Generative Artificial Intelligence for Software Engineering—A Research Agenda
Open accessAbstract
Context: Generative artificial intelligence (GenAI) tools have become increasingly prevalent in software development, offering assistance to various managerial and technical project activities. Notable examples of these tools include OpenAI's ChatGPT, GitHub Copilot, and Amazon CodeWhisperer. Objective: Although many recent publications have explored and evaluated the application of GenAI, a comprehensive understanding of the current development, applications, limitations, and open challenges remains unclear to many. Particularly, we do not have an overall picture of the current state of GenAI technology in practical software engineering usage scenarios. Method: We conducted a literature review and focus groups for a duration of five months to develop a research agenda on GenAI for software engineering. Results: We identified 78 open research questions (RQs) in 11 areas of software engineering. Our results show that it is possible to explore the adoption of GenAI in partial automation and support decision‐making in all software development activities. While the current literature is skewed toward software implementation, quality assurance and software maintenance, other areas, such as requirements engineering, software design, and software engineering education, would need further research attention. Common considerations when implementing GenAI include industry‐level assessment, dependability and accuracy, data accessibility, transparency, and sustainability aspects associated with the technology. Conclusions: GenAI is bringing significant changes to the field of software engineering. Nevertheless, the state of research on the topic still remains immature. We believe that this research agenda holds significance and practical value for informing both researchers and practitioners about current applications and guiding future research.
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The Impact of Generative Artificial Intelligence on Software Engineering Education
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“I’ll Ask AI First”: How GenAI Disrupts Collaboration in Agile Teams
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“Still in the loop” : coping with technostress in DevOps teams and the impact of GenAI
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Understanding the developer and user perspectives of design pattern detection tools
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The Impact of Negative Perceptions of Generative AI on Engineering Students’ Academic Performance
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- Authors
- Nguyen‐Duc, A., Cabrero‐Daniel, B., Przybylek, A., Arora, C., Khanna, D., Herda, T., Rafiq, U., Melegati, J., Guerra, E., Kemell, K., Saari, M., Zhang, Z., Le, H., Quan, T., & Abrahamsson, P.
- Affiliations
- Norwegian University of Science and Technology (NO)
- University of Gothenburg (SE)
- Gdansk University of Technology Gdansk Poland
- Monash University (AU)
- Free University of Bozen-Bolzano (IT)
- Austrian Post—Konzern IT Vienna Austria
- University of Helsinki (FI)
- Tampere University (FI)
- Vietnam National University Ho Chi Minh City (VN)
- Corresponding author
- Nguyen‐Duc, Anh
- ORCID
- Venue
- Software: Practice and Experience
- Publisher
- Wiley
- Published
- 2025-07-29
- Volume
- 55
- Issue
- 11
- Pages
- 1806-1843
- ISSN
- 0038-0644 (ISSN-L)
- 1097-024X
- OpenAlex
- W4412899734
- Cited by
- 60
- References
- 222
- Citations by year
- 2026: 47 · 2025: 13
- FWCI
- 62.39
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Software Practice and Experience (publishedVersion)
- Työväentutkimus Vuosikirja (submittedVersion, other-oa)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- Open Research (University of Surrey) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Software Engineering Methodologies
- Keywords
- generative AI, software engineering, research agenda, open research questions, ChatGPT, GitHub Copilot, software development automation, decision support, requirements engineering, software design, software engineering education, software maintenance, quality assurance, dependability, transparency, sustainability, data accessibility
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 ›
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