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26 results for “Code Comment”
Figure data and code used in Technical comment on "Fairness considerations in global mitigation investments"
<p>The package contains the data and code to create the figure in the associated technical comment in Science published at <a href="https://www.science.org/doi/10.1126/science.adg5893">https://www.science.org/doi/10.1126/science.adg5893</a></p>
MALAYALAM LANGUAGE (MIX CODE) Recipe channels Youtube Comments
<p>The dataset used for performing Text Classification is a combination of two different datasets scrapped from the comment section of two Youtube channels namely "Veen's Curryworld" and "Lekshmi Nair". The data is extracted using the YouTube API and contains two atrributes namely “text” and “label” where the former contains the comments and later contains the corresponding label. The comments are classified into 7 labels:</p> <p> </p> <p>- Label 1: Gratitude</p> <p>- Label 2: About the recipe</p> <p>- Label 3: About the video</p> <p>- Label 4: Praising</p> <p>- Label 5: Hybrid</p> <p>- Label 6: Undefined</p> <p>- Label 7: Suggestions and Queries</p> <p> </p> <p>The number of instances in each category is listed below:</p> <p> </p> <p>- Label 1: 484</p> <p>- Label 2: 396</p> <p>- Label 3: 300</p> <p>- Label 4: 362</p> <p>- Label 5: 249</p> <p>- Label 6: 2062</p> <p>- Label 7: 438</p>
retracted and commented papers related to non-coding RNA
<p>This is the dataset related to the research integrity analysis paper of non-coding RNA.</p> <p>The retracted and commented papers related to non-coding RNA and their publication years and citations are presented. It have the following sheets:</p> <ul> <li>Figure 1 <ul> <li>number of publications, retraction and comments of each year</li> </ul> </li> <li>Figure 2 <ul> <li>Percentage of retractions and Percentage of papers for major Prefixes/Keywords</li> </ul> </li> <li>retraction <ul> <li>retracted papers with their citation and related research integrity issue</li> </ul> </li> <li>comment <ul> <li>commented papers with thier citation and pubpeer url</li> </ul> </li> </ul>
Dataset of smell comments in Code Review Discussions
<p>The raw data contains 104,321 code review comments, with records between January 2014 to August 2023. After a keyword search, 18,850 comments were manually analyzed by 26 developers. The analyzed data resulted in 3,798 smell comments. This meticulously curated dataset was used to collect 4,058 more smell comments through semantic search, comprising a total of 7,856 smell comments, which represents 13,27% of the original data collected. </p>
Classifying code comments in Java software systems. Appendix
<p>This dataset refers to "Classifying code comments in Java software systems" paper. It contains a large sample of manual classified code comments. More in deep, code comments are a key software component containing information about the underlying implementation. Several studies have shown that code comments enhance the readability of the code. Nevertheless, not all the comments have the same goal and target audience. In this paper, we investigate how 14 diverse Java open and closed source software projects use code comments, with the aim of understanding their purpose. Through our analysis, we produce a taxonomy of source code comments; subsequently, we investigate how often each category occur by manually classifying more than 40,000 lines of code comments from the aforementioned projects. In addition, we investigate how to automatically classify code comments at line level into our taxonomy using machine learning; initial results are promising and suggest that an accurate classification is within reach, even when training the machine learner on projects different than the target one. Preprint: http://dx.doi.org/10.1007/s10664-019-09694-w</p>
Replication package for the paper "What do Developers Discuss about Code Comments"
<pre><code class="language-markdown"># RP-commenting-practices-multiple-sources Replication package for the paper "What do Developers Discuss about Code Comments?" ## Structure ``` Appendix.pdf Tags-topics.md Stack-exchange-query.md RQ1/ LDA_input/ combined-so-quora-mallet-metadata.csv topic-input.mallet LDA_output/ Mallet/ output_csv/ docs-in-topics.csv topic-words.csv topics-in-docs.csv topics-metadata.csv output_html/ all_topics.html Docs/ Topics/ RQ2/ datasource_rawdata/ quora.csv stackoverflow.csv manual_analysis_output/ stackoverflow_quora_taxonomy.xlsx ``` ## Contents of the Replication Package --- - **Appendix.pdf**- Appendix of the paper containing supplement tables - **Tags-topics.md** tags selected from Stack overflow and topics selected from Quora for the study (RQ1 & RQ2) - **Stack-exchange-query.md** the query interface used to extract the posts from stack exchnage explorer. - **RQ1/** - contains the data used to answer RQ1 - **LDA_input/** - input data used for LDA analysis - `combined-so-quora-mallet-metadata.csv` - Stack overflow and Quora questions used to perform LDA analysis - `topic-input.mallet` - input file to the mallet tool - **LDA_output/** - **Mallet/** - contains the LDA output generated by MALLET tool - **output_csv/** - `docs-in-topics.csv` - documents per topic - `topic-words.csv` - most relevant topic words - `topics-in-docs.csv` - topic probability per document - `topics-metadata.csv` - metadata per document and topic probability - **output_html/** - Browsable results of mallet output - `all_topics.html` - `Docs/` - `Topics/` - **RQ2/** - contains the data used to answer RQ2 - **datasource_rawdata/** - contains the raw data for each source - `quora.csv` - contains the processed dataset (like removing html tags). To know more about the preprocessing steps, please refer to the reproducibility section in the paper. The data is preprocessed using Makar tool. - `stackoverflow.csv` - contains the processed stackoverflow dataset. To know more about the preprocessing steps, please refer to the reproducibility section in the paper. The data is preprocessed using Makar tool. - **manual_analysis_output/** - `stackoverflow_quora_taxonomy.xlsx` - contains the classified dataset of stackoverflow and quora and description of taxonomy. - `Taxonomy` - contains the description of the first dimension and second dimension categories. Second dimension categories are further divided into levels, separated by `|` symbol. - `stackoverflow-posts` - the questions are labelled relevant or irrelevant and categorized into the first dimension and second dimension categories. - `quota-posts` - the questions are labelled relevant or irrelevant and categorized into the first dimension and second dimension categories. --- </code></pre> <p> </p>
Code Comment Git Diff
<p>Course - SOEN 691<br><br>Updating Method-Level Comments using Generative AI</p> <p>This study explores the feasibility of using GitDiff to automatically update method-level comments with GenAI. For this, we leverage the information contained in GitDiff, i.e., a patch representing the changes between two states of the file, to help infer the modifications made to the code for updating the method-level comments. For our study as depicted by below figure, we evaluate the following two GenAI architectures:</p>
The Influence of Code Comments on the Perceived Helpfulness of Stack Overflow Posts (Supplementary Material)
<p>Question-and-answer platforms such as Stack Overflow have become an important way for software developers to share and retrieve knowledge. However, reusing poorly understood code can lead to serious problems, such as bugs or security vulnerabilities. To better understand the role of code comments in the perceived helpfulness of answers on Stack Overflow, we conducted an online experiment simulating a Stack Overflow environment (n=91). The results indicate that block comments in particular were perceived as significantly more helpful than uncommented source code, especially by novices. Novices also found code snippets with block comments more helpful than those with inline comments. Interestingly, other surface features, such as the position of an answer and its usefulness score, were considered less important. The content of Stack Overflow has been a major source for training large language models. Although AI-based coding assistants such as GitHub Copilot, which are based on these models, might change the way Stack Overflow is used, our findings have implications beyond this specific platform. First, the findings of this study may help improve the ongoing relevance of community-driven platforms like Stack Overflow that provide human advice and explanations of code solutions to complement AI-based support for programmers. Second, since chat-based tools can be prompted to generate code in different ways, knowing which properties influence the perceived helpfulness of code snippets is beneficial.</p>
poojaruhal/RP-commenting-practices-social-media: RP-commenting-practices-social-media: RP_TOSEM_2020 v.1.0.1 Second release of of the replication Package for the paper "What do Developers Discuss about Code Comment Conventions on Social Media"
