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116 results for “code review”
Artifacts Package - "Software Security during Modern Code Review: The Developer's Perspective"
<p>Artifacts Package of the accepted FSE 22 paper: "Software Security during Modern Code Review: The Developer's Perspective".</p>
Paper samples for the SLR "A systematic literature review on the impact of AI models on the security of code generation"
<p>Here we provide the whole list of papers that were queried for the SLR "A systematic literature review on the impact of AI models on the security of code generation" by Negri-Ribalta et al. The dataset provides all the information of all the papers gathered, their database of origin, and if it was accepted/rejected/duplicated. </p> <p>The file is in xls format .</p>
Dataset of RCQE (Automatic Code Quality Estimation in multi-round Code Review)
<p>A multi-modal dataset , including source code, code diffs, and natural language comments as three types of metadata. The format for each instance in the dataset is as follows: code source file (in source code format), current commit code (in diff format), current commit comment (in natural language format, empty if none), previous commit (in diff format), previous submission commit comment (in natural language format).</p>
Supplemental Materials to "A Fine-grained Taxonomy of Code Review Feedback in TypeScript Projects"
<p>Supplemental Materials to the paper "A Fine-grained Taxonomy of Code Review Feedback in TypeScript Projects".</p>
Don't Forget to Change These Functions! Recommending Co-Changed Functions in Modern Code Review
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Replication Package for the Paper: "The Impact of Code Review on Architectural Changes"
<p>This is a replication package for the paper "The Impact of Code Review on Architectural Changes", to be published at the IEEE Transactions on Software Engineering (TSE).</p> <p> </p> <p>The TSE paper mentioned above is a journal extension of a previous conference paper published at the IEEE/ACM International Conference on Automated Software Engineering (ASE'17). Hence, this is also the replication package for our conference paper "Are Developers Aware of the Architectural Impact of Their Changes?"</p> <p> </p> <p>The replication package is composed of:</p> <p> </p> <p>1 - 103,778 structural architectures extracted from the source code of 7 open source software systems.</p> <p>2 - Churn and size metrics for each of the extracted structural architectures.</p> <p>3 - Values of structural cohesion and coupling computed for the significant architectural changes</p> <p>4 - A manual classification of the changes' intent</p> <p> </p> <p>The presented dataset can be used not only to replicate the TSE paper but also to perform new empirical studies involving software architecture, architectural changes, software changes, code review and software module clustering, to mention a few.</p> <p> </p>
Replication Package for the Paper: "Rebasing in Code Review Considered Harmful"
<p>This is the replication package for the paper: "Rebasing in Code Review Considered Harmful: A Large-scale Empirical Investigation", published at the 19th IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM'19).</p> <p> </p> <p>The replication package is composed of the raw results from each of our 4 research questions. In addition, we include the results from the small empirical study we performed to evaluate the methodology we proposed to handle rebasing operations in code review data.</p> <p> </p> <p>Each of the reported results has been obtained by following the procedures and heuristics described in the paper.</p> <p> </p> <p>This publication uses the CROP dataset of code review data: <a href="https://crop-repo.github.io/">https://crop-repo.github.io/</a></p>
Replication package for Journal paper title "Supporting the identification of prevalent quality issues in code changes by analyzing reviewers' feedback"
<p>Replication package for the study "Supporting the identification of prevalent quality issues in code changes by analyzing reviewers' feedback"</p> <p> </p>
Coding and results Literature. Review: Professional Learning Communities and Professional Capital in the Improvement of Learning Outcomes
<p>Project PID2020-117020GB-I00 , funded by : Ministerio de Ciencia e Innovación de España/<br>AEI/10.13039/501100011033 and by the predoctoral contracts grant for the training of PhD implemented by<br>the [grant number PRE2021-098075]</p>
Human vs. Machine: How Software Engineers Perceive and Engage with AI-Assisted Code Reviews Compared to Their Peers
