Configuring Parameters of Search-based Software Engineering Tools with Multiple LLM Models

Search-Based Software Engineering (SBSE) tools offer automatic support by applying search-based techniques to solve different optimization problems of various software engineering areas. However, configuring SBSE tools often requires expertise in optimization techniques, posing a significant barrier for non-specialist users. This task involves selecting suitable algorithms, tuning parameters, choosing appropriate objective functions and search operators, and making decisions that affect optimization quality and performance. To address this challenge, we propose a multiple-LLM approach that integrates Large Language Models (LLMs), specifically ChatGPT-4, into SBSEworkflows to provide context-aware and natural language suggestions for parameter configuration. The approach follows a Divide and Conquer strategy, in which specialized LLMs are trained for distinct parameter configuration subproblems. As a proof of concept, we implemented OPLA-Wizard that works with OPLA-Tool v2.0, for Product Line Architecture (PLA) design. This assistant supports users in configuring algorithm settings, search operators, and objective functions through a user-friendly interface. Preliminary results show that the multiple-LLM approach improves accuracy, explanation clarity, and output format adherence compared to a single-LLM approach. This work contributes to the democratization of SBSE by reducing cognitive load, preventing misconfigurations, and making advanced optimization techniques more accessible to a broader audience.