<p>RP-commenting-practices-social-media</p> <p>Replication Package for the paper "What do Developers Discuss about Code Comment Conventions on Social Media?"</p> <p>Structure</p> <pre><code>Paper-presenation.pdf Makar_tool/ Data/ stackoverfow_questions_with_answers_by_tags.csv stackoverfow_tags_metrics.csv apache_mailing_list.csv mailing_lists_ASF_@dev_@users_1.csv mailing_lists_ASF_@dev_@users_2.csv quora.csv sample_stackoverfow_questions_with_answers_by_tags.csv Schemas/ apache_mailing_lists.json quora.json stackoverfow_questions_answers_by_tag.json stackoverfow_tag_count.json stackoverfow_tag_metrics.json RQ1/ LDA_input/ stackoverfow_raw_dataset.csv LDA_output/ Mallet/ output_csv/ docs-in-topics.csv topic-words.csv topics-in-docs.csv topics-metadata.csv output_html/ all_topics.html Docs/ Topics/ RQ2/ datasource_rawdata/ mailing_lists_selection_criteria.csv quora.csv stackoverflow.csv manual_analysis_output/ stackoverflow_quora_taxonomy.xlsx </code></pre> <p>Contents of the Replication Package</p> <p><strong>Paper-presenation.pdf</strong> presents the highlights of the work in a presenation.</p> <p><strong>Makar_tool/</strong> contains the data processed using the tool for the study</p> <ul> <li> <p><strong>Data/</strong></p> <ul> <li><code>stackoverfow_questions_with_answers_by_tags.csv</code> - all stackoverflow questions used in the study as stored in Makar <ul> <li><code>stackoverfow_tags_metrics.csv</code> - all data containing the calculations done for stackoverflow tag selection</li> <li><code>apache_mailing_list.csv</code> - statistically significant sample of <code>mailing_lists_ASF_@dev_@users_1.csv</code> and <code>mailing_lists_ASF_@dev_@users_2.csv</code> used in the study</li> <li><code>mailing_lists_ASF_@dev_@users_1.csv</code> - mailing list data used in the study as stored in Makar (part 1)</li> <li><code>mailing_lists_ASF_@dev_@users_2.csv</code> - mailing list data used in the study as stored in Makar (part 2)</li> <li><code>quora.csv</code> - all quora questions used in the study as stored in Makar</li> <li><code>sample_stackoverfow_questions_with_answers_by_tags</code> - statistically significant sample of <code>stackoverfow_questions_with_answers_by_tags.csv</code> used in the study</li> </ul> </li> </ul> </li> <li> <p><strong>Schemas/</strong></p> <ul> <li><code>apache_mailing_lists.json</code> - data schema used in Makar to store mailing list data</li> <li><code>quora.json</code> - data schema used in Makar to store quora data</li> <li><code>stackoverfow_questions_answers_by_tag.json</code> - data schema used in Makar to store stackoverflow questions data</li> <li><code>stackoverfow_tag_count.json</code> - data schema used in Makar to lookup number of questions per tag available in stackoverflow</li> <li><code>stackoverfow_tag_metrics.json</code> - data schema used in Makar to stackoverflow tag metrics data</li> </ul> </li> <li> <p><strong>RQ1/</strong> - contains the data used to answer RQ1</p> <ul> <li><strong>LDA_input/</strong> - input data used for LDA analysis <ul> <li><code>stackoverfow_raw_dataset.csv</code> - stackoverflow questions used to perform LDA analysis</li> </ul> </li> <li><strong>LDA_output/</strong> <ul> <li><strong>Mallet/</strong> - contains the LDA output generated by MALLET tool <ul> <li><strong>output_csv/</strong> <ul> <li><code>docs-in-topics.csv</code> - documents per topic</li> <li><code>topic-words.csv</code> - most relevant topic words</li> <li><code>topics-in-docs.csv</code> - topic probability per document</li> <li><code>topics-metadata.csv</code> - metadata per document and topic probability <ul> <li><strong>output_html/</strong> - Browsable results of mallet output</li> </ul> </li> <li><code>all_topics.html</code></li> <li><code>Docs/</code></li> <li><code>Topics/</code></li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> <li> <p><strong>RQ2/</strong> - contains the data used to answer RQ2</p> <ul> <li><strong>datasource_rawdata/</strong> - contains the raw data for each source <ul> <li><code>mailing_lists_selection_criteria.csv</code> - criteria used to select mailing_lists.</li> <li><code>quora.csv</code> - contains the processed dataset (like removing HTML tags). To know more about the preprocessing steps, please refer to the reproducibility section in the paper. The data is preprocessed using Makar tool.</li> <li><code>stackoverflow.csv</code> - contains the processed stackoverflow dataset. To know more about the preprocessing steps, please refer to the reproducibility section in the paper. The data is preprocessed using Makar tool.</li> </ul> </li> <li><strong>manual_analysis_output/</strong> <ul> <li><code>stackoverflow_quora_taxonomy.xlsx</code> - contains the classified dataset of stackoverflow and quora and description of taxonomy. <ul> <li><code>Taxonomy</code> - contains the description of the first dimension and second dimension categories. Second dimension categories are further divided into levels, separated by <code>|</code> symbol.</li> <li><code>stackoverflow-posts</code> - the questions are labelled relevant or irrelevant and categorized into the first dimension and second dimension categories. <ul> <li><code>quota-posts</code> - the questions are labelled relevant or irrelevant and categorized into the first dimension and second dimension categories.</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul>
