Classification and evaluation of defects in a project retrospective
Journal of Systems and Software
The Art of Software Testing
Predicting the Location and Number of Faults in Large Software Systems
IEEE Transactions on Software Engineering
Discriminative pattern mining in software fault detection
Proceedings of the 3rd international workshop on Software quality assurance
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
Using Software Dependencies and Churn Metrics to Predict Field Failures: An Empirical Case Study
ESEM '07 Proceedings of the First International Symposium on Empirical Software Engineering and Measurement
A Multivariate Analysis of Static Code Attributes for Defect Prediction
QSIC '07 Proceedings of the Seventh International Conference on Quality Software
Extraction of bug localization benchmarks from history
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
Using In-Process Testing Metrics to Estimate Post-Release Field Quality
ISSRE '07 Proceedings of the The 18th IEEE International Symposium on Software Reliability
An Investigation into the Functional Form of the Size-Defect Relationship for Software Modules
IEEE Transactions on Software Engineering
Validation of network measures as indicators of defective modules in software systems
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
Merits of using repository metrics in defect prediction for open source projects
FLOSS '09 Proceedings of the 2009 ICSE Workshop on Emerging Trends in Free/Libre/Open Source Software Research and Development
On the relative value of cross-company and within-company data for defect prediction
Empirical Software Engineering
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
EQ-mine: predicting short-term defects for software evolution
FASE'07 Proceedings of the 10th international conference on Fundamental approaches to software engineering
Proceedings of the 2nd International Workshop on Emerging Trends in Software Metrics
Factors characterizing reopened issues: a case study
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
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Background: Most of the defect prediction models are built for two purposes: 1) to detect defective and defect-free modules (binary classification), and 2) to estimate the number of defects (regression analysis). It would also be useful to give more information on the nature of defects so that software managers can plan their testing resources more effectively. Aims: In this paper, we propose a defect prediction model that is based on defect categories. Method: We mined the version history of a large-scale enterprise software product to extract churn and static code metrics. and grouped them into three defect categories according to different testing phases. We built a learning-based model for each defect category. We compared the performance of our proposed model with a general one. We conducted statistical techniques to evaluate the relationship between defect categories and software metrics. We also tested our hypothesis by replicating the empirical work on Eclipse data. Results: Our results show that building models that are sensitive to defect categories is cost-effective in the sense that it reveals more information and increases detection rates (pd) by 10% keeping the false alarms (pf) constant. Conclusions: We conclude that slicing defect data and categorizing it for use in a defect prediction model would enable practitioners to take immediate actions. Our results on Eclipse replication showed that haphazard categorization of defects is not worth the effort.