Parameter tuning or default values? An empirical investigation in search-based software engineering

By Andrea Arcuri, Gordon Fraser. Empirical Software Engineering, 2013

402 citations recorded by the source corpus.

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The Impact of Automated Parameter Optimization on Defect Prediction Models (2018, background): “[3] investigate a minimal test suite that maximizes branch coverage in the context of test data generation.”
Towards Integrative Machine Learning and Knowledge Extraction (2017, methodology): “For example, [60] adopts a CNN framework for breast cancer metastasis detection in lymph nodes.”
Automated Test Case Generation as a Many-Objective Optimisation Problem with Dynamic Selection of the Targets (2018, methodology): “We used default parameter values suggested in related literature since they have been shown to give reasonably acceptable results as compared to finetuned settings [2].”
Bio-inspired computation: Where we stand and what's next (2019, methodology): “be done with each of the reference approaches in the benchmark to ensure fairness in the comparison [301].”