# ADPTriage: Approximate Dynamic Programming for Bug Triage

arXiv: https://arxiv.org/abs/2211.00872 · HTML: https://mlsanity.com/paper/2211.00872/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 53 references that are in the map.
- First posted on arXiv 2 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [Neural Approximate Dynamic Programming for On-Demand Ride-Pooling](https://mlsanity.com/paper/1911.08842/) (2019)
- [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)
- [A multi-label, dual-output deep neural network for automated bug triaging](https://mlsanity.com/paper/1910.05835/) (2019)
- [S-DABT: Schedule and Dependency-Aware Bug Triage in Open-Source Bug Tracking Systems](https://mlsanity.com/paper/2204.05972/) (2022)
- [Wayback Machine: A tool to capture the evolutionary behaviour of the bug reports and their triage process in open-source software systems](https://mlsanity.com/paper/2011.05382/) (2020)
- [Issue Auto-Assignment in Software Projects with Machine Learning Techniques](https://mlsanity.com/paper/2104.01717/) (2021)
- [Predicting the Number of Reported Bugs in a Software Repository](https://mlsanity.com/paper/2104.12001/) (2021)

## Built on by (most cited first, 1 of 1)

- [TriagerX: Dual Transformers for Bug Triaging Tasks with Content and Interaction Based Rankings](https://mlsanity.com/paper/2508.16860/) (2025)

## Abstract (arXiv)

Bug triaging is a critical task in any software development project. It entails triagers going over a list of open bugs, deciding whether each is required to be addressed, and, if so, which developer should fix it. However, the manual bug assignment in issue tracking systems (ITS) offers only a limited solution and might easily fail when triagers must handle a large number of bug reports. During the automated assignment, there are multiple sources of uncertainties in the ITS, which should be addressed meticulously. In this study, we develop a Markov decision process (MDP) model for an online bug triage task. In addition to an optimization-based myopic technique, we provide an ADP-based bug triage solution, called ADPTriage, which has the ability to reflect the downstream uncertainty in the bug arrivals and developers' timetables. Specifically, without placing any limits on the underlying stochastic process, this technique enables real-time decision-making on bug assignments while taking into consideration developers' expertise, bug type, and bug fixing time. Our result shows a significant improvement over the myopic approach in terms of assignment accuracy and fixing time. We also demonstrate the empirical convergence of the model and conduct sensitivity analysis with various model parameters. Accordingly, this work constitutes a significant step forward in addressing the uncertainty in bug triage solutions