Is it a bug or an enhancement?: a text-based approach to classify change requests

By G. Antoniol, K. Ayari, Massimiliano Di Penta, Foutse Khomh, Yann-Gaël Guéhéneuc. Conference of the Centre for Advanced Studies on Collaborative Research, 2008

425 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/6ea62d9f321fc7599ff86560b2294a37cc1809b6

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Citations in the corpus

An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation (2018, neutral): “As the content of commit messages and issue trackers might imprecisely identify bug-fixing commits [4, 29], two authors independently analyzed a statistically significant sample (95% confidence level ±5% confidence interval, for a total size of 384) of identified commits to check whether they were a”
Bug localization using latent Dirichlet allocation (2010, background): “[2] classified issues reported into bugs and non-bugs, where a ‘‘bug” referred to a corrective maintenance request and other requests (‘‘non-bugs”) referred to perfective and adaptive maintenance, refactoring, discussions, requests for help, etc.”
Evaluating defect prediction approaches: a benchmark and an extensive comparison (2011, background): “10This is not in contradiction with Antoniol et al. (2008): Bugs mentioned as fixes in CVS comments are intuitively more likely to be real bugs, as they got fixed.”
Predicting the severity of a reported bug (2010, background): “…smaller cases, Menzies et. al. could also not investigate the subsidiary research questions we reported about in section IV Antoniol et al. also used text mining techniques on the descriptions of reported bugs to predict whether a report is either a real bug or a request for an enhancement [17].”
How can i improve my app? Classifying user reviews for software maintenance and evolution (2015, background): “Panichella et al. [34], [36] mined bug reports, development mailing lists and StackOverflow in order to discover descriptions that can suitably explain methods.”
Bug report, feature request, or simply praise? On automatically classifying app reviews (2015, positive): “Table I shows a collection of possible keywords for each review type, which we compiled from the literature [1], [18], [27].”
RCLinker: Automated Linking of Issue Reports and Commits Leveraging Rich Contextual Information (2015, positive): “introduced a framework to predict whether an issue is a feature request or a bug report [2].”
AR-miner: mining informative reviews for developers from mobile app marketplace (2014, background): “Specifically, the first category of related work in this field is to address problems in bug repositories [38, 9, 8, 25].”
On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation (2017, background): “In addition, we cannot exclude imprecisions in the measurement of the fault-proneness of classes due to misclassification of issues (e.g., an enhancement classified as a bug) in the issue-tracking systems (Antoniol et al. 2008).”
What Makes a Good Bug Report? (2008, background): “[2] also pointed out that not all bug reports are related to software problems; in some cases, bug reports correspond to feature requests (which is indicated by a special property).”
When and Why Your Code Starts to Smell Bad (and Whether the Smells Go Away) (2017, neutral): “problems due to incorrect issue classification [3], [32].”