Large Language Model-based suggestion of objective functions for search-based Product Line Architecture design

Search-based design of Product Line Architecture (PLA) focuses on enhancing the design and functionality of software product lines through variability management, reuse, and optimization. A particular challenge in this area is the selection of objective functions, which significantly influence the success of the search process. Moreover, many objectives make the analysis and choice of a solution to be used harder. The literature has assigned this task to the PLA designer, i.e., the Decision-Maker (DM), who does not always know all the functions and their impact on the optimization outcomes. On the other hand, recent research shows that Large Language Models (LLMs), particularly the Generative Pre-trained Transformer series (GPT), have obtained promising results to help in various Software Engineering (SE) tasks. Considering this fact, this work explores the integration of such LLMs, notably ChatGPT, into the search-based PLA design. By leveraging LLMs’ capacity to understand/generate human-like text, we investigate their potential to assist DMs and propose an approach for suggesting objective functions, thereby simplifying and improving decision-making in PLA design optimization. Through empirical tests and qualitative feedback from domain experts, this research highlights the application of LLMs in search-based SE. The results demonstrate that integrating ChatGPT into PLA design can significantly enhance decision-making efficiency and solution quality, with a 40% reduction in time required for selecting objective functions and a 25% improvement in solution quality from the DM’s point of view. This study maps out the challenges and opportunities that lie ahead in fully harnessing their potential for PLA search-based design.