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26 results for “pull request”
Pull request contributors analysis dataset
<p>Dataset for the paper: G. Gousios, M.-A. Storey, and A. Bacchelli, “Work Practices and Challenges in Pull-Based Development: The Contributor’s Perspective,” in Proceedings of the 38th International Conference on Software Engineering, 2016.</p>
A Dataset of Pull Requests and A Trained Random Forest Model for predicting Pull Request Acceptance
<p>A Curated Dataset of 470,925 pull requests for 3349 popular NPM packages, description of the variables, code snippet for creating a Random Forest model for predicting pull request acceptance, and a pre-trained Random Forest model (in R). The dataset is for the ESEM-2020 paper: "Impact of Technical and Social Factors on Pull Request Quality for the NPM Ecosystem" (<a href="https://arxiv.org/abs/2007.04816">https://arxiv.org/abs/2007.04816</a>). </p> <p>Citation:</p> <pre>@inproceedings{dey2020effect, title={Effect of technical and social factors on pull request quality for the npm ecosystem}, author={Dey, Tapajit and Mockus, Audris}, booktitle={Proceedings of the 14th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)}, pages={1--11}, year={2020} }</pre>
Pull Request Review Comments Dataset
<p><strong>Pull Request Review Comments (PRRC) Datasets</strong></p> <p>Two datasets have been created from the <a href="https://www.gharchive.org/">gharchive</a> website. The <a href="https://docs.github.com/en/developers/webhooks-and-events/github-event-types#pullrequestreviewcommentevent">Pull Request Review Comment Event</a> was selected from the set of available GitHub events. This dataset has been created for CARA: Chatbot for Automating Repairnator Actions as part of a master's thesis at KTH, Stockholm.</p> <p>First, a source dataset was downloaded from gharchive. That dataset ranges from January 2015 to December 2020. It consisted of 54,021,838 PRRCs and is over 18 Gigabytes in size. It took over 120 hours to download all the data files and extract PRRC from it. From this source dataset, two subsets were derived:</p> <ol> <li> Pull Request Review Comments Dataset: This is the dataset of the comments from the latest100,000 threads in the source dataset from gharchive.</li> <li> Pull Request Review Threads Dataset: This is the dataset of comments that were concatenated together if they were from the same thread (in chronological order).</li> </ol> <p> </p> <p><strong>Description</strong></p> <p>The dataset is stored in the <a href="https://jsonlines.org/">JSONLines format</a>, as was the source dataset from gharchive.</p> <p>For PRRC events, the source dataset contains the fields `comment_id`, `commit_id`, `url`, `author`, `created_at`, and `body`. </p> <ul> <li>`comment_id` is the field which specifies the ID GitHub uses for that comment.</li> <li>`commit_id` is the field which specifies the ID of the commit proposed in the pull request.</li> <li>`url` is the field which specifies the url to the comment in a pull request thread.</li> <li>`author` is the field which lists the username of the author of the pull request.</li> <li>`created_at` is the field which specifies the time at which the pull request comment was created.</li> <li>`body` is the field which describes the contents of the PRRC.</li> </ul> <p>The threads dataset contains the fields `url` and `body` which contain similar information as described above. However, the body field differs: it is a concatenation of all the PRRCs in a pull request thread. The comments dataset contains the fields `comment_id`, `commit_id`, `url`, `author`, `created_at`, and `body`. They are the same fields from the initial dataset.</p> <p> </p> <p><strong>Construction</strong></p> <p>We used the <a href="https://fasttext.cc/docs/en/language-identification.html">fasttext</a> model published by Facebook to detect the language of the PRRC. Only those PRRCs in English were preserved. We also removed any PRRC or thread whose size exceeded 128 Kilobytes.</p>
Dataset: An Experience Report on Technical Debt in Pull Requests: Challenges and Lessons Learned
<p>This dataset contains the related data for the paper titled: "An Experience Report on Technical Debt in Pull Requests: Challenges and Lessons Learned". </p>
Dependabot and Security Pull Requests
<p>This deposit contains four (4) main datasets that were used in the study "<em>Dependabot and Security Pull Requests: Large Empirical Study</em>" (<a href="https://doi.org/10.1007/s10664-024-10523-y" target="_blank" rel="noopener">Link</a>). Each dataset is described as follows :</p> <ol> <li><strong>Dataset (1) - Dependency Update</strong> : This dataset concerns issues related to pull requests (PRs) that were created by both users and bots to manage dependency updates in GitHub projects. The search was based on the keywords "Dependency, Update" in the title, body or comment of a PR created in the time period between 26/05/2017 and 15/06/2021 for the 1st partition, and between 01/01/2023 and 30/09/2023 for the 2nd partition. We obtained a total of 6,573,489 PR-related issues belonging to a total of 927,007 repositories for partition (1); and for partition (2), we obtained a total of 3,342,829 PR-related issues belonging to a total of 816,028 repositories.</li> <li><strong>Dataset (2) - Dependabot Security PRs</strong> : The second dataset is related to PRs created by Dependabot to handle security vulnerabilities in project dependencies. In our search, we look for PR-related issues created by "Dependabot-preview" or "Dependabot" and with the label "security", also created during the time period between 26/05/2017 and 30/09/2023. With these parameters, our results consist of 422,388 issues from 47,987 repositories.</li> <li><strong>Dataset (3) - Manual Security PRs</strong> : For this dataset, we were interested in PRs created only by users to handle security vulnerabilities. The search consists of finding the keywords "Dependency, Vulnerable" in the title, body or comment of a PR created in the time period between 26/05/2017 and 30/09/2023. We only consider pull requests created by authors with the type "user". The final results include a total of 186,186 issues for 60,758 repositories.</li> <li><strong>Dataset (4) - Bots' Security PRs</strong> : This dataset is related to PRs created by several bots to handle security vulnerabilities in project dependencies. In the search query, we look for PR-related issues where the keywords "Dependency", and "Security", and "Vulnerability" are mentioned in the title, body, or comment of the PR. These PRs are created by one of the following bots: "Snyk", "Renovate", "Greenkeeper", or "Depfu", also created during the time period between 26/05/2017 and 30/09/2023. The obtained results for the 4 bots consists of a collection of 628,495 PR-related issues in a total of 105,342 repositories.</li> </ol> <p>We also included :</p> <ul> <li><strong>Derived Sample</strong> : This sample contains the data that was selected and extracted to conduct our manual qualitative analysis, and the manual feature extraction.</li> </ul>
"@alex, this fixes #9": Analysis of Referencing Patterns in Pull Request Discussions
<p>This publication consists of a dataset of 7k references manually identified in 450 Pull request (PR) discussion threads sampled from GitHub in CSV format. In addition to the dataset, it also contains R code files which were written to analyze this dataset statistically. This dataset is released under the research, which is accepted for publication at CSCW 2021 conference, titled "@alex, this fixes #9": Analysis of Referencing Patterns in Pull Request Discussions".</p> <p><strong>Paper Abstract</strong></p> <p>Pull Requests (PRs) are a frequently used method for proposing changes to source code repositories. When discussing proposed changes in a PR discussion, stakeholders often reference a wide variety of information objects for establishing shared awareness and common ground. Previous work has not considered how referential behavior impacts collaborative software development via PRs. This knowledge gap is the major barrier in evaluating the current support for referencing in PRs and improving them. We conducted an explorative analysis of ~7K references, collected from 450 public PRs on GitHub, and constructed taxonomies of referent types and expressions. Using our annotated dataset, we identified several patterns in the use of references. Referencing source code elements was prevalent but the authoring interface lacks support for it. Three classes of contextual factors influence referencing behaviors: referent type, discussion thread, and project attributes. Referencing patterns may indicate PR outcomes (e.g., merged PRs frequently reference issues, users, and tests). We conclude with design implications to support more effective referencing in PR discussion interfaces.</p>
Dataset - How do you propose your code changes? Empirical Analysis of Affect Metrics of Pull Requests on GitHub
<p>This package contains the raw open data for the study </p> <p>Marco Ortu, Giuseppe Destefanis, Daniel Graziotin, Michele Marchesi, Roberto Tonelli. 2020. How do you propose your code changes? Empirical Analysis of Affect Metrics of Pull Requests on GitHub. Under Review.</p> <p>The dataset is based on GHTorrent dataset:</p> <p>Georgios Gousios. 2013. The GHTorent dataset and tool suite. In Proceedings of the 10th Working Conference on Mining Software Repositories (MSR ’13). IEEE Press, 233–236</p> <p>And released with the same license (CC BY-SA 4.0).</p>
GitHub Pull Request Demonstration by Alexandr Smagin
