SPRINT: An Assistant for Issue Report Management
Ahmed Adnan, Antu Saha, Oscar Chaparro
TL;DR
The paper tackles the substantial burden of issue report management in large-scale software projects. It introduces Sprint, an integrated GitHub application that uses state-of-the-art models to classify issue severity, identify similar issues, and localize buggy code in the latest version. Key contributions include a modular, plugin-enabled architecture, end-to-end evaluation with replicated model benchmarks and a five-developer usability study, and an open-source tool designed for easy adoption across repositories. Results indicate strong performance for similarity and severity tasks, reasonable bug localization, and positive user feedback, underscoring Sprint's potential to streamline issue triage and repair workflows in real-world settings.
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_issue_report_assistant_tool.