# Self-Admitted Technical Debt Detection Approaches: A Decade Systematic Review

arXiv: https://arxiv.org/abs/2312.15020 · HTML: https://mlsanity.com/paper/2312.15020/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 129 references that are in the map.
- First posted on arXiv 19 Dec 2023.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [Language Models with Transformers](https://mlsanity.com/paper/1904.09408/) (2019)
- [Explainable Artificial Intelligence and Machine Learning: A reality rooted perspective](https://mlsanity.com/paper/2001.09464/) (2020)
- [Identifying Self-Admitted Technical Debts with Jitterbug: A Two-step Approach](https://mlsanity.com/paper/2002.11049/) (2020)
- [Automatic Identification of Self-Admitted Technical Debt from Four Different Sources](https://mlsanity.com/paper/2202.02387/) (2022)
- [Deep Transfer Learning & Beyond: Transformer Language Models in Information Systems Research](https://mlsanity.com/paper/2110.08975/) (2021)
- [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)
- [DebtFree: Minimizing Labeling Cost in Self-Admitted Technical Debt Identification using Semi-Supervised Learning](https://mlsanity.com/paper/2201.10592/) (2022)
- [On the Performance of Hybrid Search Strategies for Systematic Literature Reviews in Software Engineering](https://mlsanity.com/paper/2004.09741/) (2020)
- [Identification and Remediation of Self-Admitted Technical Debt in Issue Trackers](https://mlsanity.com/paper/2007.01568/) (2020)
- [Data Balancing Improves Self-Admitted Technical Debt Detection](https://mlsanity.com/paper/2103.13165/) (2021)
- [Automatic Detection and Analysis of Technical Debts in Peer-Review Documentation of R Packages](https://mlsanity.com/paper/2201.04241/) (2022)
- [Technical Debt in the Peer-Review Documentation of R Packages: a rOpenSci Case Study](https://mlsanity.com/paper/2103.09340/) (2021)
- [SATDBailiff- Mining and Tracking Self-Admitted Technical Debt](https://mlsanity.com/paper/2107.00073/) (2021)
- [WeakSATD: Detecting Weak Self-admitted Technical Debt](https://mlsanity.com/paper/2205.02208/) (2022)
- [DebtViz: A Tool for Identifying, Measuring, Visualizing, and Monitoring Self-Admitted Technical Debt](https://mlsanity.com/paper/2308.13128/) (2023)
- [Wait For It: Identifying "On-Hold" Self-Admitted Technical Debt](https://mlsanity.com/paper/1901.09511/) (2019)
- [Prevalence, Contents and Automatic Detection of KL-SATD](https://mlsanity.com/paper/2008.05159/) (2020)
- [Automated Identification of On-hold Self-admitted Technical Debt](https://mlsanity.com/paper/2009.13113/) (2020)
- [Characterizing and Mitigating Self-Admitted Technical Debt in Build Systems](https://mlsanity.com/paper/2102.09775/) (2021)
- [FixMe: A GitHub Bot for Detecting and Monitoring On-Hold Self-Admitted Technical Debt](https://mlsanity.com/paper/2109.02878/) (2021)
- [Measuring Improvement of F1-Scores in Detection of Self-Admitted Technical Debt](https://mlsanity.com/paper/2303.09617/) (2023)
- [Automated Self-Admitted Technical Debt Tracking at Commit-Level: A Language-independent Approach](https://mlsanity.com/paper/2304.07829/) (2023)

## Built on by (most cited first, 2 of 2)

- [Deep Learning and Data Augmentation for Detecting Self-Admitted Technical Debt](https://mlsanity.com/paper/2410.15804/) (2024)
- [TD-Suite: All Batteries Included Framework for Technical Debt Classification](https://mlsanity.com/paper/2504.11085/) (2025)

## Abstract (arXiv)

Technical debt (TD) refers to the long-term costs associated with suboptimal design or code decisions in software development, often made to meet short-term delivery goals. Self-Admitted Technical Debt (SATD) occurs when developers explicitly acknowledge these trade-offs in the codebase, typically through comments or annotations. SATD detection has become an increasingly important research area, particularly with the rise of learning-based techniques that aim to streamline SATD detection. This systematic literature review provides a comprehensive analysis of SATD detection approaches published between 2014 and early 2025, focusing on the evolution of techniques from heuristic-based techniques to more advanced ML, DL, and Transformer-based models. It examines key trends in SATD detection methodologies and tools, evaluates the effectiveness of different approaches using metrics like precision, recall, and F1 score, and highlights the primary challenges in this domain, including dataset heterogeneity, model generalizability, and explainability. The findings reveal that while early heuristic-based techniques laid the foundation for SATD detection, more recent advancements in DL and Transformer models have significantly improved detection accuracy. However, challenges remain in scaling these models for broader industrial adoption. This review offers insights into current research gaps and provides directions for future work, aiming to improve the robustness and practicality of SATD detection tools.