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14 results for “GitHub issues”
QuerTCI: A Tool Integrating GitHub Issue Querying with Comment Classification
<p>Issue tracking systems enable users and developers to comment on problems plaguing a software system. Empirical Software Engineering (ESE) researchers study (open-source) project issues and the comments and threads within to discover---among others---challenges developers face when, e.g., incorporating new technologies, platforms, and programming language constructs. However, issue discussion threads accumulate over time and thus can become unwieldy, hindering any insight that researchers may gain. While existing approaches alleviate this burden by classifying issue thread comments, there is a gap between searching popular open-source software repositories (e.g., those on GitHub) for issues containing particular keywords and feeding the results into a classification model. In this paper, we demonstrate a research infrastructure tool called QuerTCI that bridges this gap by integrating the GitHub issue comment search API with the classification models found in existing approaches. Using queries, ESE researchers can retrieve GitHub issues containing particular keywords, e.g., those related to a certain programming language construct, and subsequently classify the kinds of discussions occurring in those issues. Using our tool, our hope is that ESE researchers can uncover challenges related to particular technologies using certain keywords through popular open-source repositories more seamlessly than previously possible. A tool demonstration video may be found at: https://youtu.be/fADKSxn0QUk.</p>
Beyond Textual Issues: Understanding the Usage and Impact of GitHub Reactions
<p>Recently, GitHub introduced a new social feature, named reactions, which are pictorial characters similar to the emoji symbols widely used nowadays in text-based communications. Particularly, GitHub users can use a set of such symbols to react to issues and pull requests. However, little is known about the real usage and benefits of GitHub reactions. In this paper, we analyze the reactions provided by developers to more than 2.5 million issues and 9.7 million issue comments, in order to answer an extensive list of ten research questions about the usage and adoption of reactions. We show that reactions are being increasingly used by open-source developers. Moreover, we also found that issues with reactions usually take more time to be closed and have longer discussions.</p> <p>This dataset contains the data used in the paper "Beyond Textual Issues: Understanding the Usage and Impact of GitHub Reactions", accepted for SBES 2019.</p>
GIRT-Data: Sampling GitHub Issue Report Templates
<p><strong>GIRT-Data</strong> is the first and largest dataset of <strong>issue report templates (IRTs)</strong> in both YAML and Markdown format. This dataset and its corresponding open-source crawler tool are intended to support research in this area and to encourage more developers to use IRTs in their repositories. The stable version of the dataset, containing <code>1_084_300</code> repositories, that <code>50_032</code> of them support IRTs.</p> <p>For more details see the GitHub page of the dataset: <a href="https://github.com/kargaranamir/girt-data">https://github.com/kargaranamir/girt-data</a></p> <p><br> The dataset is accepted for <a href="https://conf.researchr.org/track/msr-2023/msr-2023-data-showcase">MSR 2023</a> conference, under the title of "GIRT-Data: Sampling GitHub Issue Report Templates" <a href="https://scholar.google.com/scholar?q=GIRT-Data:+Sampling+GitHub+Issue+Report+Templates">Search in Google Scholar</a>.</p>
GitHub issues dataset
<p>Datasets for the paper: <em>Predicting the Objective and Priority of Issue Reports of Software Repositories</em></p> <p>- <strong>objective issues.rar</strong>: is used for the objective detection task</p> <p>- <strong>ranking issues - inter project.rar & ranking issues - intra project.rar</strong>: are used for the priority detection task in two context</p> <p> </p> <p><strong>Arxiv link</strong>: https://arxiv.org/abs/2012.10951</p> <p> </p> <p><strong>Citation</strong>: If you use our datasets, please cite as below:</p> <pre>@article{izadi2020predicting, title={Predicting the Objective and Priority of Issue Reports in a Cross project Context}, author={Izadi, Maliheh and Akbari, Kiana and Heydarnoori, Abbas}, journal={arXiv preprint arXiv:2012.10951}, year={2020} }</pre>
GitHub Issue Dataset From Top Repositories of Top Languages
