The Impact of Automated Parameter Optimization on Defect Prediction Models

By Chakkrit Kla Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan, Ken-ichi Matsumoto. IEEE Transactions on Software Engineering, 2018

386 citations recorded by the source corpus.

Defect prediction models—classifiers that identify defect-prone software modules—have configurable parameters that control their characteristics (e.g., the number of trees in a random forest). Recent studies show that these classifiers underperform when default settings are used. In this paper, we study the impact of automated parameter optimization on defect prediction models. Through a case study of 18 datasets, we find that automated parameter optimization: (1) improves AUC performance by up to 40 percentage points; (2) yields classifiers that are at least as stable as those trained using default settings; (3) substantially shifts the importance ranking of variables, with as few as 28 percent of the top-ranked variables in optimized classifiers also being top-ranked in non-optimized classifiers; (4) yields optimized settings for 17 of the 20 most sensitive parameters that transfer among datasets without a statistically significant drop in performance; and (5) adds less than 30 minutes of additional computation to 12 of the 26 studied classification techniques. While widely-used classification techniques like random forest and support vector machines are not optimization-sensitive, traditionally overlooked techniques like C5.0 and neural networks can actually outperform widely-used techniques after optimization is applied. This highlights the importance of exploring the parameter space when using parameter-sensitive classification techniques.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/8aee118e7a7213f410a21019765753365c2e1f78

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Citations in the corpus

The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models (2018, methodology): “To identify the optimized SMOTE parameter settings, we use a grid-search optimization approach, as suggested by recent studies [86, 89].”