SPRINT: An Assistant for Issue Report Management

2025-02-06

AI Summary

To address the high cost and low efficiency of manual triage for bug reports in large-scale software projects, this paper proposes a GitHub-integrated AI assistant that, for the first time, unifies multi-task deep learning across the full bug-report management pipeline—including duplicate detection, severity prediction, and fix-file recommendation. The method integrates a BERT-based semantic model, a graph neural network (GNN) for code localization, and a lightweight GitHub App architecture to deliver end-to-end interpretable recommendations. Evaluated on multiple benchmark datasets, the approach achieves F1 scores of 0.82–0.89. A real-world user study with professional developers demonstrates an average 47% reduction in triage time and a 76% recommendation adoption rate. These results significantly advance the automation level and practical utility of defect management in industrial settings.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Data Mining & Knowledge Management: Recommender Systems
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Streamlining issue management tasks
• Predicting issue severity accurately
• Suggesting code modifications for issues

Innovation

• Deep learning for issue management
• Predicts issue severity automatically
• Suggests code files for modifications