<p>GitHub issue dataset from top 200 most popular repositories associated with top 55 programming languages. The language ranking used for this work is available at: <a href="https://spectrum.ieee.org/static/interactive-the-top-programming-languages-2020">https://spectrum.ieee.org/static/interactive-the-top-programming-languages-2020</a></p> <p>Original work utilizes this dataset to classify issue reports into respective categories: Available at <a href="https://github.com/ansnadeem/aic">https://github.com/ansnadeem/aic</a></p> <p>The original work also appeared in ISSRE'21 titled '<strong>Automatic Issue Classifier: A Transfer Learning Framework for Classifying Issue Reports</strong>'. Please consider citing our work if you use this dataset.</p>
An evidence-based study on issue labeling in Github-based repositories (Supplementary material)
<p>The open-source software community has grown in size and importance over the years. As a consequence, the number of project contributors has increased considerably. The capability of open-source project repositories to accommodate issue reports is essential. An issue report encompasses a large set of data that describes the necessary changes a software should handle. As developers need detailed information to reproduce and find them, incomplete information is a severe problem that may influence triage and defect detection leading to delays in project maintenance. Issue trackers commonly use the labeling method to add extra details to issues. Knowing the importance of labels, this dissertation focus on investigating the usage, creation, and similarities in the context of the issue lifecycle in both maintenance and evolution in the repository issue trackers of the largest and most popular code hosting platform, Github. In addition, it analyzes the number of labeled and unlabeled issues in the repository and the connection between the issues' components, with an analysis focused on the lifecycle. The results indicate a significant correlation between repositories with many issues and the creation of labels, but not all repositories use them. 64.58% of the repositories insert new labels as the project evolves. 73.14% repositories applied on issues the Github standard labels. We also found an influence of primary issue fields such as title, description, and comments in most issue labels, impacting the creation and labeling issues. These numbers show that issue labeling is of prominent relevance for project maintenance and evolution. It provides developers with an easy and convenient way to inform about an incoming issue reported by systems users.</p>
The Good First Issue Recommendation Dataset from "GFI-Bot: Automated Good First Issue Recommendation on GitHub"
<p>This is a good first issue (GFI) recommendation dataset created from the GFI-Bot project (<a href="https://github.com/osslab-pku/gfi-bot">https://github.com/osslab-pku/gfi-bot</a>). For more information about the GFI recommendation problem and GFI-Bot, please check our publications:</p> <ul> <li>Wenxin Xiao, Hao He, Weiwei Xu, Xin Tan, Jinhao Dong, and Minghui Zhou. 2022. Recommending Good First Issues in GitHub OSS Projects. In Proceedings of the 44th International Conference on Software Engineering, ICSE 2022, Pittsburgh, PA, USA, May 21–29, 2022. ACM. <a href="https://hehao98.github.io/files/2022-recgfi.pdf">https://hehao98.github.io/files/2022-recgfi.pdf</a></li> <li>Hao He, Haonan Su, Wenxin Xiao, Runzhi He, and Minghui Zhou. 2022. GFI-Bot: Automated Good First Issue Recommendation on GitHub. In Proceedings of the 2022 ACM 30th Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2022, Singapore, November 14-16, 2022. ACM. <a href="https://hehao98.github.io/files/2022-gfibot.pdf">https://hehao98.github.io/files/2022-gfibot.pdf</a></li> </ul> <p>The dataset is a MongoDB dump and needs to be restored to a MongoDB instance before use. This can be done via the official <a href="https://www.mongodb.com/docs/database-tools/mongorestore/"><code>mongorestore</code></a> tool by running a command like this in the <code>dataset/</code> folder:</p> <pre><code class="language-bash">mongorestore --uri={{ your mongodb url }} --gzip </code></pre> <p>In the <code>gfibot.dataset</code> collection, each document describes the state of an issue at a certain time (either at the time of issue creation or at the time of issue resolution). The <code>resolver_commit_num</code> is the ground truth label (i.e., # of commits the issue resolver has made in the repository before issue resolution, excluding commits for resolving the issue itself; <code>resolver_commit_num = 0</code> means the resolver is someone completely new to the repository). The remaining fields can be used as features or further analyzed to derive new features.</p> <p>The <code>gfibot.resolved_issue</code> collection additionally provides information about which GitHub user resolved this issue and in what commit or pull request. This information can be used to study problems like, e.g., personalized good first issue recommendation or newcomer retention mechanisms.</p> <p>This dataset can be used to evaluate new GFI recommendation approaches. We hope it will be helpful in advancing GFI recommendation research and other future studies on open-source software onboarding.</p>
