An extension of fault-prone filtering using precise training and a dynamic threshold
Proceedings of the 2008 international working conference on Mining software repositories
Predicting failures with developer networks and social network analysis
Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering
Accuracy and efficiency comparisons of single- and multi-cycled software classification models
Information and Software Technology
Revisiting the evaluation of defect prediction models
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
Misclassification cost-sensitive fault prediction models
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
Journal of Systems and Software
BBN based approach for improving the software development process of an SME—a case study
Journal of Software Maintenance and Evolution: Research and Practice
Replication of defect prediction studies: problems, pitfalls and recommendations
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Information and Software Technology
Using complexity, coupling, and cohesion metrics as early indicators of vulnerabilities
Journal of Systems Architecture: the EUROMICRO Journal
Ecological inference in empirical software engineering
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
Recalling the "imprecision" of cross-project defect prediction
Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
How, and why, process metrics are better
Proceedings of the 2013 International Conference on Software Engineering
A cost-effectiveness criterion for applying software defect prediction models
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
DConfusion: a technique to allow cross study performance evaluation of fault prediction studies
Automated Software Engineering
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This paper describes a study performed in an industrial setting that attempts to build predictive models to identify parts of a Java system with a high fault probability. The system under consideration is constantly evolving as several releases a year are shipped to customers. Developers usually have limited resources for their testing and inspections and would like to be able to devote extra resources to faulty system parts. The main research focus of this paper is two-fold: (1) use and compare many data mining and machine learning techniques to build fault-proneness models based mostly on source code measures and change/fault history data, and (2) demonstrate that the usual classification evaluation criteria based on confusion matrices may not be fully appropriate to compare and evaluate models.