C4.5: programs for machine learning
C4.5: programs for machine learning
The mythical man-month (anniversary ed.)
The mythical man-month (anniversary ed.)
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
Predicting Fault Incidence Using Software Change History
IEEE Transactions on Software Engineering
Robust Classification for Imprecise Environments
Machine Learning
Effective Learning in Dynamic Environments by Explicit Context Tracking
ECML '93 Proceedings of the European Conference on Machine Learning
Identifying Reasons for Software Changes Using Historic Databases
ICSM '00 Proceedings of the International Conference on Software Maintenance (ICSM'00)
Detection of software modules with high debug code churn in a very large legacy system
ISSRE '96 Proceedings of the The Seventh International Symposium on Software Reliability Engineering
Static analysis tools as early indicators of pre-release 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
The Top Ten List: Dynamic Fault Prediction
ICSM '05 Proceedings of the 21st IEEE International Conference on Software Maintenance
ISSRE '05 Proceedings of the 16th IEEE International Symposium on Software Reliability Engineering
Predicting defect densities in source code files with decision tree learners
Proceedings of the 2006 international workshop on Mining software repositories
Entropy-based Concept Shift Detection
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Predicting Faults from Cached History
ICSE '07 Proceedings of the 29th international conference on Software Engineering
Predicting Defects for Eclipse
PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering
Improving defect prediction using temporal features and non linear models
Ninth international workshop on Principles of software evolution: in conjunction with the 6th ESEC/FSE joint meeting
Journal of Software Maintenance and Evolution: Research and Practice
Do Crosscutting Concerns Cause Defects?
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering
Is it a bug or an enhancement?: a text-based approach to classify change requests
CASCON '08 Proceedings of the 2008 conference of the center for advanced studies on collaborative research: meeting of minds
Trend Analysis and Issue Prediction in Large-Scale Open Source Systems
CSMR '08 Proceedings of the 2008 12th European Conference on Software Maintenance and Reengineering
Guest editors introduction: special issue on mining software repositories
Empirical Software Engineering
Predicting faults using the complexity of code changes
ICSE '09 Proceedings of the 31st International Conference on 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
How power users help and hinder open bug reporting
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
How, and why, process metrics are better
Proceedings of the 2013 International Conference on Software Engineering
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It is crucial for a software manager to know whether or not one can rely on a bug prediction model. A wrong prediction of the number or the location of future bugs can lead to problems in the achievement of a project's goals. In this paper we first verify the existence of variability in a bug prediction model's accuracy over time both visually and statistically. Furthermore, we explore the reasons for such a high variability over time, which includes periods of stability and variability of prediction quality, and formulate a decision procedure for evaluating prediction models before applying them. To exemplify our findings we use data from four open source projects and empirically identify various project features that influence the defect prediction quality. Specifically, we observed that a change in the number of authors editing a file and the number of defects fixed by them influence the prediction quality. Finally, we introduce an approach to estimate the accuracy of prediction models that helps a project manager decide when to rely on a prediction model. Our findings suggest that one should be aware of the periods of stability and variability of prediction quality and should use approaches such as ours to assess their models' accuracy in advance.