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.

T. Holschuh, M. Pauser, K. Herzig, T. Zimmermann, R. Premraj, and A. Zeller, “Predicting defects in sap java code: an experience report,” in Software engineering – companion volume, 2009. icse-companion 2009. 31st international conference on, 2009, pp. 172-181.
[Bibtex]

@inproceedings{holschuh-icse-2009,
author={Holschuh, T. and Pauser, M. and Herzig, K. and Zimmermann, T. and Premraj, R. and Zeller, A.},
booktitle={Software Engineering - Companion Volume, 2009. ICSE-Companion 2009. 31st International Conference on}, title={Predicting defects in SAP Java code: An experience report},
year={2009},
month={may},
location = {Vancouver, BC, Canada},
pages={172 -181},
doi={10.1109/ICSE-COMPANION.2009.5070975},
publisher = {IEEE Computer Society},
pdf = {http://www.kim-herzig.de/wp-content/uploads/2011/02/holschuh-icse-2009.pdf},
link={http://www.kim-herzig.de/2009/04/05/predicting-defects-in-sap-java-code-an-experience-report/}
}

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