Multilingual paired code and comment changes
<p>Dataset used for the master's thesis "LLMs for Code Comment Consistency." Covers the languages Go, Java, JavaScript, TypeScript, and Python. All data is mined from permissively-licensed GitHub public projects.</p><p>This dataset consists of pairs of function/method code blocks and their documentation comments, before and after commits.<br>Examples are labeled 0 if the comment was not changed before and after, and 1 if the comment was changed. For the purpose of comment consistency, that means a 1-labeled example has an <i>old comment</i> that is inconsistent with the <i>new code</i>.<br>If you're training a <strong>code summarization</strong> or <strong>comment generation</strong> task, then of course ignore the classification label.</p><ul><li>All-22k contains the training, validation, and test set used in the models trained in the paper. The examples are balanced by language and between the positive and negative classes. Any code repository is only present in one of these sets.</li></ul>
Snippet Comment Generation Based on Code Context Expansion
<p>Dataset of the TOSEM accepted paper "Snippet Comment Generation Based on Code Context Expansion".</p> <p>Version 2 adds dictionary files.</p>
Comments On Comment: Where Code Review and Documentation Meet
<p>Comments on Comments dataset from the paper titled 'Comments on Comments: Where Code Review and Documentation Meet'</p> <p>DOI of the paper: <a href="https://doi.org/10.1145/3524842.3528475">https://doi.org/10.1145/3524842.3528475</a></p>
Dataset of "Inferring Fine-grained Traceability Links between Javadoc Comments and JUnit Test Code"
<p>Dataset of "Inferring Fine-grained Traceability Links between Javadoc Comments and JUnit Test Code"</p> <p>- study object, true link, sentence, test code snippet, experiment result</p>
Research Artifact: 9.6 Million Links in Source Code Comments
<p>This is a research artifact for the ICSE'19 paper <strong>9.6 Million Links in Source Code Comments: Purpose, Evolution, and Decay</strong>. This artifact is a data repository including all 9,654,702 links associated with the information of languages and comment location (GitHub links including account names, repository names, commit hashes, file paths, and line numbers). The purpose of this artifact is enabling researchers to replicate our mixed-methods quantitative results of the paper, and to reuse our around 9.6 million links in source code comments for further software engineering research.</p>
Supporting data and code for "Benthic habitats do show a significant latitudinal diversity gradient: a comment on Kinlock et al. (2018)".
<p>R code and dataset for: Menegotto A., Kurtz M.N. & Lana P.C. 2019. Benthic habitats do show a significant latitudinal diversity gradient: a comment on Kinlock et al. (2018). Global Ecology and Biogeography, 28, 1712-1717.</p>
Replication Package of "Optimizing Datasets for Code Summarization: Is Code-Comment Coherence Enough?"
<p>This repository contains the datasets and the scripts to replicate our work "Optimizing Datasets for Code Summarization: Is Code-Comment Coherence Enough?".</p>
Dataset of "Inferring Fine-grained Traceability Links between Javadoc Comments and JUnit Test Code"
<p>Dataset of "Inferring Fine-grained Traceability Links between Javadoc Comments and JUnit Test Code"</p>
Replication Package of "Code Generative Techniques and TODO Comments: Friends or Foes? An Evaluation of GitHub Copilot"
<p>The data and scripts contributed by the efforts in "Code Generative Techniques and TODO Comments: Friends or Foes? An Evaluation of GitHub Copilot".</p>
PENTACET data - 23 Million Contextual Code Comments and 500,000 SATD comments
<p>PENTACET is a large Curated Contextual Code Comments per Contributor and the most extensive SATD data. We mine 9,096 Open Source Software Java projects with a total of 435 million LOC. The outcome is dataset with 23 million code comments, preceding and succeeding source code context for each comment, and more than 500,000 comments labeled as SATD, including both ‘Easy to Find’ and ‘Hard to Find’ SATD.</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>
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
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Annotated Behaviour and Observability Dataset (ABODe)
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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.