<p>This Shared Document Package contains the supplementary materials package for the study, "Human vs. Machine: How Software Engineers Perceive and Engage with AI-Assisted Code Reviews Compared to Their Peers." The package contains all relevant documentation and materials utilized during the study, except interview transcripts. Documents shared in this packes are to assist the readers understand and verify the research protocol and data collection process. It contains:</p> <ol> <li>README file</li> <li>Questionnaire for interview pre-selection</li> <li>Participants’ submitted code snippets</li> <li>Code reviews submitted by participants</li> <li>ChatGPT 4.0-Generated code reviews</li> <li>Complete interview guide</li> <li>Member checking feedback data</li> </ol>
Data and code underpinning: "The association of smoking status with SARS-CoV-2 infection, hospitalisation and mortality from COVID-19: A living rapid evidence review with Bayesian meta-analyses (version 12)"
<p>No description provided.</p>
Automating Code Review Activities 2.0 (datasets, models and results)
<p>Resources related by the research work <em>"Automating Code Review Activities 2.0".</em></p> <ul> <li><strong>automating_code_review.zip</strong> contains the material to successfully run our Colab notebooks;</li> <li><strong>dataset.zip </strong>contains all the preprocessed datasets used in our work;</li> <li><strong>generate_prediction.zip </strong>contains the material to successfully generate predictions using a T5 model chekpoint;</li> <li><strong>models.zip</strong> contains the (best) checkpoints of the fine-tuned T5 models;</li> <li><strong>results.zip</strong> contains our results;</li> <li><strong>tokenizer.zip</strong> contains the Sentencepiece model and vocabulary trained on our pre-training dataset.</li> </ul> <p>More information in the replication package of our work: <a href="https://github.com/CodeReviewAutomation/code_review_automation">code_review_autmoation</a></p>
Empirical analysis of security-related code reviews in npm packages
<p>This package contains the dataset used to analyze our RQs in the manuscript.</p> <p>For more information on how to understand the folder structure and dataset, please read the README.md</p>
Dataset: An empirical study on self-admitted technical debt in modern code review
<pre>This data was used in the IST paper "An Empirical Study on Self-Admitted Technical Debt in Modern Code Review". The program to use this data is published in GitHub (https://github.com/Yutaro-Kashiwa/ReviewSATD_RP) When you use this data in your research, please cite the following papers: ``` @article{Kashiwa:IST:2022:SATD_Review, author = {Yutaro Kashiwa and Ryoma Nishikawa and Yasutaka Kamei and Masanari Kondo and Emad Shihab and Ryosuke Sato and Naoyasu Ubayashi}, title = {An empirical study on self-admitted technical debt in modern code review}, journal = {Information and Software Technology}, volume = {146}, pages = {106855}, year = {2022}, url = {https://doi.org/10.1016/j.infsof.2022.106855}, doi = {10.1016/j.infsof.2022.106855} } ``` </pre>
Dataset for the Paper: "Security Defect Detection via Code Review: A Study of the OpenStack and Qt Communities"
<p>This is the dataset for the paper: "Security Defect Detection via Code Review: A Study of the OpenStack and Qt Communities ", including the extracted data and results.</p> <p>The dataset contains the following three folders:</p> <p><strong>1. RQ1</strong>: </p> <ul> <li><strong>Security defect in Nova.xlsx</strong></li> <li><strong>Security defect in Neutron.xlsx</strong></li> <li><strong>Security defect in Qt Base.xlsx</strong></li> <li><strong>Security defect in Qt Creator.xlsx;</strong></li> </ul> <p>The RQ1 folder contains four files corresponding to the four projects (i.e., Nova and Neutron from OpenStack, Qt Base and Qt Creator from Qt), including 539 security-related review comments, in which security defects were identified by the reviewers. These instances were obtained from manual labelling after keyword-based search. The security defect type of these instances are presented to answer RQ1.</p> <p><strong>How to Read the MS Excel files in RQ1:</strong></p> <p>Each of the four MS Excel files in this folder contains 6 sheets for six years from 2017 to 2022. Each sheet has 10 columns for recoding 10 data items, among which the last four data items are used in our study to answer the RQs. We list the data items in the following table.</p> <table> <tbody> <tr> <td><strong>Data Item</strong></td> <td><strong>Description</strong></td> <td><strong>Source</strong></td> </tr> <tr> <td>Keyword</td> <td>The corresponding keyword of the comment.</td> <td>Keyword-based Search</td> </tr> <tr> <td>Code_change_id</td> <td>The code_change_id of the comment.</td> <td>Gerrit</td> </tr> <tr> <td>File</td> <td>The file in which the comment is added.</td> <td>Gerrit</td> </tr> <tr> <td>Patchset</td> <td>The patchset of the comment within the code change.</td> <td>Gerrit</td> </tr> <tr> <td>Line</td> <td>The line number in the file at which the comment is added.</td> <td>Gerrit</td> </tr> <tr> <td>Message</td> <td>The text of the review comment.</td> <td>Gerrit</td> </tr> <tr> <td>Security-related</td> <td>Whether the review comment is security-related (i.e., Yes or No).