<p>In this demonstration, NLU student Alexandr Smagin walks us through pull requests for the TOPS SCHOOL GitHub repository. You can watch the video below or find a link in the 'Additional details' section.</p>
Dataset for ESE submission "Pull Request Latency Explained: An Empirical Overview"
<p>This is the dataset for ESE submission "Pull Request Latency Explained: An Empirical Overview".</p> <p>For research purpose, if you need `pull request id`, please request <a href="https://zenodo.org/record/7299639#.Y2mQ-HpBwUE">the column</a>.</p>
Pull Request Classification - Replication Package
<p><strong>Pull Request Classification - Replication Package:</strong> Contains two .java files (Heuristics.java and a helper class, namely Formatter.java) for the classification of pull request into the eight categories (i.e., bug, feature, test, resources, refactoring, merge, deprecate and others). Additionally, the results of the classification are presented in .csv files.</p>
Measuring Unique Changes: How do Distinct Changes Affect the Size and Lifetime of Pull Requests?
<p>Size metrics are commonly cited features in studies that analyze influencing factors on pull request lifetime. These metrics are also important for integrators, as based on them, they may prefer to prioritize pull requests easy to assess. However, code changes that form pull requests may not be unique, and repetitive changes may represent less complexity than expected by considering size metrics like lines of code or source files. The goal of this study is to analyze the influence of unique changes over pull requests relative to its size and lifetime. We collected data from 83,000+ pull requests of 26 projects hosted on GitHub. Also, we proposed a metric called unique changes rate to measure the proportion of unique changes over the total changes made by a pull request. We conducted experiments with Random Forest regression models and association rules to examine the influence of unique changes rate. Results show that unique changes have more influence over the lifetime of large pull requests, is determined mainly by the number of source files, and low levels of unique changes rate affect more the relationship between pull request size and lifetime than high levels. We conclude that unique changes can figure as an interesting feature in the context of the pull request lifetime. Results indicate that unique changes may increase or decrease the influence of pull request size on its lifetime. Our work has implications for researchers and core team members in software projects since unique changes represent helpful information.</p>
On the Use of Dependabot Security Pull Requests
<p>This dataset contains the data files used to analyze our RQs in the manuscript "On the Use of Dependabot Security Pull Requests."</p> <p>For more information on how to understand the folder structure and dataset, please read the README.md.</p>
GitHub Public Pull Request Comments
<p>Over 13 MILLION pull request comments</p><p>Dataset used for the master's thesis "LLMs for Code Comment Consistency." Covers the languages Go, Java, JavaScript, TypeScripp, and Python. All data is mined from permissively-licensed GitHub public projects with at least 25 stars and 25 pull requests submitted at the time of access.</p><p> </p><p>This dataset pertains specifically to **pull request comments that are made on files.** In other words, every comment in this dataset is linked to a specific file in a pull request.</p><p> </p><p>### What can I do with this data?</p><p>Anything you want, of course, but here are some starter ideas:</p><p>- Sentiment analysis of comments, is there a correlation between number of contributions and positivity of reviews?</p><p>- Pull request comment generation: can we automatically make code review comments?</p><p>- PR text mining: can we mine out examples of a specific type of comment? (in my project, this was comments about function documentation)</p><p> </p><p>The mining code is publicly accessible.</p><p> </p><p>Each file is a JSON object where each key is a Github repository, and each value is a pull request comment in that repository.</p>
Characterizing Support for a Third-party Library: A Study of External Pull Requests for npm packages
