# Identifying Self-Admitted Technical Debts with Jitterbug: A Two-step Approach

arXiv: https://arxiv.org/abs/2002.11049 · HTML: https://mlsanity.com/paper/2002.11049/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2020.

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

- [FRUGAL: Unlocking SSL for Software Analytics](https://mlsanity.com/paper/2108.09847/) (2021)
- [DebtFree: Minimizing Labeling Cost in Self-Admitted Technical Debt Identification using Semi-Supervised Learning](https://mlsanity.com/paper/2201.10592/) (2022)
- [Learning from Very Little Data: On the Value of Landscape Analysis for Predicting Software Project Health](https://mlsanity.com/paper/2301.06577/) (2023)
- [PENTACET data -- 23 Million Contextual Code Comments and 250,000 SATD comments](https://mlsanity.com/paper/2303.14029/) (2023)
- [Self-Admitted Technical Debt Detection Approaches: A Decade Systematic Review](https://mlsanity.com/paper/2312.15020/) (2023)
- [How to Find Actionable Static Analysis Warnings: A Case Study with FindBugs](https://mlsanity.com/paper/2205.10504/) (2022)
- [When Less is More: On the Value of "Co-training" for Semi-Supervised Software Defect Predictors](https://mlsanity.com/paper/2211.05920/) (2022)
- [Measuring Improvement of F1-Scores in Detection of Self-Admitted Technical Debt](https://mlsanity.com/paper/2303.09617/) (2023)
- [Less, but Stronger: On the Value of Strong Heuristics in Semi-supervised Learning for Software Analytics](https://mlsanity.com/paper/2302.01997/) (2023)
- [Is Model Instability just Noise to be Tolerated or a Property that can be Managed?](https://mlsanity.com/paper/2607.10420/) (2026)

## Abstract (arXiv)

Keeping track of and managing Self-Admitted Technical Debts (SATDs) are important to maintaining a healthy software project. This requires much time and effort from human experts to identify the SATDs manually. The current automated solutions do not have satisfactory precision and recall in identifying SATDs to fully automate the process. To solve the above problems, we propose a two-step framework called Jitterbug for identifying SATDs. Jitterbug first identifies the "easy to find" SATDs automatically with close to 100% precision using a novel pattern recognition technique. Subsequently, machine learning techniques are applied to assist human experts in manually identifying the remaining "hard to find" SATDs with reduced human effort. Our simulation studies on ten software projects show that Jitterbug can identify SATDs more efficiently (with less human effort) than the prior state-of-the-art methods.