Year: 2022
Type: conference-paper
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 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.
Institution: The University of Texas at Austin
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: National Science Foundation
OpenAlex ID: W3207704516