SPRINT: An Assistant for Issue Report Management

TL;DR

SPRINT is a deep learning-powered GitHub application designed to assist developers in managing issue reports by identifying similar issues, predicting severity, and suggesting relevant code files, thereby streamlining the issue management process.

Contribution

This paper introduces SPRINT, a novel open-source tool that leverages deep learning to automate and improve various issue management tasks in software development.

Findings

SPRINT achieves high accuracy in issue similarity detection.

Developers found SPRINT useful and easy to integrate into workflows.

SPRINT effectively predicts issue severity and suggests relevant code files.

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.