# Identification and Remediation of Self-Admitted Technical Debt in Issue Trackers

arXiv: https://arxiv.org/abs/2007.01568 · HTML: https://mlsanity.com/paper/2007.01568/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [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)
- [Self-Admitted Technical Debt Detection Approaches: A Decade Systematic Review](https://mlsanity.com/paper/2312.15020/) (2023)
- [Text Tells the Cost: Predicting and Analyzing Repayment Effort of Self-Admitted Technical Debt](https://mlsanity.com/paper/2309.06020/) (2023)

## Abstract (arXiv)

Technical debt refers to taking shortcuts to achieve short-term goals, which might negatively influence software maintenance in the long-term. There is increasing attention on technical debt that is admitted by developers in source code comments (termed as self-admitted technical debt or SATD). But SATD in issue trackers is relatively unexplored. We performed a case study, where we manually examined 500 issues from two open source projects (i.e. Hadoop and Camel), which contained 152 SATD items. We found that: 1) eight types of technical debt are identified in issues, namely architecture, build, code, defect, design, documentation, requirement, and test debt; 2) developers identify technical debt in issues in three different points in time, and a small part is identified by its creators; 3) the majority of technical debt is paid off, 4) mostly by those who identified it or created it; 5) the median time and average time to repay technical debt are 872.3 and 25.0 hours respectively.