Predicting Defects in SAP Java Code: An Experience Report – ICSE 2009

by Tilman Holschuh, Markus Päuser, Kim Herzig, Thomas Zimmermann, Rahul Premraj, Andreas Zeller

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.

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Reference

Tilman Holschuh, Markus Päuser, Kim Herzig, Thomas Zimmermann, Rahul Premraj, Andreas Zeller. Predicting Defects in SAP Java Code: An Experience Report. In Proceedings of the 31th International Conference on Software Engineering (ICSE 2009), Vancouver, BC, Canada, May 2009.

BibTeX Entry

@inproceedings{holschuh-icse-2009,
    title = "Predicting Defects in SAP Java Code: An Experience Report",
    author = "Tilman Holschuh and Markus Päuser and Kim Herzig and Thomas Zimmermann and Rahul Premraj and Andreas
Zeller",
    year = "2009",
    month = "May",
    booktitle = "Proceedings of the 31th International Conference on Software Engineering",
    location = "Vancouver, BC, Canada",
}