C4.5: programs for machine learning
C4.5: programs for machine learning
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
Machine Learning
Quantitative Analysis of Errors in Telecommunications Software
ICSM '93 Proceedings of the Conference on Software Maintenance
Measuring and Assessing Maintainability at the End of High Level Design
ICSM '93 Proceedings of the Conference on Software Maintenance
Detection of Logical Coupling Based on Product Release History
ICSM '98 Proceedings of the International Conference on Software Maintenance
Populating a Release History Database from Version Control and Bug Tracking Systems
ICSM '03 Proceedings of the International Conference on Software Maintenance
Mining Version Histories to Guide Software Changes
Proceedings of the 26th International Conference on Software Engineering
Predicting Source Code Changes by Mining Change History
IEEE Transactions on Software Engineering
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Defect Frequency and Design Patterns: An Empirical Study of Industrial Code
IEEE Transactions on Software Engineering
MSR '05 Proceedings of the 2005 international workshop on Mining software repositories
Empirical Validation of Object-Oriented Metrics on Open Source Software for Fault Prediction
IEEE Transactions on Software Engineering
Are refactorings less error-prone than other changes?
Proceedings of the 2006 international workshop on Mining software repositories
An empirical study of fine-grained software modifications
Empirical Software Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Threats on building models from CVS and Bugzilla repositories: the Mozilla case study
CASCON '07 Proceedings of the 2007 conference of the center for advanced studies on Collaborative research
An approach to detecting duplicate bug reports using natural language and execution information
Proceedings of the 30th international conference on Software engineering
Proceedings of the joint international and annual ERCIM workshops on Principles of software evolution (IWPSE) and software evolution (Evol) workshops
Characterizing and predicting which bugs get fixed: an empirical study of Microsoft Windows
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
Bug localization using latent Dirichlet allocation
Information and Software Technology
A machine learning approach for text categorization of fixing-issue commits on CVS
Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
Recovering traceability links between source code and fixed bugs via patch analysis
Proceedings of the 6th International Workshop on Traceability in Emerging Forms of Software Engineering
Are popular classes more defect prone?
FASE'10 Proceedings of the 13th international conference on Fundamental Approaches to Software Engineering
An exploratory study of the impact of antipatterns on class change- and fault-proneness
Empirical Software Engineering
Mining whining in support forums with frictionary
CHI '12 Extended Abstracts on Human Factors in Computing Systems
Evaluating defect prediction approaches: a benchmark and an extensive comparison
Empirical Software Engineering
Time variance and defect prediction in software projects
Empirical Software Engineering
Identifying Linux bug fixing patches
Proceedings of the 34th International Conference on Software Engineering
Five days of empirical software engineering: the PASED experience
Proceedings of the 34th International Conference on Software Engineering
Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement
Mining textual requirements to assist architectural software design: a state of the art review
Artificial Intelligence Review
A field study of refactoring challenges and benefits
Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
Multi-layered approach for recovering links between bug reports and fixes
Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
Studying the impact of social interactions on software quality
Empirical Software Engineering
Proceedings of the 8th ACM SIGSAC symposium on Information, computer and communications security
It's not a bug, it's a feature: how misclassification impacts bug prediction
Proceedings of the 2013 International Conference on Software Engineering
International Journal of Open Source Software and Processes
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Bug tracking systems are valuable assets for managing maintenance activities. They are widely used in open-source projects as well as in the software industry. They collect many different kinds of issues: requests for defect fixing, enhancements, refactoring/restructuring activities and organizational issues. These different kinds of issues are simply labeled as "bug" for lack of a better classification support or of knowledge about the possible kinds. This paper investigates whether the text of the issues posted in bug tracking systems is enough to classify them into corrective maintenance and other kinds of activities. We show that alternating decision trees, naive Bayes classifiers, and logistic regression can be used to accurately distinguish bugs from other kinds of issues. Results from empirical studies performed on issues for Mozilla, Eclipse, and JBoss indicate that issues can be classified with between 77% and 82% of correct decisions.