The Confounding Effect of Class Size on the Validity of Object-Oriented Metrics
IEEE Transactions on Software Engineering
Identifying and characterizing change-prone classes in two large-scale open-source products
Journal of Systems and Software
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
Predicting Faults from Cached History
ICSE '07 Proceedings of the 29th international conference on Software Engineering
Adequate and Precise Evaluation of Quality Models in Software Engineering Studies
PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in 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
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
Proceedings of the 30th international conference on Software engineering
On the Distribution of Software Faults
IEEE Transactions on Software Engineering
Empirical Software Engineering
IEEE Transactions on Software Engineering
Predicting faults using the complexity of code changes
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Tracking concept drift of software projects using defect prediction quality
MSR '09 Proceedings of the 2009 6th IEEE International Working Conference on Mining Software Repositories
Cross-project defect prediction: a large scale experiment on data vs. domain vs. process
Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Fair and balanced?: bias in bug-fix datasets
Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
On the relative value of cross-company and within-company data for defect prediction
Empirical Software Engineering
Journal of Systems and Software
Replication of defect prediction studies: problems, pitfalls and recommendations
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Ownership, experience and defects: a fine-grained study of authorship
Proceedings of the 33rd International Conference on Software Engineering
Don't touch my code!: examining the effects of ownership on software quality
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
BugCache for inspections: hit or miss?
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Empirical Evaluation of Mixed-Project Defect Prediction Models
SEAA '11 Proceedings of the 2011 37th EUROMICRO Conference on Software Engineering and Advanced Applications
Transfer learning for cross-company software defect prediction
Information and Software Technology
Ecological inference in empirical software engineering
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Predicting software development errors using software complexity metrics
IEEE Journal on Selected Areas in Communications
Time variance and defect prediction in software projects
Empirical Software Engineering
Recalling the "imprecision" of cross-project defect prediction
Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
Hi-index | 0.00 |
Defect prediction techniques could potentially help us to focus quality-assurance efforts on the most defect-prone files. Modern statistical tools make it very easy to quickly build and deploy prediction models. Software metrics are at the heart of prediction models; understanding how and especially why different types of metrics are effective is very important for successful model deployment. In this paper we analyze the applicability and efficacy of process and code metrics from several different perspectives. We build many prediction models across 85 releases of 12 large open source projects to address the performance, stability, portability and stasis of different sets of metrics. Our results suggest that code metrics, despite widespread use in the defect prediction literature, are generally less useful than process metrics for prediction. Second, we find that code metrics have high stasis; they dont change very much from release to release. This leads to stagnation in the prediction models, leading to the same files being repeatedly predicted as defective; unfortunately, these recurringly defective files turn out to be comparatively less defect-dense.