Generative Artificial Intelligence for Software Engineering -- A
Research Agenda
- URL: http://arxiv.org/abs/2310.18648v1
- Date: Sat, 28 Oct 2023 09:14:39 GMT
- Title: Generative Artificial Intelligence for Software Engineering -- A
Research Agenda
- Authors: Anh Nguyen-Duc, Beatriz Cabrero-Daniel, Adam Przybylek, Chetan Arora,
Dron Khanna, Tomas Herda, Usman Rafiq, Jorge Melegati, Eduardo Guerra,
Kai-Kristian Kemell, Mika Saari, Zheying Zhang, Huy Le, Tho Quan, Pekka
Abrahamsson
- Abstract summary: We conducted a literature review and focus groups for a duration of five months to develop a research agenda on GenAI for 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.
Common considerations when implementing GenAI include industry-level assessment, dependability and accuracy, data accessibility, transparency, and sustainability aspects associated with the technology.
- Score: 8.685607624226037
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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
OpenAIs ChatGPT, GitHub Copilot, and Amazon CodeWhisperer. 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. We conducted a literature
review and focus groups for a duration of five months to develop a research
agenda on GenAI for Software Engineering. 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. 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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