Proceedings ArticleDOI
Is it a bug or an enhancement?: a text-based approach to classify change requests
Giuliano Antoniol,Kamel Ayari,Massimiliano Di Penta,Foutse Khomh,Yann-Gaël Guéhéneuc +4 more
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TLDR
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 and shows that alternating decision trees, naive Bayes classifiers, and logistic regression can be used to accurately distinguish bugs from other kinds.Abstract:
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.read more
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Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Proceedings ArticleDOI
What makes a good bug report
Nicolas Bettenburg,Sascha Just,Adrian Schroter,Cathrin Weiss,Rahul Premraj,Thomas Zimmermann +5 more
TL;DR: The CUEZILLA prototype is a tool that measures the quality of new bug reports and recommends which elements should be added to improve the quality, and discusses several recommendations for better bug tracking systems which should focus on engaging bug reporters, better tool support, and improved handling of bug duplicates.
Proceedings ArticleDOI
An extensive comparison of bug prediction approaches
TL;DR: The absence of an established benchmark makes it hard, if not impossible, to compare approaches to bug prediction, and this paper aims to address this problem.
Journal ArticleDOI
Evaluating defect prediction approaches: a benchmark and an extensive comparison
TL;DR: The results indicate that, while some approaches perform better than others in a statistically significant manner, external validity in defect prediction is still an open problem, as generalizing results to different contexts/learners proved to be a partially unsuccessful endeavor.
Proceedings ArticleDOI
AR-miner: mining informative reviews for developers from mobile app marketplace
TL;DR: This work presents “AR-Miner” — a novel computational framework for App Review Mining, which performs comprehensive analytics from raw user reviews by first extracting informativeuser reviews by filtering noisy and irrelevant ones, then grouping the informative reviews automatically using topic modeling, and finally presenting the groups of most “informative” reviews via an intuitive visualization approach.
References
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