Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911650629550080 |
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| author | Sartaj, Hassan Ali, Shaukat Arcaini, Paolo Arcuri, Andrea |
| author_facet | Sartaj, Hassan Ali, Shaukat Arcaini, Paolo Arcuri, Andrea |
| contents | Search-based software engineering (SBSE), which integrates metaheuristic search techniques with software engineering, has been an active area of research for about 25 years. It has been applied to solve numerous problems across the entire software engineering lifecycle and has demonstrated its versatility in multiple domains. With recent advances in Artificial Intelligence (AI), particularly the emergence of foundation models (FMs) such as large language models (LLMs), the evolution of SBSE alongside these models remains undetermined. In this window of opportunity, we present a research roadmap that articulates the current landscape of SBSE in relation to FMs, identifies open challenges, and outlines potential research directions to advance SBSE through its synergy with FMs. Specifically, we analyze three core aspects: utilizing FMs to enhance SBSE, applying SBSE to advance FMs, and exploring the integration of SBSE and FMs. Furthermore, we present a forward-thinking perspective that envisions the future of SBSE in the era of FMs, highlighting promising research opportunities to address challenges in emerging domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19625 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap Sartaj, Hassan Ali, Shaukat Arcaini, Paolo Arcuri, Andrea Software Engineering Artificial Intelligence Search-based software engineering (SBSE), which integrates metaheuristic search techniques with software engineering, has been an active area of research for about 25 years. It has been applied to solve numerous problems across the entire software engineering lifecycle and has demonstrated its versatility in multiple domains. With recent advances in Artificial Intelligence (AI), particularly the emergence of foundation models (FMs) such as large language models (LLMs), the evolution of SBSE alongside these models remains undetermined. In this window of opportunity, we present a research roadmap that articulates the current landscape of SBSE in relation to FMs, identifies open challenges, and outlines potential research directions to advance SBSE through its synergy with FMs. Specifically, we analyze three core aspects: utilizing FMs to enhance SBSE, applying SBSE to advance FMs, and exploring the integration of SBSE and FMs. Furthermore, we present a forward-thinking perspective that envisions the future of SBSE in the era of FMs, highlighting promising research opportunities to address challenges in emerging domains. |
| title | Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2505.19625 |