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Tools and benchmarks evolve: what is their impact on parameter tuning in SBSE experiments?
Amid Golmohammadi1, Man Zhang2, Andrea Arcuri1,3
1Kristiania University of Applied Sciences, Kirkegata 24-26, 0153 Oslo, Norway.
Empirical Software Engineering
|November 6, 2025
Summary
Tool evolution in Search-Based Software Engineering (SBSE) necessitates reevaluating prior experiments. Replicating studies with the EvoMaster tool shows most parameters remain effective, but some require tuning for optimal performance.
Area of Science:
- Software Engineering
- Artificial Intelligence
- Computational Intelligence
Background:
- Search-Based Software Engineering (SBSE) research relies on evolving tools.
- Tool development can impact the validity and reproducibility of previous SBSE studies.
- Parameter tuning is crucial for adapting experiments to updated software engineering tools.
Purpose of the Study:
- To reevaluate previous SBSE studies using the latest version of the EvoMaster tool.
- To assess the impact of tool evolution on experimental validity and reproducibility.
- To investigate parameter tuning for optimizing SBSE tool performance.
Main Methods:
- Replicated 5 previous studies using the EvoMaster search-based fuzzer.
- Expanded the set of artifacts used in replication experiments.
- Tested 729 parameter configurations and used machine learning to analyze parameter impact.
Main Results:
- Most parameters retained their efficacy in the latest EvoMaster version.
- Two parameters required adjustment for optimal performance.
- Optimized parameter configurations demonstrated superior performance over default settings.
Conclusions:
- Regular reevaluation of SBSE tools and their parameters is essential.
- Continuous tool development necessitates updated experimental validation.
- Parameter tuning can significantly enhance the performance of SBSE tools like EvoMaster.
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