Descriptor: C++ Self-Admitted Technical Debt Dataset (CppSATD)
This work addresses a gap in software engineering research by providing a foundational dataset for C++ SATD, which is incremental as it extends existing Java-focused studies to a new language.
The authors tackled the lack of cross-language Self-Admitted Technical Debt (SATD) datasets by introducing CppSATD, a C++ dataset with over 531,000 annotated comments and source code contexts, enabling future research in SATD detection and generalization.
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.