Predicting Issue Types on GitHub
- URL: http://arxiv.org/abs/2107.09936v1
- Date: Wed, 21 Jul 2021 08:14:48 GMT
- Title: Predicting Issue Types on GitHub
- Authors: Rafael Kallis, Andrea Di Sorbo, Gerardo Canfora, Sebastiano Panichella
- Abstract summary: 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.
- Score: 8.791809365994682
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Software maintenance and evolution involves critical activities for the
success of software projects. To support such activities and keep code
up-to-date and error-free, software communities make use of issue trackers,
i.e., tools for signaling, handling, and addressing the issues occurring in
software systems. However, in popular projects, tens or hundreds of issue
reports are daily submitted. In this context, identifying the type of each
submitted report (e.g., bug report, feature request, etc.) would facilitate the
management and the prioritization of the issues to address. To support issue
handling activities, in this paper, we propose Ticket Tagger, a GitHub app
analyzing the issue title and description through machine learning techniques
to automatically recognize the types of reports submitted on GitHub and assign
labels to each issue accordingly. We empirically evaluated the tool's
prediction performance on about 30,000 GitHub issues. Our results show that the
Ticket Tagger can identify the correct labels to assign to GitHub issues with
reasonably high effectiveness. Considering these results and the fact that the
tool is designed to be easily integrated in the GitHub issue management
process, Ticket Tagger consists in a useful solution for developers.
Related papers
- Visual Analysis of GitHub Issues to Gain Insights [2.9051263101214566]
This paper presents a prototype web application that generates visualizations to offer insights into issue timelines.
It focuses on the lifecycle of issues and depicts vital information to enhance users' understanding of development patterns.
arXiv Detail & Related papers (2024-07-30T15:17:57Z) - VDebugger: Harnessing Execution Feedback for Debugging Visual Programs [103.61860743476933]
We introduce V Debugger, a critic-refiner framework trained to localize and debug visual programs by tracking execution step by step.
V Debugger identifies and corrects program errors leveraging detailed execution feedback, improving interpretability and accuracy.
Evaluations on six datasets demonstrate V Debugger's effectiveness, showing performance improvements of up to 3.2% in downstream task accuracy.
arXiv Detail & Related papers (2024-06-19T11:09:16Z) - MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution [47.850418420195304]
Large Language Models (LLMs) have shown promise in code generation but face difficulties in resolving GitHub issues.
We propose a novel Multi-Agent framework for GitHub Issue reSolution, MAGIS, consisting of four agents customized for software evolution.
arXiv Detail & Related papers (2024-03-26T17:57:57Z) - 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) - SWE-bench: Can Language Models Resolve Real-World GitHub Issues? [80.52201658231895]
SWE-bench is an evaluation framework consisting of $2,294$ software engineering problems drawn from real GitHub issues and corresponding pull requests across $12$ popular Python repositories.
We show that both state-of-the-art proprietary models and our fine-tuned model SWE-Llama can resolve only the simplest issues.
arXiv Detail & Related papers (2023-10-10T16:47:29Z) - 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) - Using Developer Discussions to Guide Fixing Bugs in Software [51.00904399653609]
We propose using bug report discussions, which are available before the task is performed and are also naturally occurring, avoiding the need for additional information from developers.
We demonstrate that various forms of natural language context derived from such discussions can aid bug-fixing, even leading to improved performance over using commit messages corresponding to the oracle bug-fixing commits.
arXiv Detail & Related papers (2022-11-11T16:37:33Z) - Automatic Issue Classifier: A Transfer Learning Framework for
Classifying Issue Reports [0.0]
We use an off-the-shelf neural network called RoBERTa and finetune it to classify the issue reports.
This paper presents our approach to classify the issue reports in a multi-label setting. We use an off-the-shelf neural network called RoBERTa and finetune it to classify the issue reports.
arXiv Detail & Related papers (2022-02-12T21:43:08Z) - 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) - A Transfer Learning Approach for Dialogue Act Classification of GitHub
Issue Comments [1.370633147306388]
This paper presents a transfer learning approach for performing dialogue act classification on issue comments on GitHub.
Since no large labeled corpus of GitHub issue comments exists, employing transfer learning enables us to leverage standard dialogue act datasets.
Being able to map the issue comments to dialogue acts is a useful stepping stone towards understanding cognitive team processes.
arXiv Detail & Related papers (2020-11-10T02:56:18Z)
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.