Learning to Describe Solutions for Bug Reports Based on Developer Discussions
This work addresses a domain-specific issue for software developers by automating the extraction of bug solutions from discussions, though it is incremental as it builds on existing techniques for text and code synthesis.
The paper tackles the problem of extracting concise solution descriptions from lengthy developer discussions on bug reports, which are often buried in text and code, by proposing a method to generate natural language summaries and establishing benchmarks with noisy supervision from repository changes.
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.