by Tilman Holschuh · Markus Päuser · Kim Herzig · Thomas Zimmermann · Rahul Premraj · Andreas Zeller at ICSE 2009
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.
Talk at ICSE 2009 by Tilman Holschuh
Reference
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[2009,inproceedings] bibtexT. Holschuh, M. Päuser, K. Herzig, T. Zimmermann, R. Premraj, and A. Zeller, "Predicting Defects in SAP Java Code: An Experience Report," in ICSE ’09: Procs. of the International Conference on Software Engineering, Companion Volume, Vancouver, 2009, pp. 172-181.
@inproceedings{holschuh:icse:2009, Address = {Vancouver},
Author = {Holschuh, Tilman and P{\"a}user, Markus and Herzig, Kim and Zimmermann, Thomas and Premraj, Rahul and Zeller, Andreas},
Booktitle = {ICSE '09: Procs. of the International Conference on Software Engineering, Companion Volume},
Month = {May},
Pages = {172--181},
Title = {Predicting Defects in {SAP} Java Code: An Experience Report},
Year = {2009}
}