Expanding the capabilities of a bug report annotation tool for summarization

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Devaiya, Shraddhaben Nareshbhai
University of Lethbridge. Faculty of Arts and Science
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Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science
Bug reports serve as critical communication channels between users and software developers, conveying information about unexpected issues or flaws within a software system. Sometimes developers or triagers need to spend significant time understanding a long and complex bug report. A bug report summarizer streamlines the bug triage process for software developers by condensing complex bug reports into concise and informative summaries, facilitating efficient issue prioritization and resolution. To cater comments to the need of the developer for the complex bug report, prior work suggested the solution of annotation of bug report comments. Though the developed annotator worked well, it had issues with annotating the description and off-topic labels. Other prior work identified fiftyone patterns to detect planning in bug report comments. We used these phrases to create an annotated dataset of bug report sentences for plan intention research. In this research, we improve the annotation capabilities of the description and off-topic labeler and automate the plan labeler using a supervised machine-learning approach.
bug reports , bug report summarization , annotation tool , plan labeler , off-topic labeler , description labeler , machine learning model