
Descriptor: C++ Self-Admitted Technical Debt Dataset (CppSATD)
Author(s) -
Phuoc Pham,
Murali Sridharan,
Matteo Esposito,
Valentina Lenarduzzi
Publication year - 2025
Publication title -
ieee data descriptions
Language(s) - English
Resource type - Magazines
eISSN - 2995-4274
DOI - 10.1109/ieeedata.2025.3576339
Subject(s) - computing and processing
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.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom