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SPRINT: An Assistant for Issue Report Management
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Managing issue reports is essential for the evolution and maintenance of software systems. However, manual issue management tasks such as triaging, prioritizing, localizing, and resolving issues are highly resource-intensive for projects with large codebases and users. To address this challenge, we present SPRINT, a GitHub application that utilizes state-of-the-art deep learning techniques to streamline issue management tasks. SPRINT assists developers by: (i) identifying existing issues similar to newly reported ones, (ii) predicting issue severity, and (iii) suggesting code files that likely require modification to solve the issues. We evaluated SPRINT using existing datasets and methodologies, measuring its predictive performance, and conducted a user study with five professional developers to assess its usability and usefulness. The results show that SPRINT is accurate, usable, and useful, providing evidence of its effectiveness in assisting developers in managing issue reports. SPRINT is an open-source tool available at https://github.com/sea-lab-wm/sprint_issue_report_assistant_tool.
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Cited by 1 Pith paper
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Applying Large Language Models to Issue Classification: Revisiting with Extended Data and New Models
Fine-tuned GPT-4o classifies GitHub issue types with about 86% F1 on NLBSE 2024 data, while a much larger NLBSE 2023 dataset does not improve results.
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