BEE: A Tool for Structuring and Analyzing Bug Reports (Teaser, ESEC/FSE 2020)
BEE: A Tool for Structuring and Analyzing Bug Reports (Teaser, ESEC/FSE 2020)
Yang Song and Oscar Chaparro
(College of William and Mary, USA; College of William and Mary, USA)
Abstract: This paper introduces BEE, a tool that automatically analyzes user-written bug reports and provides feedback to reporters and developers about the system’s observed behavior (OB), expected behavior (EB), and the steps to reproduce the bug (S2R). BEE employs machine learning to (i) detect if an issue describes a bug, an enhancement, or a question; (ii) identify the structure of bug descriptions by automatically labeling the sentences that correspond to the OB, EB, or S2R; and (iii) detect when bug reports fail to provide these elements. BEE is integrated with GitHub and offers a public web API that researchers can use to investigate bug management tasks based on bug reports. We evaluated BEE’s underlying models on more than 5k existing bug reports and found they can correctly detect OB, EB, and S2R sentences as well as missing information in bug reports. BEE is an open-source project that can be found at https://git.io/JfFnN. A screencast showing the full capabilities of BEE can be found at • BEE-TOOL .
Article: https://doi.org/10.1145/3368089.3417928
Supplementary web page: https://git.io/JfFnN
Submitted to the conference by Yang Song on 2020-11-01
Video Tags: Bug reporting, bug report structure, bug report quality, text analysis, fse20demo-p17-p, DOI: 10.1145/3368089.3417928
Presentation at the ESEC/FSE 2020 conference, November 8–13, 2020, https://2020.esec-fse.org/
Sponsored by ACM SIGSOFT, https://www.sigsoft.org/
Twitter: / fseconf
Reddit: / esecfse
ISSTA 2021: Panels Celebrating 30 editions of ISSTA (statements from panel in timeband 1)
ISSTA 2021: Panels Celebrating 30 editions of ISSTA (statements from panel in timeband 3)