204
Using GitHub Copilot to Solve Simple Programming Problems
Wermelinger, M. (2023) · Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 · doi:10.1145/3545945.3569830 · Cited by 191
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
Effective test generation using pre-trained Large Language Models and mutation testing
Dakhel, A. M., Nikanjam, A., Majdinasab, V., Khomh, F., & Desmarais, M. C. (2024) · Information and Software Technology · doi:10.1016/j.infsof.2024.107468 · Cited by 157
Measuring and predicting software productivity: A systematic map and review
Petersen, K. (2011) · Information and Software Technology · doi:10.1016/j.infsof.2010.12.001 · Cited by 128
Measuring GitHub Copilot's Impact on Productivity
Ziegler, A., Kalliamvakou, E., Li, X. A., Rice, A., Rifkin, D., Simister, S., Sittampalam, G., & Aftandilian, E. (2024) · Communications of the ACM · doi:10.1145/3633453 · Cited by 124
Is GitHub copilot a substitute for human pair-programming?: an empirical study
Imai, S. (2022) · Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings · doi:10.1145/3510454.3522684 · Cited by 116
Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming
Mozannar, H., Bansal, G., Fourney, A., & Horvitz, E. (2022) · arXiv · doi:10.48550/arxiv.2210.14306 · Cited by 114
Is ChatGPT the Ultimate Programming Assistant -- How far is it?
Tian, H., Lu, W., Li, T. O., Tang, X., Cheung, S.-C., Klein, J., & Bissyandé, T. F. (2023) · arXiv · doi:10.48550/arxiv.2304.11938 · Cited by 114
Investigating and Designing for Trust in AI-powered Code Generation Tools
Wang, R., Cheng, R., Ford, D., & Zimmermann, T. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658984 · Cited by 87
Incorporating Generative AI into Software Development Education
Petrovska, O., Clift, L., Moller, F., & Pearsall, R. (2024) · Proceedings of the 8th Conference on Computing Education Practice · doi:10.1145/3633053.3633057 · Cited by 75
Kuhail, M. A., Mathew, S. S., Khalil, A., Berengueres, J., & Shah, S. J. H. (2024) · Science of Computer Programming · doi:10.1016/j.scico.2024.103111 · Cited by 68
Reasoning with large language models for medical question answering
Lucas, M. M., Yang, J., Pomeroy, J. K., & Yang, C. C. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae131 · Cited by 66
León-Domínguez, U. (2024) · Neuropsychology · doi:10.1037/neu0000948 · Cited by 65
Accelerating Software Development Using Generative AI: ChatGPT Case Study
Rajbhoj, A., Somase, A., Kulkarni, P., & Kulkarni, V. (2024) · Proceedings of the 17th Innovations in Software Engineering Conference · doi:10.1145/3641399.3641403 · Cited by 50
Human-AI collaboration in software engineering: lessons learned from a hands-on workshop
Hamza, M., Siemon, D., Akbar, M. A., & Rahman, T. (2024) · Proceedings of the 7th ACM/IEEE International Workshop on Software-intensive Business · doi:10.1145/3643690.3648236 · Cited by 38
Navigating the Complexity of Generative AI Adoption in Software Engineering—RCR Report
Russo, D. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3680471 · Cited by 27
Grounded Copilot: How Programmers Interact with Code-Generating Models
Barke, S., James, M. B., & Polikarpova, N. (2022) · arXiv · doi:10.48550/arxiv.2206.15000 · Cited by 23
Research works printed but not linked: 56
- Boyd, 2018, Technology, innovation, employment and power: does robotics and artificial intelligence really mean social transformation?, J. Sociol., 54, 331, 10.1177/1440783317726591 · DOI
- Walsham, 2012, Are we making a better world with ICTs? Reflections on a future agenda for the IS field, J. Inf. Technol., 27, 87, 10.1057/jit.2012.4 · DOI · Paper page
- Ebert, 2018, Digital transformation, IEEE Softw., 35, 16, 10.1109/MS.2018.2801537 · DOI · Paper page
- Berente, 2021, Special issue editor's comments: managing artificial intelligence, MIS Q., 45, 1433, 10.25300/MISQ/2021/16274 · DOI · Paper page
- Eloundou, 2024, GPTs are GPTs: Labor market impact potential of LLMs, Science, 384, 1306, 10.1126/science.adj0998 · DOI · Paper page
