Automated Parameter Optimization of Classification Techniques for Defect Prediction Models

Defect prediction models are classifiers that are trained to identify defect-prone software modules. Such classifiers have configurable parameters that control their characteristics (e.g., the number of trees in a random forest classifier). Recent studies show that these classifiers may underperform...

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Veröffentlicht in:	Proceedings / International Conference on Software Engineering S. 321 - 332
Hauptverfasser:	Tantithamthavorn, Chakkrit, McIntosh, Shane, Hassan, Ahmed E., Matsumoto, Kenichi
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	ACM 14.05.2016
Schlagworte:	Boosting classification techniques Decision trees experimental design Kernel Optimization parameter optimization Predictive models software defect prediction Vegetation
ISSN:	1558-1225
Online-Zugang:	Volltext
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