Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap

TL;DR

This paper reviews the current state and future prospects of Search-Based Software Engineering (SBSE) in the context of AI foundation models, highlighting challenges and research opportunities.

Contribution

It provides a comprehensive research roadmap analyzing how SBSE can be enhanced by, applied to, and integrated with AI foundation models, outlining future research directions.

Findings

Identifies open challenges in integrating SBSE with AI foundation models.

Proposes research directions for enhancing SBSE with FMs.

Envisions future applications of SBSE in AI-driven domains.

Abstract

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.