Software fault prediction tool
Proceedings of the 19th international symposium on Software testing and analysis
Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
An experience report on scaling tools for mining software repositories using MapReduce
Proceedings of the IEEE/ACM international conference on Automated software engineering
Programmer-based fault prediction
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Proceedings of the 2nd International Workshop on Emerging Trends in Software Metrics
On the use of calling structure information to improve fault prediction
Empirical Software Engineering
Evaluating defect prediction approaches: a benchmark and an extensive comparison
Empirical Software Engineering
Using citation influence to predict software defects
Proceedings of the 10th Working Conference on Mining Software Repositories
Is lines of code a good measure of effort in effort-aware models?
Information and Software Technology
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Previous studies have shown that software code attributes, such as lines of source code, and history information, such as the number of code changes and the number of faults in prior releases of software, are useful for predicting where faults will occur. In this study of an industrial software system, we investigate the effectiveness of adding information about calling structure to fault prediction models. The addition of calling structure information to a model based solely on non-calling structure code attributes provided noticeable improvement in prediction accuracy, but only marginally improved the best model based on history and non-calling structure code attributes. The best model based on history and non-calling structure code attributes outperformed the best model based on calling and non-calling structure code attributes.