Use of relative code churn measures to predict system defect density
Proceedings of the 27th international conference on Software engineering
Predicting the Location and Number of Faults in Large Software Systems
IEEE Transactions on Software Engineering
Using Historical In-Process and Product Metrics for Early Estimation of Software Failures
ISSRE '06 Proceedings of the 17th International Symposium on Software Reliability 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
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
The influence of organizational structure on software quality: an empirical case study
Proceedings of the 30th international conference on Software engineering
Predicting defects using network analysis on dependency graphs
Proceedings of the 30th international conference on Software engineering
Implications of ceiling effects in defect predictors
Proceedings of the 4th international workshop on Predictor models in software engineering
Can data transformation help in the detection of fault-prone modules?
DEFECTS '08 Proceedings of the 2008 workshop on Defects in large software systems
Empirical Software Engineering
IEEE Transactions on Software Engineering
Predicting failures with developer networks and social network analysis
Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of 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
Does calling structure information improve the accuracy of fault prediction?
MSR '09 Proceedings of the 2009 6th IEEE International Working Conference on Mining Software Repositories
On the relative value of cross-company and within-company data for defect prediction
Empirical Software Engineering
Usage of multiple prediction models based on defect categories
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Factors characterizing reopened issues: a case study
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
Influence of confirmation biases of developers on software quality: an empirical study
Software Quality Control
An algorithmic approach to missing data problem in modeling human aspects in software development
Proceedings of the 9th International Conference on Predictive Models in Software Engineering
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Defect prediction has been evolved with variety of metric sets, and defect types. Researchers found code, churn, and network metrics as significant indicators of defects. However, all metric sets may not be informative for all defect categories such that only one metric type may represent majority of a defect category. Our previous study showed that defect category sensitive prediction models are more successful than general models, since each category has different characteristics in terms of metrics. We extend our previous work, and propose specialized prediction models using churn, code, and network metrics with respect to three defect categories. Results show that churn metrics are the best for predicting all defects. The strength of correlation for code and network metrics varies with defect category: Network metrics have higher correlations than code metrics for defects reported during functional testing and in the field, and vice versa for defects reported during system testing.