WorkDifferentWithAI.com Academic Paper Alert!
Written by 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
Category: “AI for IT”
Article Section: AI Development and Operations; AI-Assisted Programming
Publication Date: 2023-10-28
SEO Description: “Exploring the potential of Generative Artificial Intelligence (GenAI) in software engineering: applications, challenges, and research gaps.”
AI-Generated Paper Summary
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The article titled ‘Generative Artificial Intelligence for Software Engineering — A Research Agenda’ focuses on the role and impact of Generative Artificial Intelligence (GenAI) tools in software development. The tools, such as OpenAIs ChatGPT, GitHub Copilot, and Amazon CodeWhisperer, contribute in the technical and managerial aspects of software development and project activities. However, a complete understanding of GenAI, its applications, limitations and the challenges it brings to software engineering is still evolving. The article proposes a research agenda based on a five-month literature review and focus groups, which identified 78 research questions in 11 areas of software engineering. It emphasizes the potential of GenAI in partial automation and decision-making support in software development, and pinpoints areas requiring further research, including requirements engineering and software design. The authors believe that despite GenAI catalyzing substantial changes in the field of software engineering, the research landscape in this regard remains immature. The research agenda presented seeks to guide researchers and practitioners on current applications and the path of future research.
Claude.ai Full PDF Analysis
Here is a summary of my analysis of the research paper:
Novelty Assessment:
- This paper presents one of the first comprehensive research agendas on generative AI (GenAI) specifically for software engineering. It covers a broad range of SE topics and identifies key open research questions in each area. Developing research agendas is valuable for guiding and organizing research efforts on emerging topics like GenAI.
- The paper takes a novel focus group approach combined with a literature review to identify and refine research questions. The focus groups with SE researchers provide practical, value-driven insights to complement findings from the literature. This method allows the agenda to capture the most timely challenges.
- The research spans 11 diverse SE areas, highlighting how GenAI could assist or transform requirements engineering, software design, implementation, quality assurance, maintenance, processes, management, competencies, education, business aspects, and fundamental concerns. This wide scope is unique and provides a holistic view.
- 78 open research questions are presented overall. Many of these appear novel and timely, tapping into the potential benefits and risks of using GenAI for different SE tasks. For example, how GenAI can support requirements elicitation, automated program repair, or code migration between languages.
- The historical context, state-of-the-art, challenges, and future prospects discussed for each SE area help situate the research questions and provide useful background for exploring them. The limitations of current techniques are well articulated.
Overall, the paper offers a comprehensive, up-to-date research agenda on an emerging high-impact topic. The breadth of open questions across SE subdomains is a novel contribution.
Commercial Applications:
- Many of the research questions have significant commercial relevance, especially those related to integrating GenAI into software processes, tools, and management. Answering these could help companies utilize GenAI more effectively.
- RQs on developer productivity, required skills, and training with GenAI tools are very applicable for software companies looking to leverage these technologies, address talent gaps, and enhance competencies.
- Research on data needs, fine-tuning, and customization of GenAI models for companies can enable commercial usage while protecting IP and security.
- Investigating the business impacts like cost savings, market effects, IP rights, and ethical concerns outlined in the agenda will inform corporate strategy around GenAI adoption.
- Automating software maintenance, quality assurance, and requirements tasks with GenAI has tangible business benefits like reduced costs, quicker delivery, and improved quality.
- Many GenAI application areas like code generation, summarization, translation, and documentation could boost productivity and efficiency in commercial software development.
Overall, companies and tool vendors can derive value from advancements in the research agenda by translating findings into practical methods, guidelines, and technologies that enhance GenAI’s commercial utility. The agenda provides an insightful roadmap for industry-academia collaboration on impactful GenAI research directions.
Keywords
Generative Artificial Intelligence, Software Engineering, Research Agenda, Software Development Activities, Partial Automation
Author’s 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.