mlsanityData to 7 Oct 2026

Descriptor: C++ Self-Admitted Technical Debt Dataset (CppSATD)

arXiv 2505.01136 ↗First posted 2 May 2025cs.SE cs.IR cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 28 references that are in the map. First posted on arXiv 2 May 2025.

Builds on

9 of 28 references.

References · 9

  1. 2025Generative AI for Software Architecture. Applications, Challenges, and Future Directionscited by 15
  2. 2024An Extensive Comparison of Static Application Security Testing Toolscited by 10
  3. 2024Leveraging Large Language Models for Preliminary Security Risk Analysis: A Mission-Critical Case Studycited by 10
  4. 2024Beyond Words: On Large Language Models Actionability in Mission-Critical Risk Analysiscited by 9
  5. 2024An Empirical Study on the Effectiveness of Large Language Models for SATD Identification and Classificationcited by 4
  6. 2023Early Career Developers' Perceptions of Code Understandability. A Study of Complexity Metricscited by 2
  7. 2023PENTACET data -- 23 Million Contextual Code Comments and 250,000 SATD commentscited by 2
  8. 2020Scoring Popularity in GitHubcited by 1
  9. 2025Tracing the Lifecycle of Architecture Technical Debt in Software Systems: A Dependency Approachcited by 1

Built on by

Not cited yet.