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15 results for “commit messages”
What Makes a Good Commit Message?
<p><strong>What-Makes-a-Good-Commit-Message?</strong></p> <p>This repository contains the main data and scripts used in "What Makes a Good Commit Message"</p> <p><strong>Notes:</strong></p> <p><em>The code will be maintained on <a href="https://github.com/WhatMakesAGoodCM/What-Makes-a-Good-Commit-Message.git">GitHub</a>. If you have any questions, please feel free to mention issue.</em></p> <p><strong>Dataset:</strong></p> <p>The folder dataset contains the following files.</p> <ul> <li> <p>literature survery.xlsx</p> <ul> <li>It contains the data of 46 relevant literature reviewed in this study (Section 3.2).</li> </ul> </li> <li> <p>Questionnaire.pdf</p> <ul> <li>It is the questionnaire sent to experienced contributors.</li> <li>It contains three questions.</li> <li>It also contains an example of the initialized questionnaire.</li> </ul> </li> <li>Frequency.pdf <ul> <li>It describes the number and proportion of occurrences of a category/subcategory.</li> </ul> </li> <li> <p>posts list.xlsx</p> <ul> <li>It contains all posts we studied in Sec. 3.2.</li> </ul> </li> <li> <p>sampled messages.csv</p> <ul> <li>It contains meta-information of 1649 labeled commit messages.</li> <li>label = 0 means a commit message contains "Why and What".</li> <li>label = 1 means a commit message contains "Neither Why nor What".</li> <li>label = 2 means a commit message contains "No What".</li> <li>label = 3 means a commit message contains "No Why".</li> <li>if_mulit_commit = 1 means a commit is non-atomic.</li> <li>new_message1 means a message after preprocessing.</li> </ul> </li> <li> <p>maintenance type and expression way.xlsx</p> <ul> <li>It contains the results of our RQ2: the expression ways of Why and What, as well as links to maintenance types.</li> </ul> </li> </ul> <p><strong>CommitMessage (Scripts):</strong></p> <p>The folder contains the following scripts files.</p> <ul> <li> <p>Preprocessor</p> <ul> <li>It contains the preprocessing of commit messages, including the replacement of token in the message, etc.</li> </ul> </li> <li> <p>ModelTraining</p> <ul> <li>It contains the code for our model training, that is, the implementation of different classification techniques.</li> </ul> </li> </ul>
Code diffs and commit messages from top1000-2000 Java projects in GitHub
<p>This dataset contains pairs <code changes, commit message> from top 1000-2000 Java projects in GitHub via <a href="https://developer.github.com/v3/">GitHub Developer API</a>.</p> <p>The structure of files is as follows:</p> <ul> <li><repository-id>.<repository-name>/ # Each directory contains commits of one project. <ul> <li><commit-id>.<commit-sha>/ # Each directory contains one commit information, and<br> # the commit-sha is the primary key of this commit in GitHub. <br> # The children in this directory have 3 types: the file<br> # "commit_msg", multiple directories "<filename>", and the file<br> # "error". <ul> <li>commit_msg # This file contains one line, representing the commit<br> # message. </li> <li><filename>/ # The name of this directory is the changed file name. <ul> <li>patch # The code diffs of this <filename>, describing<br> # which lines are added and which lines are<br> # removed with some same lines context.</li> </ul> </li> <li><filename>/ <ul> <li>patch</li> </ul> </li> <li>error # This file contains the error message when crawling<br> # from GitHub. If this file exists, the directories<br> # <filename> will not exist. </li> </ul> </li> </ul> </li> </ul>
MCMD | Multi-programming-language Commit Message Dataset
<p>A large-scale dataset in multi-programming languages and with rich information.</p> <p>This dataset is proposed in the paper "<a href="https://ieeexplore.ieee.org/document/9609189">On the Evaluation of Commit Message Generation Models: An Experimental Study</a>" accepted to ICSME 2021 and "<a href="https://link.springer.com/article/10.1007/s10664-022-10219-1">A large-scale empirical study of commit message generation: models, datasets and evaluation</a>" accepted to EMSE 2022.</p> <p><strong>Welcome to use our dataset, <a href="https://github.com/DeepSoftwareAnalytics/CommitMsgEmpirical/blob/main/dataset">MCMD</a>, and the <a href="https://github.com/DeepSoftwareAnalytics/CommitMsgEmpirical/blob/main/metrics">evaluation scripts</a> to test the performance of the commit message generation!</strong></p> <p>Citations for these two works can be found <a href="https://github.com/DeepSoftwareAnalytics/CommitMsgEmpirical#Citation">here</a>.</p>
CommitChronicle dataset from the paper "From Commit Message Generation to History-Aware Commit Message Completion", ASE 2023
