Descriptor: C++ Self-Admitted Technical Debt Dataset (CppSATD)
Fuente:
arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908389199577088 |
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| author | Pham, Phuoc Sridharan, Murali Esposito, Matteo Lenarduzzi, Valentina |
| author_facet | Pham, Phuoc Sridharan, Murali Esposito, Matteo Lenarduzzi, Valentina |
| contents | In software development, technical debt (TD) refers to suboptimal implementation choices made by the developers to meet urgent deadlines and limited resources, posing challenges for future maintenance. Self-Admitted Technical Debt (SATD) is a sub-type of TD, representing specific TD instances ``openly admitted'' by the developers and often expressed through source code comments. Previous research on SATD has focused predominantly on the Java programming language, revealing a significant gap in cross-language SATD. Such a narrow focus limits the generalizability of existing findings as well as SATD detection techniques across multiple programming languages. Our work addresses such limitation by introducing CppSATD, a dedicated C++ SATD dataset, comprising over 531,000 annotated comments and their source code contexts. Our dataset can serve as a foundation for future studies that aim to develop SATD detection methods in C++, generalize the existing findings to other languages, or contribute novel insights to cross-language SATD research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01136 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Descriptor: C++ Self-Admitted Technical Debt Dataset (CppSATD) Pham, Phuoc Sridharan, Murali Esposito, Matteo Lenarduzzi, Valentina Software Engineering Information Retrieval Machine Learning Programming Languages In software development, technical debt (TD) refers to suboptimal implementation choices made by the developers to meet urgent deadlines and limited resources, posing challenges for future maintenance. Self-Admitted Technical Debt (SATD) is a sub-type of TD, representing specific TD instances ``openly admitted'' by the developers and often expressed through source code comments. Previous research on SATD has focused predominantly on the Java programming language, revealing a significant gap in cross-language SATD. Such a narrow focus limits the generalizability of existing findings as well as SATD detection techniques across multiple programming languages. Our work addresses such limitation by introducing CppSATD, a dedicated C++ SATD dataset, comprising over 531,000 annotated comments and their source code contexts. Our dataset can serve as a foundation for future studies that aim to develop SATD detection methods in C++, generalize the existing findings to other languages, or contribute novel insights to cross-language SATD research. |
| title | Descriptor: C++ Self-Admitted Technical Debt Dataset (CppSATD) |
| topic | Software Engineering Information Retrieval Machine Learning Programming Languages |
| url | https://arxiv.org/abs/2505.01136 |