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7 results for “Semantic Versioning”
Data for "On the Practice of Semantic Versioning for Ansible Galaxy Roles: An Empirical Study and a Change Classification Model"
<p>This dataset accompanies a replication package provided for a study on Semantic Versioning for Ansible Galaxy roles.</p> <p>The replication package is available at https://github.com/ROpdebee/ansible_semver_ext_replication</p>
Breaking Bad? Semantic Versioning and Impact of Breaking Changes in Maven Central (Dataset)
<p>The content presented in this repository accompanies the paper "Breaking Bad? Semantic Versioning and Impact of Breaking Changes in Maven Central" authored by Lina Ochoa, Thomas Degueule, Jean-Rémy Falleri, and Jurgen Vinju. The paper was submitted and accepted in the Journal of Empirical Software Engineering (EMSE'21). This study is an external and differentiated replication study of the paper <a href="https://jstvssr.github.io/assets/pdf/semantic-versioning-maven.pdf">"Semantic Versioning and Impact of Breaking Changes in the Maven Repository"</a> presented by Steven Raemaekers, Arie van Deursen, and Joost Visser.</p> <p><strong>Content</strong></p> <ul> <li><strong>README.md: </strong>document with the main description to start exploring the bundle.</li> <li><strong>data.zip: </strong>contains the datasets used within the study. These datasets must be used to get the same results like the ones presented in the article.</li> <li><strong>maven-api-dataset.zip:</strong> contains the code used to generate the datasets and to analyse the obtained results. Check the README.md file within this bundle for more information.</li> </ul> <p><strong>Relevant Links</strong></p> <ul> <li><strong>maven-api-dataset repository:</strong> <a href="https://github.com/tdegueul/maven-api-dataset">https://github.com/tdegueul/maven-api-dataset</a></li> <li><strong>maracas repository:</strong> <a href="https://github.com/crossminer/maracas">https://github.com/crossminer/maracas</a></li> <li><strong>Companion webpage:</strong> <a href="https://crossminer.github.io/maracas/2021/08/16/emse21/">https://crossminer.github.io/maracas/2021/08/16/emse21/</a></li> </ul>
geoscience-semantics: First release--stripped down version
<p>Stripped down version of geoscience semantic models "review"</p>
Benchmark for Pairs of Papers in Semantic Scholar: 1 hop vs. 2-4 hops version 0.0
<p><strong>Benchmark for Pairs of Papers in Semantic Scholar: 1 hop vs. 2-4 hops (version 0.0)</strong></p> <p>There are two files: valid.txt and test.txt; both files use the same format.</p> <p>Columns 2 and 3 are corpus ids from Semantic Scholar.</p> <p>Column 1 is the distance between the two papers in the citation index.</p> <p>Columns 4 and 5 are the bins of the two paper, respectively. The bin is a number between 0 and 100. Papers are sorted by publication date. There are about 2M papers per bin, with the oldest papers in bin 0, and the newest papers in bin 99.</p> <p>Bin 100 is a catch-all for papers with unknown publication dates.</p> <p>head valid.txt</p> <p>1 248518397 1041744 97 51</p> <p>2 248518397 23848439 97 21</p> <p>3 248518397 4235810 97 12</p> <p>4 248518397 82079949 97 11</p> <p>1 3374228 140728989 79 0</p> <p>1 68334187 36144275 58 34</p> <p>2 68334187 7008060 58 4</p> <p>1 205881482 94036919 77 72</p> <p>2 205881482 95069173 77 53</p> <p>3 205881482 53480264 77 52</p> <p>Each row is assigned to a bin, B, where B = max(col4, col5).</p> <p> </p> <p><strong>Task</strong>: the task is to distinguish pairs of papers with distance == 1 from pairs of papers with distance > 1.</p> <p><strong>Test/Train splits</strong>: For all thresholds, 0 <= T_{train} <= 99, train a model on rows in bins between 0 and T_{train} (inclusively). Test these models on rows in all bins 0 <= T_{test} <= 99. Report average accuracy for all combinations of T_{train} and T_{test}.</p> <p> </p> <p>Average Accuracy is defined as: mean(Predict(row) == 1, Gold(row) == 1)</p> <p>The means are computed over rows in a test bin.</p> <p> </p>
What do package dependencies tell us about semantic versioning?
