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Source code analysis dataset
The data in this article pair source code with three artifacts from 108,568 projects downloaded from Github that have a redistributable license and at least 10 stars. The first set of pairs connects snippets of source code in C, C++, Java, and Python with their corresponding comments, which are extr...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6859235/ https://www.ncbi.nlm.nih.gov/pubmed/31763386 http://dx.doi.org/10.1016/j.dib.2019.104712 |
_version_ | 1783471089246011392 |
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author | Gelman, Ben Obayomi, Banjo Moore, Jessica Slater, David |
author_facet | Gelman, Ben Obayomi, Banjo Moore, Jessica Slater, David |
author_sort | Gelman, Ben |
collection | PubMed |
description | The data in this article pair source code with three artifacts from 108,568 projects downloaded from Github that have a redistributable license and at least 10 stars. The first set of pairs connects snippets of source code in C, C++, Java, and Python with their corresponding comments, which are extracted using Doxygen. The second set of pairs connects raw C and C++ source code repositories with the build artifacts of that code, which are obtained by running the make command. The last set of pairs connects raw C and C++ source code repositories with potential code vulnerabilities, which are determined by running the Infer static analyzer. The code and comment pairs can be used for tasks such as predicting comments or creating natural language descriptions of code. The code and build artifact pairs can be used for tasks such as reverse engineering or improving intermediate representations of code from decompiled binaries. The code and static analyzer pairs can be used for tasks such as machine learning approaches to vulnerability discovery. |
format | Online Article Text |
id | pubmed-6859235 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-68592352019-11-22 Source code analysis dataset Gelman, Ben Obayomi, Banjo Moore, Jessica Slater, David Data Brief Computer Science The data in this article pair source code with three artifacts from 108,568 projects downloaded from Github that have a redistributable license and at least 10 stars. The first set of pairs connects snippets of source code in C, C++, Java, and Python with their corresponding comments, which are extracted using Doxygen. The second set of pairs connects raw C and C++ source code repositories with the build artifacts of that code, which are obtained by running the make command. The last set of pairs connects raw C and C++ source code repositories with potential code vulnerabilities, which are determined by running the Infer static analyzer. The code and comment pairs can be used for tasks such as predicting comments or creating natural language descriptions of code. The code and build artifact pairs can be used for tasks such as reverse engineering or improving intermediate representations of code from decompiled binaries. The code and static analyzer pairs can be used for tasks such as machine learning approaches to vulnerability discovery. Elsevier 2019-10-24 /pmc/articles/PMC6859235/ /pubmed/31763386 http://dx.doi.org/10.1016/j.dib.2019.104712 Text en © 2019 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Computer Science Gelman, Ben Obayomi, Banjo Moore, Jessica Slater, David Source code analysis dataset |
title | Source code analysis dataset |
title_full | Source code analysis dataset |
title_fullStr | Source code analysis dataset |
title_full_unstemmed | Source code analysis dataset |
title_short | Source code analysis dataset |
title_sort | source code analysis dataset |
topic | Computer Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6859235/ https://www.ncbi.nlm.nih.gov/pubmed/31763386 http://dx.doi.org/10.1016/j.dib.2019.104712 |
work_keys_str_mv | AT gelmanben sourcecodeanalysisdataset AT obayomibanjo sourcecodeanalysisdataset AT moorejessica sourcecodeanalysisdataset AT slaterdavid sourcecodeanalysisdataset |