- Peres, 2020, Industrial artificial intelligence in industry 4.0 - systematic review, challenges and outlook, IEEE Access, 8, 220121, 10.1109/ACCESS.2020.3042874 · DOI · Paper page
- Evans, 1991, Solving home automation problems using artificial intelligence techniques, IEEE Trans. Consum. Electron., 37, 395, 10.1109/30.85542 · DOI
- Muhammad, 2021, Deep learning for safe autonomous driving: current challenges and future directions, IEEE Trans. Intell. Transp. Syst., 22, 4316, 10.1109/TITS.2020.3032227 · DOI · Paper page
- Ågerfalk, 2020, Artificial intelligence as digital agency, Eur. J. Inf. Syst., 29, 1, 10.1080/0960085X.2020.1721947 · DOI · Paper page
- Fügener, 2022, Cognitive challenges in human–artificial intelligence collaboration: investigating the path toward productive delegation, Inf. Syst. Res., 33, 678, 10.1287/isre.2021.1079 · DOI · Paper page
- Sowa, 2021, Cobots in knowledge work, J. Bus. Res., 125, 135, 10.1016/j.jbusres.2020.11.038 · DOI · Paper page
- Banh, 2023, Generative artificial intelligence, Electron. Mark., 33, 63, 10.1007/s12525-023-00680-1 · DOI · Paper page
- Jovanovic, 2022, Generative artificial intelligence: trends and prospects, Computer, 55, 107, 10.1109/MC.2022.3192720 · DOI · Paper page
- Strobel, 2024, Exploring generative artificial intelligence: a taxonomy and types, 10.24251/HICSS.2024.546 · DOI
- Moradi Dakhel, 2023, GitHub copilot AI pair programmer: asset or liability?, J. Syst. Softw., 203, 10.1016/j.jss.2023.111734 · DOI · Paper page
- de O. Melo, 2013, Interpretative case studies on agile team productivity and management, Inf. Softw. Technol., 55, 412, 10.1016/j.infsof.2012.09.004 · DOI
- Spinellis, 2024, Pair programming with generative AI, IEEE Softw., 41, 16, 10.1109/MS.2024.3363848 · DOI
- Wang, 2023, Friend or foe? Teaming between artificial intelligence and workers with variation in experience, Manag. Sci., 10.1287/mnsc.2021.00588 · DOI
- Jarrahi, 2018, Artificial intelligence and the future of work: human-AI symbiosis in organizational decision making, Bus. Horiz., 61, 577, 10.1016/j.bushor.2018.03.007 · DOI · Paper page
- Gioia, 2013, Seeking qualitative rigor in inductive research, Organ. Res. Methods, 16, 15, 10.1177/1094428112452151 · DOI · Paper page
- Ozkaya, 2023, The next frontier in software development: AI-augmented software development processes, IEEE Softw., 40, 4
- Hie, 2024, Efficient evolution of human antibodies from general protein language models, Nat. Biotechnol., 42, 275, 10.1038/s41587-023-01763-2 · DOI · Paper page
- Ruthotto, 2021, An introduction to deep generative modeling, GAMM Mitt., 44, 10.1002/gamm.202100008 · DOI · Paper page
- GM, 2020, A comprehensive survey and analysis of generative models in machine learning, Comput. Sci. Rev., 38, 10.1016/j.cosrev.2020.100285 · DOI · Paper page
- Schmidt, 2023, Assistant platforms, Electron. Mark., 33, 10.1007/s12525-023-00671-2 · DOI
- Susarla, 2023, The Janus effect of generative AI: charting the path for responsible conduct of scholarly activities in information systems, Inf. Syst. Res., 34, 399, 10.1287/isre.2023.ed.v34.n2 · DOI · Paper page
- Sauvola, 2024, Future of software development with generative AI, Autom. Softw. Eng., 31, 26, 10.1007/s10515-024-00426-z · DOI
- Ozkaya, 2023, Application of large language models to Software Engineering tasks: opportunities, risks, and implications, IEEE Softw., 40, 4
- Shafiq, 2021, A literature review of using machine learning in software development life cycle stages, IEEE Access, 9, 140896, 10.1109/ACCESS.2021.3119746 · DOI
- Jalil, 2023, The transformative influence of large language models on software development, arXiv
- Khojah, 2024, Beyond Code generation: an observational study of ChatGPT usage in Software Engineering practice, Proc. ACM Softw. Eng., 1, 1819, 10.1145/3660788 · DOI
- Bucaioni, 2024, Programming with ChatGPT: how far can we go?, Mach. Learn. Appl., 15
- Jaworski, 2023, Study of software developers’ experience using the Github Copilot Tool in the software development process, arXiv
- Ernst, 2022, AI-driven development is here: should you worry?, IEEE Softw., 39, 106, 10.1109/MS.2021.3133805 · DOI · Paper page