<pre>This is the CommitChronicle dataset for commit message generation (and/or completion), introduced in the paper "From Commit Message Generation to History-Aware Commit Message Completion", ASE 2023. For further details, see the attached README.md. <strong>Note.</strong> Also available on HuggingFace Hub: <a href="https://huggingface.co/datasets/JetBrains-Research/commit-chronicle">JetBrains-Research/commit-chronicle</a></pre>
RACE: Retrieval-Augmented Commit Message Generation
<p>The dataset is used to reproduce our work "RACE: Retrieval-Augmented Commit Message Generation".</p>
Cross-Language Vulnerability Dataset with File Changes and Commit Messages
<p>Cross-Language Vulnerability Dataset with File Changes and Commit Messages</p>
Replication Package of the Paper: "Using Large Language Models for Commit Message Generation: A Preliminary Study"
<p>This replication package contains the evaluation data and script files used in the paper "Using Large Language Models for Commit Message Generation: A Preliminary Study". We provide below a brief description of each folder:</p><ul><li><strong>experiment_data/human_evaluation</strong>: Human evaluation results from two participants.</li><li><strong>experiment_data/msg</strong>: Generated commit messages of each method (baselines & LLMs) and human-written commit messages.</li><li><strong>script</strong>: Evaluation metric script, parallel inference script (for using OpenAI API).</li></ul>
Acceptance and Commitment Therapy Delivered by Automated Software Messaging
ClinicalTrials.gov study NCT04329897. IPD Sharing: NO. Countries: 1. Publications: 6.
Unveiling Developers' Feelings: A Benchmarking Study on Emotion and Sentiment Analysis of Software Commit Messages
Open the record for dataset details and reuse information.
Context-Aware Retrieval-based Deep Commit Message Generation Dataset
<p>dataset</p>
Instant Message-delivered Personalised Acceptance and Commitment Therapy (ACT) for Neuropsychiatric Symptoms in Persons With Mild Cognitive Impairment
ClinicalTrials.gov study NCT07332624. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Self-Admitted Technical Debt in Commit Messages: Comparing Java, Python, and R
<p><strong><span>The folder organization and datasets within each are as follows:</span></strong></p> <p><strong><span>Collection Folder:</span></strong><span> the original dataset that we scraped is placed. We have removed the user names and email addresses to keep the users’ privacy. </span><strong><span>RQ1 Folder</span></strong><span> has three subfolders: </span></p> <p><span><span>❖<span> </span></span></span><strong><span>Manual Training:</span></strong><span> The initial manually labeled data we used to initially train the classifiers is included. Note that columns A-O in this dataset are all extracted from GitHub’s API. Column O (heading “message”) is the commit message itself. The following columns P and Q (heading “author_a” and “author_b”) are the final classification (upon which the Cohen Kappa was calculated). Column R (heading “notes”) contains some commentaries on specific cases that may be meaningful.</span></p> <p><span><span>❖<span> </span></span></span><strong><span>Predicted:</span></strong><span> The results of the automatic classifiers (both 1st and 2nd round) are included. The additional columns are generated by the classifiers.</span></p> <p><span><span>❖<span> </span></span></span><strong><span>Verifications</span></strong><span> contain the manually labeled data that we used as 1st and 2nd verification rounds. This is a simplified dataset with the commit’s sha and the parsed message. The authors classified columns E and F independently and individually. The labels stated here are those that the authors agreed to (without having access to column D). Note that column D was added afterward by sha-matching by another author to calculate the Cohen Kappa. The yellow rows are those with disagreements.</span></p> <p><span> </span><strong><span>RQ2_RQ3 Folder</span></strong><span> contains the manually labeled dataset for RQ2 and RQ3 (SATD Types and Activities). </span></p> <p><span>NOTE: Kindly note that many messages or classifications are <em>multiline</em>. This means that the cells have to be expanded to be capable of reading all text included in a cell.</span></p>
Kevin: Knowledge-Enhanced Conventional Commit Message Generation