<p>This is the replication package for our paper on dependency constraints and semantic versioning.</p> <p>This package requires Python 3.5+, and all the dependencies listed in *requirements.txt*. They can be automatically installed using *pip install -r requirements.txt*.</p> <p>The figures and results that are contained in the paper could be obtained by running the corresponding notebooks with Jupyter (*jupyter lab* at the root of this package).</p> <p>Here is a short description of what is contained in this package:</p> <p> * Raw datasets (*data-raw* folder)<br> This folder "should" contain the raw data. Because of their size, and because we provide the required files in the *data* folder, you need to download *Libraries.io-open-data-1.2.0.tar.gz* from libraries.io. Extract *versions.csv* and *dependencies.csv* from this archive, and run the *convert.py* script with Python. The script will extract the data related to the four considered ecosystems into *{ecosystem}-(versions|dependencies).csv.gz*.<br> <br> * Datasets (*data* folder)<br> This folder contains the data that are required for the analyses. They are provided in this replication package, but can be automatically generated from the ones provided in *data-raw* folder by running the *versions.py* and *dependencies.py* scripts. These scripts collect and identify dependency constraints.<br> <br> * Notebooks (*notebooks* folder)<br> This folder contains the notebooks that were used to generate all the results and figures of the paper. File *Constraint differences.ipynb* contains examples of constraints and the corresponding equivalent intervals. The main notebook is *Semver compliance.ipynb* and contains all the necessary material.<br> <br> * Python modules (*constraints* folder)<br> This folder contains the necessary Python modules to run "everything". File *parser.py* contains the 4 dependency constraint parsers. File *versions.py* defines data model. File *constraints.py* contains routines to identify constraint types. The *tool.py* script is a prototype of the tool explained in the paper (and should be executed from the root directory, e.g. *python -m constraints.tool*). All these modules/scripts/tools depend on *python-interval* (see *requirements.txt*). The three remaining files (*test_constraints.py*, *test_parser.py* and *test_versions.py*) contain unit tests. They are expected to be executed with *pytest*.</p>
Artifact For A Large Scale Analysis of Semantic Versioning in NPM
<p>This is the artifact for: A Large Scale Analysis of Semantic Versioning in NPM.</p> <p>The artifact contains:</p> <ul> <li>A full scrape of all metadata from NPM (package / version information, dependencies, etc.) as of October 31, 2022.</li> <li>A copy of our code, which includes the software for scraping metadata and package tarball (code) data, as well as all analysis scripts that are needed to replicate the figures from the paper.</li> </ul>
Replication Package for "On the Practice of Semantic Versioning for Ansible Galaxy Roles: An Empirical Study and a Change Classification Model"
<p>Replication package for our analysis of Semantic Versioning in Ansible Galaxy role repositories.</p> <p>This replication package consists of three parts:</p> <ul> <li> <p>Classification Model: Contains Jupyter notebooks used to train and evaluate a Random Forest classification model based on structural features. Training and evaluation data is included.</p> </li> <li> <p>Quantitative Notebooks: Contains Jupyter notebooks used to perform quantitative analyses of versions and changes.</p> </li> <li> <p>data: CSV files of the data used in the Quantitative Notebooks, and the source data for the classification model. Should be downloaded separately fromthe classification model. Should be downloaded separately from <a href="https://doi.org/10.5281/zenodo.4991955">https://doi.org/10.5281/zenodo.4991955</a>.</p> </li> </ul> <p>The data is under the Creative Commons Attribution Share-Alike 4.0 license. The source code is under the GNU General Public License.</p>
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