Detecting planning conversations in bug reports
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Date
2022-12-14
Authors
Islam, Rafat Bin
University of Lethbridge. Faculty of Arts and Science
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Journal ISSN
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Publisher
Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science
Abstract
Software developers refer to bug reports as a reliable source of information. However, these bug reports are written in the form of conversations among developers and often become long depending on the complexity of the issue, necessitating a significant amount of time and effort to locate the desired information. Prior work focused on tagging the different types of information in the bug reports. However, their work did not identify Plans. In our work, we focus on retrieving Plans from bug reports and labeling them with a Plan Labeller. First, we analyzed bug reports to identify which section contains Plans. Then we examined three methods to detect Plans. Based on that, we found keywords and key-phrases to be the best approach. We applied lists of keywords and key-phrases iteratively to randomly selected bug reports to construct a list of keywords and key-phrases that can identify Plans in a bug report.
Description
Keywords
Bug reports , Plan labeller , List of keywords and key-phrases , Manually labeled sentences , Automatic sentence annotation , Tagging , Annotated dataset , Software bugs