<div> <h1>Introduction</h1> <br> <div>We purpose a novel conventional commit message generation framework Kevin. In this directory, we give all code, dataset, and experiment results.</div> <br> <div>We use the dataset MCMD to evaluate our model, and filter some meaningless data on [`it`](dataset/MCMD_filter.zip).</div> <br> <div>Now we introduce the purpose of each folder:</div> <br> <div>1. `code`: this is the implement of our approach.</div> <div> 1. `commit_type_classification`: this is implement of our approach in commit type classification, and there are detailed documents in this folder.</div> <div> 2. `commit_description_classification`: this is implement of our approach in commit description generation, and there are detailed documents in this folder.</div> <div>2. `dataset`: this is the dataset we used to evaluate our approach, `MCMD_filter.zip` contains the filtered version of the dataset MCDM, `metrics.zip` includes 10 code change metrics used in commit type classification.</div> <div>3. `empirical_study`: this is the result of empirical study abour the correlation between code change situations and 8 commit types.</div> <div>4. `experiment_results`: this is all original experiment results of Kevin and six state-of-the-art approaches.</div> <div> 1. `RQ1 Overall Effectiveness`: this is for experiment RQ1.</div> <div> 2. `RQ2 Ablation Study`: this is for experiment RQ2.</div> <div> 3. `RQ3 Human Evaluation`: this is for experiment RQ3.</div> <div> 4. `metrics`: this is the implement of automated evaluation metrics BLEU, ROUGE-L, and METEOR.</div> <div>5. `checkpoints`: this directory includes five checkpoints of commit description generation model across five languages.</div> <br> <div># Usage</div> <br> <div>Before run the model, unzip all dataset in directory `dataset/`.</div> <br> <div>``` shell</div> <div>unzip MCMD_filter.zip</div> <div>unzip metrics.zip</div> <div>```</div> <br> <div>In the directory `code/commit_type_classification` and `code/commit_description_generation`, there are detailed document for install dependencies and run the model.</div> <br> <div>To run commit type classification model,</div> <br> <div>``` shell</div> <div>python classification.py</div> <div>```</div> <br> <div>If extract features from original dataset, configure parameters in function `main()` of `code/commit_type_classification/get_feature.py`, and</div> <div>``` shell</div> <div>python get_feature.py</div> <div>python classification.py</div> <div>```</div> <br> <div>To run commit description generation model, prepare the data modify the parameters in function `main()`.</div> <div>``` shell</div> <div>python prepare.py</div> <div>```</div> <br> <div>configure parmeters in directory `code/commit_description_generation/conf/*.py`, includes the path of dataset, model, and hyperparameters.</div> <div>``` shell</div> <div>CUDA_VISIBLE_DEVICES=0 python eval.py +model=codet5</div> <div>```</div> <br> <div>If train the model,</div> <div>``` shell</div> <div>CUDA_VISIBLE_DEVICES=0 python train.py +model=codet5</div> <div>```</div> </div>
Conventional commit message of dataset MCMD
<div> <h1>Introduction</h1> <br> <div>This directory includes the dataset MCMD and its conventional subset.</div> <br> <div><code>MCMD.zip</code> is the filtered dataset MCMD.</div> <div><code>MCMD_conventional.zip</code> is the conventional subset of the dataset MCMD.</div> </div>
What Makes a Good Commit Message?
<p><strong>What-Makes-a-Good-Commit-Message?</strong></p> <p>This repository contains the main data and scripts used in "What Makes a Good Commit Message"</p> <p><strong>Dataset:</strong></p> <p>The folder dataset contains the following files.</p> <ul> <li> <p>literature survery.xlsx</p> <ul> <li>It contains the data of 46 relevant literature reviewed in this study (Section 3.2).</li> </ul> </li> <li> <p>Questionnaire.pdf</p> <ul> <li>It is the questionnaire sent to experienced contributors.</li> <li>It contains three questions.</li> <li>It also contains an example of the initialized questionnaire.</li> </ul> </li> <li>Frequency.pdf <ul> <li>It describes the number and proportion of occurrences of a category/subcategory.</li> </ul> </li> <li> <p>posts list.xlsx</p> <ul> <li>It contains all posts we studied in Sec. 3.2.</li> </ul> </li> <li> <p>sampled messages.csv</p> <ul> <li>It contains meta-information of 1649 labeled commit messages.</li> <li>label = 0 means a commit message contains "Why and What".</li> <li>label = 1 means a commit message contains "Neither Why nor What".</li> <li>label = 2 means a commit message contains "No What".</li> <li>label = 3 means a commit message contains "No Why".</li> <li>if_mulit_commit = 1 means a commit is non-atomic.</li> <li>new_message1 means a message after preprocessing.</li> </ul> </li> <li> <p>maintenance type and expression way.xlsx</p> <ul> <li>It contains the results of our RQ2: the expression ways of Why and What, as well as links to maintenance types.</li> </ul> </li> </ul> <p><strong>CommitMessage (Scripts):</strong></p> <p>The folder contains the following scripts files.</p> <ul> <li> <p>Preprocessor</p> <ul> <li>It contains the preprocessing of commit messages, including the replacement of token in the message, etc.</li> </ul> </li> <li> <p>ModelTraining</p> <ul> <li>It contains the code for our model training, that is, the implementation of different classification techniques.</li> </ul> </li> </ul>
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