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When a software bug is reported, developers engage in a discussion tocollaboratively resolve it. While the solution is likely formulated within thediscussion, it is often buried in a large amount of text, making it difficultto comprehend and delaying its implementation. To expedite bug resolution, wepropose generating a concise natural language description of the solution bysynthesizing relevant content within the discussion, which encompasses bothnatural language and source code. We build a corpus for this task using a noveltechnique for obtaining noisy supervision from repository changes linked to bugreports, with which we establish benchmarks. We also design two systems forgenerating a description during an ongoing discussion by classifying whensufficient context for performing the task emerges in real-time. With automatedand human evaluation, we find this task to form an ideal testbed for complexreasoning in long, bimodal dialogue context.

Learning to Describe Solutions for Bug Reports Based on Developer Discussions