Predicting defects in SAP Java code: An experience report


Which components of a large software system are the most defect-prone? In a study on a large SAP Java system, we evaluated and compared a number of defect predictors, based on code features such as complexity metrics, static error detectors, change frequency, or component imports, thus replicating a number of earlier case studies in an industrial context. We found the overall predictive power to be lower than expected; still, the resulting regression models successfully predicted 50–60% of the 20% most defect-prone components.

DOI: 10.1109/ICSE-COMPANION.2009.5070975

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@article{Holschuh2009PredictingDI, title={Predicting defects in SAP Java code: An experience report}, author={Tilman Holschuh and Markus Pauser and Kim Herzig and Thomas Zimmermann and Rahul Premraj and Andreas Zeller}, journal={2009 31st International Conference on Software Engineering - Companion Volume}, year={2009}, pages={172-181} }