探索搜索式软件工程与大模型的融合路径，指明未来研究方向。
Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap
- 用大模型增强搜索算法求解效率
- 用搜索技术优化大模型训练与部署
- 适合关注AI+软件工程交叉的科研人员
搜索式软件工程（SBSE）结合元启发式搜索技术与软件工程，已有约25年研究历史，广泛应用于全生命周期问题求解。随着人工智能发展，尤其是大语言模型等基础模型（FMs）兴起，SBSE如何与之协同演进尚不明确。本文提出一项研究路线图，梳理当前SBSE与基础模型的关联现状，识别开放挑战，并规划潜在研究方向。重点分析三方面：利用基础模型增强SBSE、用搜索方法推进基础模型、探索二者融合机制。同时展望大模型时代下SBSE的未来，指出新兴领域中的关键研究机遇。
原文摘要 · Abstract (English)
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.
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