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Copiloting the future: How generative AI transforms Software Engineering

Banh, Leonardo, Holldack, Florian, Strobel, Gero·2025·Information and Software Technology

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Research works printed but not linked: 56

  1. Boyd, 2018, Technology, innovation, employment and power: does robotics and artificial intelligence really mean social transformation?, J. Sociol., 54, 331, 10.1177/1440783317726591 · DOI
  2. 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
  3. Ebert, 2018, Digital transformation, IEEE Softw., 35, 16, 10.1109/MS.2018.2801537 · DOI · Paper page
  4. Berente, 2021, Special issue editor's comments: managing artificial intelligence, MIS Q., 45, 1433, 10.25300/MISQ/2021/16274 · DOI · Paper page
  5. Eloundou, 2024, GPTs are GPTs: Labor market impact potential of LLMs, Science, 384, 1306, 10.1126/science.adj0998 · DOI · Paper page
  6. 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
  7. Evans, 1991, Solving home automation problems using artificial intelligence techniques, IEEE Trans. Consum. Electron., 37, 395, 10.1109/30.85542 · DOI
  8. 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
  9. Ågerfalk, 2020, Artificial intelligence as digital agency, Eur. J. Inf. Syst., 29, 1, 10.1080/0960085X.2020.1721947 · DOI · Paper page
  10. 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
  11. Sowa, 2021, Cobots in knowledge work, J. Bus. Res., 125, 135, 10.1016/j.jbusres.2020.11.038 · DOI · Paper page
  12. Banh, 2023, Generative artificial intelligence, Electron. Mark., 33, 63, 10.1007/s12525-023-00680-1 · DOI · Paper page
  13. Jovanovic, 2022, Generative artificial intelligence: trends and prospects, Computer, 55, 107, 10.1109/MC.2022.3192720 · DOI · Paper page
  14. Strobel, 2024, Exploring generative artificial intelligence: a taxonomy and types, 10.24251/HICSS.2024.546 · DOI
  15. Moradi Dakhel, 2023, GitHub copilot AI pair programmer: asset or liability?, J. Syst. Softw., 203, 10.1016/j.jss.2023.111734 · DOI · Paper page
  16. 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
  17. Spinellis, 2024, Pair programming with generative AI, IEEE Softw., 41, 16, 10.1109/MS.2024.3363848 · DOI
  18. Wang, 2023, Friend or foe? Teaming between artificial intelligence and workers with variation in experience, Manag. Sci., 10.1287/mnsc.2021.00588 · DOI
  19. 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
  20. Gioia, 2013, Seeking qualitative rigor in inductive research, Organ. Res. Methods, 16, 15, 10.1177/1094428112452151 · DOI · Paper page
  21. Ozkaya, 2023, The next frontier in software development: AI-augmented software development processes, IEEE Softw., 40, 4
  22. 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
  23. Ruthotto, 2021, An introduction to deep generative modeling, GAMM Mitt., 44, 10.1002/gamm.202100008 · DOI · Paper page
  24. 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
  25. Schmidt, 2023, Assistant platforms, Electron. Mark., 33, 10.1007/s12525-023-00671-2 · DOI
  26. 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
  27. Sauvola, 2024, Future of software development with generative AI, Autom. Softw. Eng., 31, 26, 10.1007/s10515-024-00426-z · DOI
  28. Ozkaya, 2023, Application of large language models to Software Engineering tasks: opportunities, risks, and implications, IEEE Softw., 40, 4
  29. 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
  30. Jalil, 2023, The transformative influence of large language models on software development, arXiv
  31. 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
  32. Bucaioni, 2024, Programming with ChatGPT: how far can we go?, Mach. Learn. Appl., 15
  33. Jaworski, 2023, Study of software developers’ experience using the Github Copilot Tool in the software development process, arXiv
  34. Ernst, 2022, AI-driven development is here: should you worry?, IEEE Softw., 39, 106, 10.1109/MS.2021.3133805 · DOI · Paper page
  35. Hasselbring, 2006, Toward trustworthy software systems, Computer, 39, 91, 10.1109/MC.2006.142 · DOI
  36. Janhunen, 2024, Trust in digital human-AI team collaboration: a systematic review, 3
  37. Coutinho, 2024, The role of generative AI in software development productivity: a pilot case study, arXiv
  38. 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
  39. Seaman, 1999, Qualitative methods in empirical studies of software engineering, IIEEE Trans. Softw. Eng., 25, 557, 10.1109/32.799955 · DOI · Paper page
  40. 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
  41. 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
  42. Paré, 2004, Investigating information systems with positivist case research, CAIS, 13, 10.17705/1CAIS.01318 · DOI · Paper page
  43. Collins, 2018, The central role of theory in qualitative research, Int. J. Qual. Methods, 17, 10.1177/1609406918797475 · DOI · Paper page
  44. Parker, 2014, Qualitative perspectives: through a methodological lens, Qual. Res. Account. Manag., 11, 13, 10.1108/QRAM-02-2014-0013 · DOI
  45. 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
  46. 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
  47. 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
  48. 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
  49. 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
  50. 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
  51. Sabherwal, 2024, The societal impacts of generative artificial intelligence: a balanced perspective, JAIS, 25, 13, 10.17705/1jais.00860 · DOI
  52. Jarvenpaa, 2024, New frontiers in information systems theorizing: human-gAI collaboration, JAIS, 25, 110, 10.17705/1jais.00868 · DOI
  53. Alavi, 2024, Knowledge management perspective of generative artificial intelligence, JAIS, 25, 1, 10.17705/1jais.00859 · DOI
  54. van Huy, 2024, Generative AI, why, how, and outcomes: a user adoption study, THCI, 16, 1, 10.17705/1thci.00198 · DOI
  55. 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
  56. Benbya, 2024, Navigating generative artificial intelligence promises and perils for knowledge and creative work, JAIS, 25, 23, 10.17705/1jais.00861 · DOI

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Metadata

Authors
Banh, L., Holldack, F., & Strobel, G.
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

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.