Tools and benchmarks evolve: what is their impact on parameter tuning in SBSE experiments?

In this article, we explore the impact of tool development and its evolution in Search-Based Software Engineering (SBSE) research. As a research tool evolves throughout the years, experiments with novel techniques might require reevaluation of previous studies, especially regarding parameter tuning. These reevaluations also give the opportunity to address the threats to external validity of these previous studies by employing a larger selection of artifacts. To conduct the replicated experiments in this study, the search-based fuzzer EvoMaster is chosen. This SBSE tool has been developed and extended throughout several years (since 2016) and tens of scientific studies. Among the chosen tool's parameters, 6 were carefully selected based on 5 previous studies that we replicate in this article with the latest version of EvoMaster. The replication is applied across an expanded set of artifacts compared to the original replicated studies. Our objective is to validate the robustness and validity of previous findings and to determine the need for parameter tuning in response to the tool's continuous development. Beyond replication, we explored parameter tuning by testing 729 different configurations to find a more performant parameter set, which is later validated through additional rounds of experiments. Additionally, we analyzed the impact of individual parameters on test generation performance using machine learning models, providing insights into their relative effects. Our findings indicate that, although most parameters maintain their efficacy, 2 of them require adjustment. Furthermore, the investigation into the effects of combining different parameter values reveals that carefully optimized configurations can outperform default settings. These findings highlight the importance of regularly reevaluating parameter settings to enhance tool performance in SBSE research.