Predicting Fault Incidence Using Software Change History
IEEE Transactions on Software Engineering
CVS Release History Data for Detecting Logical Couplings
IWPSE '03 Proceedings of the 6th International Workshop on Principles of Software Evolution
Use of relative code churn measures to predict system defect density
Proceedings of the 27th 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
Proceedings of the 30th international conference on Software engineering
EQ-mine: predicting short-term defects for software evolution
FASE'07 Proceedings of the 10th international conference on Fundamental approaches to software engineering
Are change metrics good predictors for an evolving software product line?
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
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In this paper, we describe an experiment, which analyzes the relative importance and stability of change metrics for predicting defects for 3 releases of the Eclipse project. The results indicate that out of 18 change metrics 3 metrics contain most information about software defects. Moreover, those 3 metrics remain stable across 3 releases of the Eclipse project. A comparative analysis with the full model shows that the prediction accuracy is not too much affected by using a subset of 3 metrics and the recall even improves.