- Hasselbring, 2006, Toward trustworthy software systems, Computer, 39, 91, 10.1109/MC.2006.142 · DOI
- Janhunen, 2024, Trust in digital human-AI team collaboration: a systematic review, 3
- Coutinho, 2024, The role of generative AI in software development productivity: a pilot case study, arXiv
- Cámara, 2023, On the assessment of generative AI in modeling tasks: an experience report with ChatGPT and UML, Softw. Syst. Model., 22, 781, 10.1007/s10270-023-01105-5 · DOI
- Seaman, 1999, Qualitative methods in empirical studies of software engineering, IIEEE Trans. Softw. Eng., 25, 557, 10.1109/32.799955 · DOI · Paper page
- Myers, 2007, The qualitative interview in IS research: examining the craft, Inf. Organ., 17, 2, 10.1016/j.infoandorg.2006.11.001 · DOI · Paper page
- Schultze, 2011, Designing interviews to generate rich data for information systems research, Inf. Organ., 21, 1, 10.1016/j.infoandorg.2010.11.001 · DOI · Paper page
- Paré, 2004, Investigating information systems with positivist case research, CAIS, 13, 10.17705/1CAIS.01318 · DOI · Paper page
- Collins, 2018, The central role of theory in qualitative research, Int. J. Qual. Methods, 17, 10.1177/1609406918797475 · DOI · Paper page
- Parker, 2014, Qualitative perspectives: through a methodological lens, Qual. Res. Account. Manag., 11, 13, 10.1108/QRAM-02-2014-0013 · DOI
- Volkoff, 2013, Critical realism and affordances: theorizing IT-associated organizational change processes, MIS Q., 37, 819, 10.25300/MISQ/2013/37.3.07 · DOI · Paper page
- Trocin, 2021, How Artificial Intelligence affords digital innovation: a cross-case analysis of Scandinavian companies, Technol. Forecast. Soc. Change, 173, 10.1016/j.techfore.2021.121081 · DOI
- Birks, 2013, Grounded theory method in information systems research: its nature, diversity and opportunities, Eur. J. Inf. Syst., 22, 1, 10.1057/ejis.2012.48 · DOI
- Dennis, 2023, AI agents as team members: effects on satisfaction, conflict, trustworthiness, and willingness to work with, J. Manag. Inf. Syst., 40, 307, 10.1080/07421222.2023.2196773 · DOI
- Fügener, 2021, Will humans-in-the-loop become Borgs? Merits and pitfalls of working with AI, MIS Q., 45, 1527, 10.25300/MISQ/2021/16553 · DOI · Paper page
- Humphreys, 2024, AI hype as a cyber security risk: the moral responsibility of implementing generative AI in business, AI Ethics, 4, 791, 10.1007/s43681-024-00443-4 · DOI
- Sabherwal, 2024, The societal impacts of generative artificial intelligence: a balanced perspective, JAIS, 25, 13, 10.17705/1jais.00860 · DOI
- Jarvenpaa, 2024, New frontiers in information systems theorizing: human-gAI collaboration, JAIS, 25, 110, 10.17705/1jais.00868 · DOI
- Alavi, 2024, Knowledge management perspective of generative artificial intelligence, JAIS, 25, 1, 10.17705/1jais.00859 · DOI
- van Huy, 2024, Generative AI, why, how, and outcomes: a user adoption study, THCI, 16, 1, 10.17705/1thci.00198 · DOI
- Grinschgl, 2022, Supporting cognition with modern technology: distributed cognition today and in an AI-enhanced future, Front. Artif. Intell., 5, 10.3389/frai.2022.908261 · DOI · Paper page
- Benbya, 2024, Navigating generative artificial intelligence promises and perils for knowledge and creative work, JAIS, 25, 23, 10.17705/1jais.00861 · DOI
Cited by
What Needs Attention? Prioritizing Drivers of Developers' Trust and Adoption of Generative AI
Choudhuri, R., Trinkenreich, B., Pandita, R., Kalliamvakou, E., Steinmacher, I., Gerosa, M., Sanchez, C., & Sarma, A. (2025) · arXiv · doi:10.48550/arxiv.2505.17418 · Cited by 14
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 19
Aydeniz, M. (2026) · Sociology Compass · doi:10.1111/soc4.70230 · Cited by 1
Metadata
- Authors
- Banh, L., Holldack, F., & Strobel, G.
- ORCID
- Banh, Leonardo
- Venue
- Information and Software Technology
- Publisher
- Elsevier BV
- Published
- 2025-07
- Volume
- 183
- Pages
- 107751
- ISSN
- 0950-5849
- Cited by
- 67
- References
- 50
- Language
- en
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- creativecommons.org/licenses/by/4.0/ (vor)
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