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

arXiv: https://arxiv.org/abs/2505.01136 · HTML: https://mlsanity.com/paper/2505.01136/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 2 May 2025.

## Builds on (most cited first, 9 of 9)

- [Generative AI for Software Architecture. Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2503.13310/) (2025)
- [An Extensive Comparison of Static Application Security Testing Tools](https://mlsanity.com/paper/2403.09219/) (2024)
- [Leveraging Large Language Models for Preliminary Security Risk Analysis: A Mission-Critical Case Study](https://mlsanity.com/paper/2403.15756/) (2024)
- [Beyond Words: On Large Language Models Actionability in Mission-Critical Risk Analysis](https://mlsanity.com/paper/2406.10273/) (2024)
- [An Empirical Study on the Effectiveness of Large Language Models for SATD Identification and Classification](https://mlsanity.com/paper/2405.06806/) (2024)
- [Early Career Developers' Perceptions of Code Understandability. A Study of Complexity Metrics](https://mlsanity.com/paper/2303.07722/) (2023)
- [PENTACET data -- 23 Million Contextual Code Comments and 250,000 SATD comments](https://mlsanity.com/paper/2303.14029/) (2023)
- [Scoring Popularity in GitHub](https://mlsanity.com/paper/2011.04865/) (2020)
- [Tracing the Lifecycle of Architecture Technical Debt in Software Systems: A Dependency Approach](https://mlsanity.com/paper/2501.15387/) (2025)

## 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.