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.

为加速缺陷解决，通过综合讨论中的相关内容，包括自然语言和源代码，生成简明的自然语言解决方案描述来解决实际的软件缺陷.