Do quality indicators prefer particular multi-objective search algorithms in search-based software engineering? (Hot off the press track at GECCO 2021)

In Search-based Software Engineering (SBSE), researchers and practitioners (SBSE users) using multi-objective search algorithms (MOSAs) often select commonly used MOSAs to solve their search problems. Such a selection is usually not justified, and the main selection criterion is the MOSA popularity. On the other hand, SBSE users are usually aware of the desired qualities of solutions of their search problem, captured by Quality Indicators (QIs). Consequently, to guide SBSE users in selecting MOSAs for their specific SBSE problems, we study preference relationships between QIs and MOSAs with an empirical evaluation. Given a QI or a quality aspect (e.g., convergence), we suggest a MOSA that is highly likely to produce solutions representing the QI or the quality aspect. Based on our experiments' results, we provide insights and suggestions for SBSE users to choose a MOSA based on experimental evidence. This is an extended abstract of the paper [2]: S. Ali, P. Arcaini, and T. Yue, "Do Quality Indicators Prefer Particular Multi-Objective Search Algorithms in Search-Based Software Engineering?", 12th International Symposium on Search-Based Software Engineering (SSBSE 2020).