SPRINT: An Assistant for Issue Report Management

• URL: http://arxiv.org/abs/2502.04147v2
• Date: Fri, 07 Feb 2025 09:37:07 GMT
• Title: SPRINT: An Assistant for Issue Report Management
• Authors: Ahmed Adnan, Antu Saha, Oscar Chaparro,
• Abstract summary: Sprint is a GitHub application that utilizes state-of-the-art deep learning techniques to streamline issue management tasks.<n>Sprint identifies existing issues similar to newly reported ones, predicts issue severity, and suggests code files that likely require modification to solve the issues.<n>Sprint is accurate, usable, and useful, providing evidence of its effectiveness in assisting developers in managing issue reports.
• Score: 1.9451328614697958
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Unveiling Pitfalls: Understanding Why AI-driven Code Agents Fail at GitHub Issue Resolution [22.03052751722933]
  Python execution errors during the issue resolution phase correlate with lower resolution rates and increased reasoning overheads. We have identified the most prevalent errors -- such as ModuleNotFoundError and TypeError -- and highlighted particularly challenging errors like OSError and database-related issues.
  arXiv  Detail & Related papers  (2025-03-16T06:24:51Z)
• An Empirical Study on the Classification of Bug Reports with Machine Learning [1.1499574149885023]
  We study how different factors (e.g., project language, report content) can influence the performance of models in handling classification of issue reports. Using the report title or description does not significantly differ; Support Vector Machine, Logistic Regression, and Random Forest are effective in classifying issue reports. Models based on heterogeneous projects can classify reports from projects not present during training.
  arXiv  Detail & Related papers  (2025-03-01T23:19:56Z)
• Enhancing Automated Program Repair with Solution Design [5.547148114448699]
  We introduce DRCodePilot, an approach designed to augment GPT-4-Turbo's APR capabilities by incorporating DR into the prompt instruction. Our experimental results are impressive: DRCodePilot achieves a full-match ratio that is a remarkable 4.7x higher than when GPT-4 is utilized directly.
  arXiv  Detail & Related papers  (2024-08-22T01:13:02Z)
• MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains [54.117238759317004]
  Massive Multitask Agent Understanding (MMAU) benchmark features comprehensive offline tasks that eliminate the need for complex environment setups. It evaluates models across five domains, including Tool-use, Directed Acyclic Graph (DAG) QA, Data Science and Machine Learning coding, Contest-level programming and Mathematics. With a total of 20 meticulously designed tasks encompassing over 3K distinct prompts, MMAU provides a comprehensive framework for evaluating the strengths and limitations of LLM agents.
  arXiv  Detail & Related papers  (2024-07-18T00:58:41Z)
• How to Understand Whole Software Repository? [64.19431011897515]
  An excellent understanding of the whole repository will be the critical path to Automatic Software Engineering (ASE) We develop a novel method named RepoUnderstander by guiding agents to comprehensively understand the whole repositories. To better utilize the repository-level knowledge, we guide the agents to summarize, analyze, and plan.
  arXiv  Detail & Related papers  (2024-06-03T15:20:06Z)
• TroVE: Inducing Verifiable and Efficient Toolboxes for Solving Programmatic Tasks [75.1781376169951]
  Language models (LMs) can solve tasks such as answering questions about tables or images by writing programs. To enable better solutions without human labor, we ask code LMs to curate reusable high-level functions. We present TROVE, a training-free method of inducing a verifiable and efficient toolbox of functions.
  arXiv  Detail & Related papers  (2024-01-23T16:03:17Z)
• GitAgent: Facilitating Autonomous Agent with GitHub by Tool Extension [81.44231422624055]
  A growing area of research focuses on Large Language Models (LLMs) equipped with external tools capable of performing diverse tasks. In this paper, we introduce GitAgent, an agent capable of achieving the autonomous tool extension from GitHub.
  arXiv  Detail & Related papers  (2023-12-28T15:47:30Z)
• MaintainoMATE: A GitHub App for Intelligent Automation of Maintenance Activities [3.2228025627337864]
  Software development projects rely on issue tracking systems at the core of tracking maintenance tasks such as bug reports, and enhancement requests. The handling of issue-reports is critical and requires thorough scanning of the text entered in an issue-report making it a labor-intensive task. We present a unified framework called MaintainoMATE, which is capable of automatically categorizing the issue-reports in their respective category and further assigning the issue-reports to a developer with relevant expertise.
  arXiv  Detail & Related papers  (2023-08-31T05:15:42Z)
• GiveMeLabeledIssues: An Open Source Issue Recommendation System [9.312780130838952]
  Developers often struggle to navigate an Open Source Software (OSS) project's issue-tracking system and find a suitable task. This paper presents a tool that mines project repositories and labels issues based on the skills required to solve them. GiveMeLabeledIssues facilitates matching developers' skills to tasks, reducing the burden on project maintainers.
  arXiv  Detail & Related papers  (2023-03-23T16:39:31Z)
• Supporting the Task-driven Skill Identification in Open Source Project Issue Tracking Systems [0.0]
  We investigate the automatic labeling of open issues strategy to help the contributors to pick a task to contribute. By identifying the skills, we claim the contributor candidates should pick a task more suitable. We applied quantitative studies to analyze the relevance of the labels in an experiment and compare the strategies' relative importance.
  arXiv  Detail & Related papers  (2022-11-02T14:17:22Z)
• Task Compass: Scaling Multi-task Pre-training with Task Prefix [122.49242976184617]
  Existing studies show that multi-task learning with large-scale supervised tasks suffers from negative effects across tasks. We propose a task prefix guided multi-task pre-training framework to explore the relationships among tasks. Our model can not only serve as the strong foundation backbone for a wide range of tasks but also be feasible as a probing tool for analyzing task relationships.
  arXiv  Detail & Related papers  (2022-10-12T15:02:04Z)
• Predicting Issue Types on GitHub [8.791809365994682]
  Ticket Tagger is a GitHub app analyzing the issue title and description through machine learning techniques. We empirically evaluated the tool's prediction performance on about 30,000 GitHub issues.
  arXiv  Detail & Related papers  (2021-07-21T08:14:48Z)
• GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning [172.36214872466707]
  We focus on solving geometric problems, which requires a comprehensive understanding of textual descriptions, visual diagrams, and theorem knowledge. We propose a Geometric Question Answering dataset GeoQA, containing 5,010 geometric problems with corresponding annotated programs.
  arXiv  Detail & Related papers  (2021-05-30T12:34:17Z)
• S3M: Siamese Stack (Trace) Similarity Measure [55.58269472099399]
  We present S3M -- the first approach to computing stack trace similarity based on deep learning. It is based on a biLSTM encoder and a fully-connected classifier to compute similarity. Our experiments demonstrate the superiority of our approach over the state-of-the-art on both open-sourced data and a private JetBrains dataset.
  arXiv  Detail & Related papers  (2021-03-18T21:10:41Z)
• Exploring and Predicting Transferability across NLP Tasks [115.6278033699853]
  We study the transferability between 33 NLP tasks across three broad classes of problems. Our results show that transfer learning is more beneficial than previously thought. We also develop task embeddings that can be used to predict the most transferable source tasks for a given target task.
  arXiv  Detail & Related papers  (2020-05-02T09:39:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.