# Identifying Self-Admitted Technical Debt in Issue Tracking Systems using Machine Learning

arXiv: https://arxiv.org/abs/2202.02180 · HTML: https://mlsanity.com/paper/2202.02180/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 62 references that are in the map.
- First posted on arXiv 4 Feb 2022.

## Builds on (most cited first, 15 of 15)

- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1510.03820/) (2015)
- [Towards Universal Paraphrastic Sentence Embeddings](https://mlsanity.com/paper/1511.08198/) (2015)
- [Sentiment Polarity Detection for Software Development](https://mlsanity.com/paper/1709.02984/) (2017)
- [A Practitioners' Guide to Transfer Learning for Text Classification using Convolutional Neural Networks](https://mlsanity.com/paper/1801.06480/) (2018)
- [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)
- [DNN and CNN with Weighted and Multi-task Loss Functions for Audio Event Detection](https://mlsanity.com/paper/1708.03211/) (2017)

## Built on by (most cited first, 6 of 6)

- [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)
- [Identifying Technical Debt and Its Types Across Diverse Software Projects Issues](https://mlsanity.com/paper/2408.09128/) (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)

## Abstract (arXiv)

Technical debt is a metaphor indicating sub-optimal solutions implemented for short-term benefits by sacrificing the long-term maintainability and evolvability of software. A special type of technical debt is explicitly admitted by software engineers (e.g. using a TODO comment); this is called Self-Admitted Technical Debt or SATD. Most work on automatically identifying SATD focuses on source code comments. In addition to source code comments, issue tracking systems have shown to be another rich source of SATD, but there are no approaches specifically for automatically identifying SATD in issues. In this paper, we first create a training dataset by collecting and manually analyzing 4,200 issues (that break down to 23,180 sections of issues) from seven open-source projects (i.e., Camel, Chromium, Gerrit, Hadoop, HBase, Impala, and Thrift) using two popular issue tracking systems (i.e., Jira and Google Monorail). We then propose and optimize an approach for automatically identifying SATD in issue tracking systems using machine learning. Our findings indicate that: 1) our approach outperforms baseline approaches by a wide margin with regard to the F1-score; 2) transferring knowledge from suitable datasets can improve the predictive performance of our approach; 3) extracted SATD keywords are intuitive and potentially indicating types and indicators of SATD; 4) projects using different issue tracking systems have less common SATD keywords compared to projects using the same issue tracking system; 5) a small amount of training data is needed to achieve good accuracy.