# Automatic Identification of Self-Admitted Technical Debt from Four Different Sources

arXiv: https://arxiv.org/abs/2202.02387 · HTML: https://mlsanity.com/paper/2202.02387/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 56 references that are in the map.
- First posted on arXiv 4 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1510.03820/) (2015)
- [Beyond the Code: Mining Self-Admitted Technical Debt in Issue Tracker Systems](https://mlsanity.com/paper/2003.09418/) (2020)
- [Identification and Remediation of Self-Admitted Technical Debt in Issue Trackers](https://mlsanity.com/paper/2007.01568/) (2020)

## Built on by (most cited first, 10 of 10)

- [Defending Code Language Models against Backdoor Attacks with Deceptive Cross-Entropy Loss](https://mlsanity.com/paper/2407.08956/) (2024)
- [Self-Admitted Technical Debt Detection Approaches: A Decade Systematic Review](https://mlsanity.com/paper/2312.15020/) (2023)
- [Automatically Identifying Relations Between Self-Admitted Technical Debt Across Different Sources](https://mlsanity.com/paper/2303.07079/) (2023)
- [SATDAUG -- A Balanced and Augmented Dataset for Detecting Self-Admitted Technical Debt](https://mlsanity.com/paper/2403.07690/) (2024)
- [Artificial Intelligence for Technical Debt Management in Software Development](https://mlsanity.com/paper/2306.10194/) (2023)
- [Text Tells the Cost: Predicting and Analyzing Repayment Effort of Self-Admitted Technical Debt](https://mlsanity.com/paper/2309.06020/) (2023)
- [Dopamin: Transformer-based Comment Classifiers through Domain Post-Training and Multi-level Layer Aggregation](https://mlsanity.com/paper/2408.04663/) (2024)
- [Deep Learning and Data Augmentation for Detecting Self-Admitted Technical Debt](https://mlsanity.com/paper/2410.15804/) (2024)
- [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)
- [Studying, Identifying, and Fixing Hidden Technical Debt in AI-Intensive Cyber-Physical Systems](https://mlsanity.com/paper/2608.02638/) (2026)

## Abstract (arXiv)

Technical debt refers to taking shortcuts to achieve short-term goals while sacrificing the long-term maintainability and evolvability of software systems. A large part of technical debt is explicitly reported by the developers themselves; this is commonly referred to as Self-Admitted Technical Debt or SATD. Previous work has focused on identifying SATD from source code comments and issue trackers. However, there are no approaches available for automatically identifying SATD from other sources such as commit messages and pull requests, or by combining multiple sources. Therefore, we propose and evaluate an approach for automated SATD identification that integrates four sources: source code comments, commit messages, pull requests, and issue tracking systems. Our findings show that our approach outperforms baseline approaches and achieves an average F1-score of 0.611 when detecting four types of SATD (i.e., code/design debt, requirement debt, documentation debt, and test debt) from the four aforementioned sources. Thereafter, we analyze 23.6M code comments, 1.3M commit messages, 3.7M issue sections, and 1.7M pull request sections to characterize SATD in 103 open-source projects. Furthermore, we investigate the SATD keywords and relations between SATD in different sources. The findings indicate, among others, that: 1) SATD is evenly spread among all sources; 2) issues and pull requests are the two most similar sources regarding the number of shared SATD keywords, followed by commit messages, and then followed by code comments; 3) there are four kinds of relations between SATD items in the different sources.