Advances in software inspections
IEEE Transactions on Software Engineering
Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
Understanding and Controlling Software Costs
IEEE Transactions on Software Engineering
C4.5: programs for machine learning
C4.5: programs for machine learning
Safeware: system safety and computers
Safeware: system safety and computers
Some Conservative Stopping Rules for the Operational Testing of Safety-Critical Software
IEEE Transactions on Software Engineering
Software evolution: code delta and code churn
Journal of Systems and Software - Special issue on software maintenance
Fast formal analysis of requirements via “Topoi Diagrams”
ICSE '01 Proceedings of the 23rd International Conference on Software Engineering
Software Testability: The New Verification
IEEE Software
IEEE Software
Quantitative Analysis of Faults and Failures in a Complex Software System
IEEE Transactions on Software Engineering
Towards a Theory for Integration of Mathematical Verification and Empirical Testing
ASE '98 Proceedings of the 13th IEEE international conference on Automated software engineering
Proceedings of the 17th IEEE international conference on Automated software engineering
Fault Prediction Modeling for Software Quality Estimation: Comparing Commonly Used Techniques
Empirical Software Engineering
What We Have Learned About Fighting Defects
METRICS '02 Proceedings of the 8th International Symposium on Software Metrics
Operational anomalies as a cause of safety-critical requirements evolution
Journal of Systems and Software
Static analysis tools as early indicators of pre-release defect density
Proceedings of the 27th international conference on Software engineering
Data Mining
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
A Replicated Quantitative Analysis of Fault Distributions in Complex Software Systems
IEEE Transactions on Software Engineering
Architecture-Based Software Reliability: Why Only a Few Parameters Matter?
COMPSAC '07 Proceedings of the 31st Annual International Computer Software and Applications Conference - Volume 01
The Effects of Over and Under Sampling on Fault-prone Module Detection
ESEM '07 Proceedings of the First International Symposium on Empirical Software Engineering and Measurement
Comments on "Data Mining Static Code Attributes to Learn Defect Predictors"
IEEE Transactions on Software Engineering
The influence of organizational structure on software quality: an empirical case study
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
On the Distribution of Software Faults
IEEE Transactions on Software Engineering
Empirical Software Engineering
IEEE Transactions on Software Engineering
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
Design and code inspections to reduce errors in program development
IBM Systems Journal
Defect prediction from static code features: current results, limitations, new approaches
Automated Software Engineering
Customization support for CBR-based defect prediction
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
Sample-based software defect prediction with active and semi-supervised learning
Automated Software Engineering
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BACKGROUND: Defect predictors learned from static code measures can isolate code modules with a higher than usual probability of defects. AIMS: To improve those learners by focusing on the defect-rich portions of the training sets. METHOD: Defect data CM1, KC1, MC1, PC1, PC3 was separated into components. A subset of the projects (selected at random) were set aside for testing. Training sets were generated for a NaiveBayes classifier in two ways. In sample the dense treatment, the components with higher than the median number of defective modules were used for training. In the standard treatment, modules from any component were used for training. Both samples were run against the test set and evaluated using recall, probability of false alarm, and precision. In addition, under sampling and over sampling was performed on the defect data. Each method was repeated in a 10-by-10 cross-validation experiment. RESULTS: Prediction models learned from defect dense components out-performed standard method, under sampling, as well as over sampling. In statistical rankings based on recall, probability of false alarm, and precision, models learned from dense components won 4--5 times more often than any other method, and also lost the least amount of times. CONCLUSIONS: Given training data where most of the defects exist in small numbers of components, better defect predictors can be trained from the defect dense components.