# DABT: A Dependency-aware Bug Triaging Method

arXiv: https://arxiv.org/abs/2104.12744 · HTML: https://mlsanity.com/paper/2104.12744/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 26 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [DeepTriage: Exploring the Effectiveness of Deep Learning for Bug Triaging](https://mlsanity.com/paper/1801.01275/) (2018)
- [Automatic Bug Triage using Semi-Supervised Text Classification](https://mlsanity.com/paper/1704.04769/) (2017)

## Built on by (most cited first, 3 of 3)

- [S-DABT: Schedule and Dependency-Aware Bug Triage in Open-Source Bug Tracking Systems](https://mlsanity.com/paper/2204.05972/) (2022)
- [Learning Software Bug Reports: A Systematic Literature Review](https://mlsanity.com/paper/2507.04422/) (2025)
- [When Bugs Linger: A Study of Anomalous Resolution Time Outliers and Their Themes](https://mlsanity.com/paper/2509.16140/) (2025)

## Abstract (arXiv)

In software engineering practice, fixing a bug promptly reduces the associated costs. On the other hand, the manual bug fixing process can be time-consuming, cumbersome, and error-prone. In this work, we introduce a bug triaging method, called Dependency-aware Bug Triaging (DABT), which leverages natural language processing and integer programming to assign bugs to appropriate developers. Unlike previous works that mainly focus on one aspect of the bug reports, DABT considers the textual information, cost associated with each bug, and dependency among them. Therefore, this comprehensive formulation covers the most important aspect of the previous works while considering the blocking effect of the bugs. We report the performance of the algorithm on three open-source software systems, i.e., EclipseJDT, LibreOffice, and Mozilla. Our result shows that DABT is able to reduce the number of overdue bugs up to 12%. It also decreases the average fixing time of the bugs by half. Moreover, it reduces the complexity of the bug dependency graph by prioritizing blocking bugs.