Learning to Describe Solutions for Bug Reports Based on Developer Discussions

• URL: http://arxiv.org/abs/2110.04353v1
• Date: Fri, 8 Oct 2021 19:39:55 GMT
• Title: Learning to Describe Solutions for Bug Reports Based on Developer Discussions
• Authors: Sheena Panthaplackel, Junyi Jessy Li, Milos Gligoric, Raymond J. Mooney
• Abstract summary: We propose generating a concise natural language description of the solution by synthesizing relevant content within the discussion. To support generating an informative description during an ongoing discussion, we propose a secondary task of determining when sufficient context about the solution emerges in real-time. We construct a dataset for these tasks with a novel technique for obtaining noisy supervision from repository changes linked to bug reports.
• Score: 43.427873307255425
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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, which delays 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. Furthermore, to support generating an informative description during an ongoing discussion, we propose a secondary task of determining when sufficient context about the solution emerges in real-time. We construct a dataset for these tasks with a novel technique for obtaining noisy supervision from repository changes linked to bug reports. We establish baselines for generating solution descriptions, and develop a classifier which makes a prediction following each new utterance on whether or not the necessary context for performing generation is available. Through automated and human evaluation, we find these tasks to form an ideal testbed for complex reasoning in long, bimodal dialogue context.

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