%0 Conference Proceedings %T Learning to Describe Solutions for Bug Reports Based on Developer Discussions %A Panthaplackel, Sheena %A Li, Junyi Jessy %A Gligoric, Milos %A Mooney, Ray %Y Muresan, Smaranda %Y Nakov, Preslav %Y Villavicencio, Aline %S Findings of the Association for Computational Linguistics: ACL 2022 %D 2022 %8 May %I Association for Computational Linguistics %C Dublin, Ireland %F panthaplackel-etal-2022-learning %X 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. %R 10.18653/v1/2022.findings-acl.231 %U https://aclanthology.org/2022.findings-acl.231 %U https://doi.org/10.18653/v1/2022.findings-acl.231 %P 2935-2952