Predicting Defects in SAP Java Code: An Experience Report
by
Tilman Holschuh, Markus Paeuser, Kim Herzig, Thomas Zimmermann, Rahul Premraj, Andreas Zeller
Proceedings of the 31th International Conference on Software Engineering, May 2009.
Abstract
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.
BibTeX Entry
@inproceedings{holschuh-icse-2009,
title = "Predicting Defects in SAP Java Code: An Experience Report",
author = "Tilman Holschuh and Markus Paeuser 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",
}