Learning to Describe Solutions for Bug Reports Based on Developer Discussions

Sheena Panthaplackel, Junyi Jessy Li, Milos Gligoric, Ray Mooney


Abstract
When a software bug is reported, developers engage in a discussion to collaboratively resolve it. While the solution is likely formulated within the discussion, it is often buried in a large amount of text, making it difficult to comprehend and delaying its implementation. To expedite bug resolution, we propose generating a concise natural language description of the solution by synthesizing relevant content within the discussion, which encompasses both natural language and source code. We build a corpus for this task using a novel technique for obtaining noisy supervision from repository changes linked to bug reports, with which we establish benchmarks. We also design two systems for generating a description during an ongoing discussion by classifying when sufficient context for performing the task emerges in real-time. With automated and human evaluation, we find this task to form an ideal testbed for complex reasoning in long, bimodal dialogue context.
Anthology ID:
2022.findings-acl.231
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Smaranda Muresan, Preslav Nakov, Aline Villavicencio
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2935–2952
Language:
URL:
https://aclanthology.org/2022.findings-acl.231
DOI:
10.18653/v1/2022.findings-acl.231
Bibkey:
Cite (ACL):
Sheena Panthaplackel, Junyi Jessy Li, Milos Gligoric, and Ray Mooney. 2022. Learning to Describe Solutions for Bug Reports Based on Developer Discussions. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2935–2952, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Learning to Describe Solutions for Bug Reports Based on Developer Discussions (Panthaplackel et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-acl.231.pdf
Video:
 https://aclanthology.org/2022.findings-acl.231.mp4
Code
 panthap2/describing-bug-report-solutions