Mining metrics to predict component failures
Proceedings of the 28th international conference on Software engineering
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
Predicting Defects for Eclipse
PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering
Comments on "Data Mining Static Code Attributes to Learn Defect Predictors"
IEEE Transactions on Software Engineering
Data Mining Techniques for Building Fault-proneness Models in Telecom Java Software
ISSRE '07 Proceedings of the The 18th IEEE International Symposium on Software Reliability
Predicting defects using network analysis on dependency graphs
Proceedings of the 30th international conference on Software engineering
Defect prediction from static code features: current results, limitations, new approaches
Automated Software Engineering
Effort-Aware Defect Prediction Models
CSMR '10 Proceedings of the 2010 14th European Conference on Software Maintenance and Reengineering
Dealing with noise in defect prediction
Proceedings of the 33rd International Conference on Software Engineering
ReLink: recovering links between bugs and changes
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Sample-based software defect prediction with active and semi-supervised learning
Automated Software Engineering
Ecological inference in empirical software engineering
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Why software fails [software failure]
IEEE Spectrum
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Ideally, software defect prediction models should help organize software quality assurance (SQA) resources and reduce cost of finding defects by allowing the modules most likely to contain defects to be inspected first. In this paper, we study the cost-effectiveness of applying defect prediction models in SQA and propose a basic cost-effectiveness criterion. The criterion implies that defect prediction models should be applied with caution. We also propose a new metric FN/(FN+TN) to measure the cost-effectiveness of a defect prediction model.