Learning to Describe Solutions for Bug Reports Based on Developer Discussions

Sheena Panthaplackel, Junyi Jessy Li, Milos Gligoric, Ray Mooney
2022 Findings of the Association for Computational Linguistics: ACL 2022   unpublished
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
more » ... 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.
doi:10.18653/v1/2022.findings-acl.231 fatcat:ehtowbv4p5bu3pkqflkomi6yqm