Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Hipikat: recommending pertinent software development artifacts
Proceedings of the 25th International Conference on Software Engineering
Populating a Release History Database from Version Control and Bug Tracking Systems
ICSM '03 Proceedings of the International Conference on Software Maintenance
Analyzing and Relating Bug Report Data for Feature Tracking
WCRE '03 Proceedings of the 10th Working Conference on Reverse Engineering
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Machine Learning
MSR '05 Proceedings of the 2005 international workshop on Mining software repositories
Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Predicting Defects for Eclipse
PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering
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
Classifying Software Changes: Clean or Buggy?
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
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
ReLink: recovering links between bugs and changes
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
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We studied data mining from CVS repositories of two large OO projects, Eclipse and Netbeans, focusing on "fixing-issue" commits. We highlight common characteristics of issue reporting, and problems related to the identification of these messages, and compare static traditional approaches, like Knowledge Engineering, to dynamic approaches based on Machine Learning techniques. We compare for the first time performances of Machine Learning (ML) techniques to automatic classify "fixing-issues" among message commits. Our study calculates precision and recall of different Machine Learning Classifiers for the correct classification of issue-reporting commits. Our results show that some ML classifiers can correctly classify up to 99.9% of such commits.