</td> <td>Labelling</td> </tr> <tr> <td>Security defect type</td> <td>The type of the security defect identified in the comment.</td> <td>Labelling</td> </tr> <tr> <td>Consequence</td> <td>The Consequence of the security defect.</td> <td>Extraction</td> </tr> <tr> <td>Resolution Evidence</td> <td>The information about where the identified security defect was resolved in the code</td> <td>Extraction</td> </tr> </tbody> </table> <p><strong>2. RQ2</strong>: </p> <ul> <li><strong>Extracted data for RQ2.mx22</strong></li> </ul> <p>The RQ2 folder contains the extracted data of 539 security-related review comments in <strong>Extracted data for RQ2.mx22</strong>, which was encoded and analyzed by the MAXQDA tool, investigating the treatment of security defects by developers and reviewers to answer RQ2.</p> <p><strong>3. RQ3</strong>: </p> <ul> <li><strong>Extracted data for RQ3.mx22</strong></li> </ul> <p>The RQ3 folder contains the extracted data of 161 review comments in which identified security defects were not resolved by developers in <strong>Extracted data for RQ3.mx22</strong>. which was also encoded and analyzed by the MAXQDA tool, exploring the causes of not resolving security defects to answer RQ3.</p> <p><strong>Note</strong>: The mx22 can be opened by MAXQDA 22, which are available at <a href="https://www.maxqda.com/">https://www.maxqda.com/</a> for download. You may also use the free trial version of MAXQDA 2022, which is available at <a href="https://www.maxqda.com/trial">https://www.maxqda.com/trial</a> for download.</p>
Replication Package - EvaCRC: Evaluating Code Review Comments
<p>Replication Package of the accepted ESEC/FSE 2023 research paper: "EvaCRC: Evaluating Code Review Comments".</p> <p>Please refer to README.md for more information. </p> <p> </p>
Data and code from: A review of cisco form diversity and lake depth
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Mitigating Turnover with Code Review Recommendation: Balancing Expertise, Workload, and Knowledge Distribution
<p>Data for the paper:</p> <p>Ehsan Mirsaeedi and Peter C. Rigby. 2020. Mitigating Turnover with Code Review Recommendation: Balancing Expertise, Workload, and Knowledge Distribution. In 42nd International Conference on Software Engineering (ICSE ’20), May 23–29, 2020, Seoul, Republic of Korea. ACM, New York, NY, USA, 13 pages. https://doi.org/10.1145/3377811.3380335</p> <p>Code and tools available at</p> <pre>https://doi.org/10.5281/zenodo.3678570</pre>
Replication Package for the paper: "Behind the Intents: An In-depth Empirical Study on Software Refactoring in Modern Code Review"
<p>This is the replication package for the paper: "Behind the Intents: An In-depth Empirical Study on Software Refactoring in Modern Code Review", published at the 17th International Conference on Mining Software Repositories (MSR ’20). </p> <p> </p> <p>It contains all the preliminary and final results of our empirical methodology. We highlight the manual classification of developers' intents behind code changes with refactoring operations. This might be used for further studies on developers' motivations when performing refactoring. </p> <p> </p> <p>Feel free to use any part of this replication package in your study, please cite as:</p> <p>Matheus Paixão, Anderson Uchôa, Ana Carla Bibiano, Daniel Oliveira, Alessandro Garcia, Jens Krinke, and Emilio Arvonio. 2020. Behind the In-tents: An In-depth Empirical Study on Software Refactoring in Modern Code Review. In 17th International Conference on Mining Software Repositories (MSR ’20), October 5–6, 2020, Seoul, Republic of Korea. ACM, New York, NY,USA, 11 pages.</p>
Replication Package for the paper: "How Does Modern Code Review Impact Software Design Degradation? An In-depth Empirical Study"
<p>This is the replication package for the paper: "How Does Modern Code Review Impact Software Design Degradation? An In-depth Empirical Study", published at the 36th International Conference on Software Maintenance and Evolution (ICSME' 20).</p> <p> </p> <p>It contains all the preliminary and final results of our empirical methodology. We highlight the manual classification of design-related and design-unrelated reviews, according to the developers’ intent of improving the structural design of the system. This might be used for further studies on the impact of design discussions on the structural quality of design.</p> <p> </p> <p>Feel free to use any part of this replication package in your study, please cite as:</p> <p>Anderson Uchôa, Caio Barbosa, Willian Oizumi, Publio Blenílio, Rafael Lima, Alessandro Garcia, and Carla Bezerra. How Does Modern Code Review Impact Software Design Degradation? An In-depth Empirical Study. Proceedings of the 36th International Conference on Software Maintenance and Evolution (ICSME), Adelaide, Australia, September 2020.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.