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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.18251 |
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| _version_ | 1866909549927071744 |
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| author | Gautam, Kuldeep VenkataKeerthy, S. Upadrasta, Ramakrishna |
| author_facet | Gautam, Kuldeep VenkataKeerthy, S. Upadrasta, Ramakrishna |
| contents | In recent years, a lot of technological advances in computer science have aided software programmers to create innovative and real-time user-friendly software. With the creation of the software and the urging interest of people to learn to write software, there is a large collection of source codes that can be found on the web, also known as Big Code, which can be used as a source of data for driving the machine learning applications tending to solve certain software engineering problems. In this paper, we present COFO, a dataset consisting of 809 classes/problems with a total of 369K source codes written in C, C++, Java, and Python programming languages, along with other metadata such as code tags, problem specification, and input-output specifications. COFO has been scraped from the openly available Codeforces website using a selenium-beautifulsoup-python based scraper. We envision that this dataset can be useful for solving machine learning-based problems like program classification/recognition, tagging, predicting program properties, and code comprehension. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_18251 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | COFO: COdeFOrces dataset for Program Classification, Recognition and Tagging Gautam, Kuldeep VenkataKeerthy, S. Upadrasta, Ramakrishna Software Engineering In recent years, a lot of technological advances in computer science have aided software programmers to create innovative and real-time user-friendly software. With the creation of the software and the urging interest of people to learn to write software, there is a large collection of source codes that can be found on the web, also known as Big Code, which can be used as a source of data for driving the machine learning applications tending to solve certain software engineering problems. In this paper, we present COFO, a dataset consisting of 809 classes/problems with a total of 369K source codes written in C, C++, Java, and Python programming languages, along with other metadata such as code tags, problem specification, and input-output specifications. COFO has been scraped from the openly available Codeforces website using a selenium-beautifulsoup-python based scraper. We envision that this dataset can be useful for solving machine learning-based problems like program classification/recognition, tagging, predicting program properties, and code comprehension. |
| title | COFO: COdeFOrces dataset for Program Classification, Recognition and Tagging |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2503.18251 |