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Title: Learning to Describe Solutions for Bug Reports Based on Developer Discussions
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.  more » « less
Award ID(s):
1850153
PAR ID:
10350191
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Findings of the Association for Computational Linguistics: ACL 2022
Page Range / eLocation ID:
2935 to 2952
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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