<p>Third-party libraries play a key role in building contemporary software applications. Despite this, most libraries are open source that often rely on volunteer (usually unpaid and overworked) contributions for their sustainability. Our motivation is to understand the extent to which third-party libraries are supported by contributions in the form of Pull Requests (PR) from outside the project team (i.e., External PR). Concretely, we analyze 1,076,123 PRs to investigate the External PR prevalence, bots, and the PR characteristics. Our results show that external contributions are prevalent, with packages receiving a high rate of (median of 73.45%) External PR . Furthermore, contributors are also submitting a high proportion of External PR (median of 87.62%). Results indicate a statistical difference in the acceptance of PRs submitted by bots compared to abandoned or open PRs. Furthermore, comparing external and internal PR, we find that Internal PR are more likely to be accepted. Statistically, we find that submitted patches (i.e., commit and code metrics) submitted by Internal PR are higher than patches submitted by External PR. We find that the External PR and Internal PR both have the same content (i.e., introducing new features and fixing bugs). Differently, External PR have more PRs that relate to documentation content, while Internal PR relates to refactoring-related changes</p>
Understanding the Role of External Pull Requests in the NPM Ecosystem
<p>The risk to using third-party libraries in a software application is that much needed maintenance is solely carried out by library maintainers. These libraries may rely on a core team of maintainers (who might be a single maintainer that is unpaid and overworked) to serve a massive client user-base. On the other hand, being open source has the benefit of receiving contributions (in the form of External PRs) to help fix bugs and add new features. In this paper, we investigate the role by which External PRs (contributions from<br> outside the core team of maintainers) contribute to a library. Through a preliminary analysis, we find that External PRs are prevalent, and just as likely to be accepted as maintainer PRs. We find that 26.75% of External PRs submitted fix existing issues. Moreover, fixes also belong to labels such as breaking changes, urgent, and on-hold. Differently from Internal PRs, External PRs cover documentation changes (44 out of 384 PRs), while not having as much refactoring (34 out of 384 PRs). On the other hand, External PRs also cover<br> new features (380 out of 384 PRs) and bugs (120 out of 384). Our results lay the groundwork for understanding how maintainers decide which external contributions they select to evolve their libraries and what role they play in reducing the workload.</p>
The commit history of the dependent libraries and the associated pull requests related to dependency removal.
<p>The dataset is the commit history within a specific period and the matched pull requests corresponding to the commits.</p>
Avaliando as Interações entre Desenvolvedores e ChatGPT no GitHub: Uma análise de dados de Pull Requests
<p>Com o surgimento dos modelos de Linguagem de Grande Escala (LLMs) como o ChatGPT, introduziu um novo conjunto de ferramentas para apoiar desenvolvedores de software na resolução de tarefas de programação. No entanto, a compreensão das interações (ou seja, prompts) entre desenvolvedores e o ChatGPT que resultam em contribuições para o código permanece limitada. Para explorar essa limitação, foi realizada uma avaliação manual de 155 links válidos do ChatGPT, extraídos de 139 Pull Requests (PRs) mesclados à branch principal, revelando as interações entre desenvolvedores e revisores com o ChatGPT que levaram às integrações na branch principal. Os resultados produziram um catálogo de 14 tipos de solicitações feitas ao ChatGPT, categorizadas em quatro grupos principais. Foi identificado um número significativo de solicitações envolvendo revisão de código e a implementação de trechos de código com base em tarefas específicas. Os desenvolvedores também buscaram esclarecer dúvidas solicitando explicações técnicas ou refinamentos de texto para suas páginas web. Além disso, foi verificado que prompts envolvendo a geração de código geralmente exigiram mais interações para produzir a resposta desejada, em comparação com prompts solicitando revisão de código ou informações técnicas.</p>
Towards Filtering Out Deficient Pull Requests Collected through the GitHub API
<p>This is a replication package for an APSEC 2024 ERA paper.</p>
GitHub Pull Request Demonstration by Dhruvil Prajapati
<div> <p>In this demonstration, NLU student Dhruvil Prajapati walks us through pull requests for the TOPS SCHOOL GitHub repository. You can watch the video below or find a link in the 'Additional details' section.</p> </div>
Replication package for our TOSEM paper entitled "An Empirical Study on GitHub Pull Requests' Reactions"
<p>This package contains our dataset and the source code used to collect data from the the top 10,000 most starred GitHub repositories, and the selected six repositories (i.e., Cataclysm-DDA, Julia, Laravel, Node, RPCS3 and Rust), as well as the source code to analyze the data and generate all the figures in the paper. </p> <p>Please carefully read the README.md file for more details.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.