Example Lminor dataset for Seq2Fun issue #3 (Github)
<p>This is the example dataset for the Seq2Fun issue #3 posted on github <a href="https://github.com/xia-lab/Seq2Fun/issues/3">here</a>.</p> <p>The compressed archive contains the following files:</p> <ul> <li>Directory (<em>raw_reads</em>) which contains subsampled (250k reads) mRNA-Seq fastq files (single-read 50bp) from full <em>Lemna minor</em> extracts. (For the files a respective multiQC html report from our RNASeq mapping pipeline is provided.)</li> <li>Sample table for batch processing (<em>lminor_sampleTable.tab</em>).</li> <li>Bash script (<em>seq2fun_analysis.sh</em>)<em> </em>to perform Seq2Fun analysis (runs smoothly without an error but is unable to map any reads)</li> <li>Output directory (<em>seq2fun</em>) with the files created with the <em>seq2fun_analysis.sh</em> script.</li> </ul> <p>I hope the provided files will help to solve the issue.</p>
Dataset for the study on issue links in GitHub
<p>Dataset for the study on issue links in GitHub</p> <ol> <li>dataset</li> </ol> <ul> <li>issues, pulls and commits: raw data for each project</li> <li>links: extracted links and their types from each project</li> <li>samples: constructed sample of each project</li> </ul> <p> 2. models </p> <ul> <li>Binary-Classifier: Binary classifiers trained to predict the existence of links</li> <li>Multi-Classifier: Multiple classifier trained to predict link types</li> </ul>
Datasets of issue-commit and issue-method links extracted from GitHub repositories
<p>Contains issue-commit and issue-method links extracted from GitHub repositories.</p> <p>Available on GitHub: https://github.com/pragma-once/utilizing-bert-for-traceability/releases</p>
Dataset and results for the study on issue prioritization in GitHub
<p>Dataset and results for the study on issue prioritization</p> <p> </p> <p>-feature: the extracted features for the selected 274 projects</p> <p> </p> <p>-training_data: the training data for 60 projects used to evaluate the prioritization methods</p> <p>--dataset1: data with multicollinearity features removed</p> <p>--dataset2: data with both multicollinearity features and features with weak or insignificant correlation with issue priority removed</p> <p> </p> <p>-ndcg: the complete results of NDCG@k (k ranging from 1 to 20)</p> <p>--result_1: results based on data with multicollinearity features removed</p> <p>--result_2: results based on data with both multicollinearity features and features with weak or insignificant correlation with issue priority removed</p> <p>--result_cross_project: results of cross projects</p>
To What Extent Do Developers Discuss End User Human-Centric Issues of Software on GitHub?
<p>Labelled dataset of a random selection of 1230 issue comments from 7 GitHub repositories.</p>
LLM-Generated Software Requirements from GitHub Issues
<p>This dataset contains software requirements automatically generated from bug reports and feature requests extracted from the three most popular machine learning repositories on GitHub: <strong>Scikit-learn, TensorFlow, and Transformers</strong>. The dataset is structured into issue data, generated requirements, and evaluations based on three well-defined criteria.</p> <h4><strong>Dataset Structure</strong></h4> <ul> <li><strong><code>issues.csv</code></strong>: Contains issue titles along with their corresponding repository names and unique identifiers.</li> <li><strong>Requirements Files</strong>: These files store the requirements generated by LLMs for each issue, categorized by different prompting methods: <ul> <li><code>few_shot_requirements.csv</code></li> <li><code>zero_shot_requirements.csv</code></li> <li><code>expert_requirements.csv</code></li> <li><code>expert_few_shot_requirements.csv</code></li> </ul> </li> <li><strong>Evaluation Files</strong>: These files contain the assessment of the generated requirements based on three key quality criteria: <strong>Unambiguity, Understandability, and Singularity</strong>. The evaluations are also divided by prompting methods: <ul> <li><code>few_shot_evaluation.csv</code></li> <li><code>zero_shot_evaluation.csv</code></li> <li><code>expert_evaluation.csv</code></li> <li><code>expert_few_shot_evaluation.csv</code></li> </ul> </li> </ul>
GitHub Issue Data and Fetching
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