# SATDAUG -- A Balanced and Augmented Dataset for Detecting Self-Admitted Technical Debt

arXiv: https://arxiv.org/abs/2403.07690 · HTML: https://mlsanity.com/paper/2403.07690/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 4 of 4)

- [Do We Need More Training Data?](https://mlsanity.com/paper/1503.01508/) (2015)
- [Automatic Identification of Self-Admitted Technical Debt from Four Different Sources](https://mlsanity.com/paper/2202.02387/) (2022)
- [Identifying Self-Admitted Technical Debt in Issue Tracking Systems using Machine Learning](https://mlsanity.com/paper/2202.02180/) (2022)
- [Beyond the Code: Mining Self-Admitted Technical Debt in Issue Tracker Systems](https://mlsanity.com/paper/2003.09418/) (2020)

## Built on by (most cited first, 1 of 1)

- [From AI Technical Debt to Agentic Technical Debt: A Systematic Mapping of Root Causes and Manifestations in Agentic AI Systems](https://mlsanity.com/paper/2608.01001/) (2026)

## Abstract (arXiv)

Self-admitted technical debt (SATD) refers to a form of technical debt in which developers explicitly acknowledge and document the existence of technical shortcuts, workarounds, or temporary solutions within the codebase. Over recent years, researchers have manually labeled datasets derived from various software development artifacts: source code comments, messages from the issue tracker and pull request sections, and commit messages. These datasets are designed for training, evaluation, performance validation, and improvement of machine learning and deep learning models to accurately identify SATD instances. However, class imbalance poses a serious challenge across all the existing datasets, particularly when researchers are interested in categorizing the specific types of SATD. In order to address the scarcity of labeled data for SATD textitidentification (i.e., whether an instance is SATD or not) and textitcategorization (i.e., which type of SATD is being classified) in existing datasets, we share the textitSATDAUG dataset, an augmented version of existing SATD datasets, including source code comments, issue tracker, pull requests, and commit messages. These augmented datasets have been balanced in relation to the available artifacts and provide a much richer source of labeled data for training machine learning or deep learning models.