# Reinforcement Learning for Test Case Prioritization

arXiv: https://arxiv.org/abs/2011.01834 · HTML: https://mlsanity.com/paper/2011.01834/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 13 of 13)

- [ChatGPT: A Study on its Utility for Ubiquitous Software Engineering Tasks](https://mlsanity.com/paper/2305.16837/) (2023)
- [RLocator: Reinforcement Learning for Bug Localization](https://mlsanity.com/paper/2305.05586/) (2023)
- [Systematic Literature Review on Application of Learning-based Approaches in Continuous Integration](https://mlsanity.com/paper/2406.19765/) (2024)
- [RAGVA: Engineering Retrieval Augmented Generation-based Virtual Assistants in Practice](https://mlsanity.com/paper/2502.14930/) (2025)
- [Robust Risk-Sensitive Reinforcement Learning Agents for Trading Markets](https://mlsanity.com/paper/2107.08083/) (2021)
- [A Comparison of Reinforcement Learning Frameworks for Software Testing Tasks](https://mlsanity.com/paper/2208.12136/) (2022)
- [Systematic Literature Review on Application of Machine Learning in Continuous Integration](https://mlsanity.com/paper/2305.12695/) (2023)
- [DREAM: Debugging and Repairing AutoML Pipelines](https://mlsanity.com/paper/2401.00379/) (2023)
- [Targeted Test Selection Approach in Continuous Integration](https://mlsanity.com/paper/2509.10279/) (2025)
- [Reconsidering Requirements Engineering: Human-AI Collaboration in AI-Native Software Development](https://mlsanity.com/paper/2510.04380/) (2025)
- [Test Case Prioritization: A Snowballing Literature Review and TCPFramework with Approach Combinators](https://mlsanity.com/paper/2603.00183/) (2026)
- [Trajectory-Aware Benchmark Subset Selection for Cost-Efficient Software Engineering Agent Regression Testing](https://mlsanity.com/paper/2609.24928/) (2026)
- [Not All Correct Fixes Run Alike: An Empirical Study of Patch Performance in Repository-Level Issue Resolution](https://mlsanity.com/paper/2610.13563/) (2026)

## Abstract (arXiv)

Continuous Integration (CI) significantly reduces integration problems, speeds up development time, and shortens release time. However, it also introduces new challenges for quality assurance activities, including regression testing, which is the focus of this work. Though various approaches for test case prioritization have shown to be very promising in the context of regression testing, specific techniques must be designed to deal with the dynamic nature and timing constraints of CI. Recently, Reinforcement Learning (RL) has shown great potential in various challenging scenarios that require continuous adaptation, such as game playing, real-time ads bidding, and recommender systems. Inspired by this line of work and building on initial efforts in supporting test case prioritization with RL techniques, we perform here a comprehensive investigation of RL-based test case prioritization in a CI context. To this end, taking test case prioritization as a ranking problem, we model the sequential interactions between the CI environment and a test case prioritization agent as an RL problem, using three alternative ranking models. We then rely on carefully selected and tailored state-of-the-art RL techniques to automatically and continuously learn a test case prioritization strategy, whose objective is to be as close as possible to the optimal one. Our extensive experimental analysis shows that the best RL solutions provide a significant accuracy improvement over previous RL-based work, with prioritization strategies getting close to being optimal, thus paving the way for using RL to prioritize test cases in a CI context.