Comparing fine-grained source code changes and code churn for bug prediction
Proceedings of the 8th Working Conference on Mining Software Repositories
Using the gini coefficient for bug prediction in eclipse
Proceedings of the 12th International Workshop on Principles of Software Evolution and the 7th annual ERCIM Workshop on Software Evolution
On the use of calling structure information to improve fault prediction
Empirical Software Engineering
Bug prediction based on fine-grained module histories
Proceedings of the 34th International Conference on Software Engineering
Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement
Using citation influence to predict software defects
Proceedings of the 10th Working Conference on Mining Software Repositories
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Dependency network measures capture various facets of the dependencies among software modules. For example, betweenness centrality measures how much information flows through a module compared to the rest of the network. Prior studies have shown that these measures are good predictors of post-release failures. However, these studies did not explore the causes for such good performance and did not provide guidance for practitioners to avoid future bugs. In this paper, we closely examine the causes for such performance by replicating prior studies using data from the Eclipse project. Our study shows that a small subset of dependency network measures have a large impact on post-release failure, while other network measures have a very limited impact. We also analyze the benefit of bug prediction in reducing testing cost. Finally, we explore the practical implications of the important network measures.