# Generative Artificial Intelligence for Software Engineering -- A Research Agenda

arXiv: https://arxiv.org/abs/2310.18648 · HTML: https://mlsanity.com/paper/2310.18648/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2023.

## Built on by (most cited first, 23 of 23)

- [From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future](https://mlsanity.com/paper/2408.02479/) (2024)
- [LLM-based agents for automating the enhancement of user story quality: An early report](https://mlsanity.com/paper/2403.09442/) (2024)
- [Generative AI for Requirements Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2409.06741/) (2024)
- [Generating Test Scenarios from NL Requirements using Retrieval-Augmented LLMs: An Industrial Study](https://mlsanity.com/paper/2404.12772/) (2024)
- [Exploring Human-AI Collaboration in Agile: Customised LLM Meeting Assistants](https://mlsanity.com/paper/2404.14871/) (2024)
- [Rethinking Software Engineering in the Foundation Model Era: A Curated Catalogue of Challenges in the Development of Trustworthy FMware](https://mlsanity.com/paper/2402.15943/) (2024)
- [RAGVA: Engineering Retrieval Augmented Generation-based Virtual Assistants in Practice](https://mlsanity.com/paper/2502.14930/) (2025)
- [The Future of Generative AI in Software Engineering: A Vision from Industry and Academia in the European GENIUS Project](https://mlsanity.com/paper/2511.01348/) (2025)
- [Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation](https://mlsanity.com/paper/2506.20869/) (2025)
- [BanaServe: Unified KV Cache and Dynamic Module Migration for Balancing Disaggregated LLM Serving in AI Infrastructure](https://mlsanity.com/paper/2510.13223/) (2025)
- [Iterative Resolution of Prompt Ambiguities Using a Progressive Cutting-Search Approach](https://mlsanity.com/paper/2505.02952/) (2025)
- [Explainable Artificial Intelligence Techniques for Software Development Lifecycle: A Phase-specific Survey](https://mlsanity.com/paper/2505.07058/) (2025)
- [From PowerPoint UI Sketches to Web-Based Applications: Pattern-Driven Code Generation for GIS Dashboard Development Using Knowledge-Augmented LLMs, Context-Aware Visual Prompting, and the React Framework](https://mlsanity.com/paper/2502.08756/) (2025)
- [A Research Roadmap for Augmenting Software Engineering Processes and Software Products with Generative AI](https://mlsanity.com/paper/2510.26275/) (2025)
- [Adoption and Impact of ChatGPT in Computer Science Education: A Case Study on a Database Administration Course](https://mlsanity.com/paper/2407.12145/) (2024)
- [Inter-event Interval Microscopy for Event Cameras](https://mlsanity.com/paper/2504.04924/) (2025)
- [AI and Agile Software Development: A Research Roadmap from the XP2025 Workshop](https://mlsanity.com/paper/2508.20563/) (2025)
- [Understanding Practitioners Perspectives on Monitoring Machine Learning Systems](https://mlsanity.com/paper/2509.25195/) (2025)
- [Context-Aware Visual Prompting: Automating Geospatial Web Dashboards with Large Language Models and Agent Self-Validation for Decision Support](https://mlsanity.com/paper/2511.20656/) (2025)
- [Towards AI Evaluation in Domain-Specific RAG Systems: The AgriHubi Case Study](https://mlsanity.com/paper/2602.02208/) (2026)
- [Toward a Sustainable Software Architecture Community: Evaluating ICSA's Environmental Impact](https://mlsanity.com/paper/2604.04096/) (2026)
- [Neuro-Symbolic Agents for Hallucination-Free Requirements Reuse](https://mlsanity.com/paper/2605.01562/) (2026)
- [Persona-Execution Separation: An Architecture Pattern for Evolving LLM Agents under Execution Audit](https://mlsanity.com/paper/2608.27427/) (2026)

## Abstract (arXiv)

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.