# Test Case Prioritization: A Snowballing Literature Review and TCPFramework with Approach Combinators

arXiv: https://arxiv.org/abs/2603.00183 · HTML: https://mlsanity.com/paper/2603.00183/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 56 references that are in the map.
- First posted on arXiv 26 Feb 2026.

## Builds on (most cited first, 10 of 10)

- [Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration](https://mlsanity.com/paper/1811.04122/) (2018)
- [Reinforcement Learning for Test Case Prioritization](https://mlsanity.com/paper/2011.01834/) (2020)
- [Test Case Selection and Prioritization Using Machine Learning: A Systematic Literature Review](https://mlsanity.com/paper/2106.13891/) (2021)
- [Scalable and Accurate Test Case Prioritization in Continuous Integration Contexts](https://mlsanity.com/paper/2109.13168/) (2021)
- [Test Prioritization in Continuous Integration Environments](https://mlsanity.com/paper/1809.00143/) (2018)
- [Regression Test Case Prioritization by Code Combinations Coverage](https://mlsanity.com/paper/2007.00370/) (2020)
- [Revisiting Machine Learning based Test Case Prioritization for Continuous Integration](https://mlsanity.com/paper/2311.13413/) (2023)
- [A Large-Scale Empirical Comparison of Static and Dynamic Test Case Prioritization Techniques](https://mlsanity.com/paper/1801.05917/) (2018)
- [Assessing Test Case Prioritization on Real Faults and Mutants](https://mlsanity.com/paper/1807.08823/) (2018)
- [Comparative Study of Machine Learning Test Case Prioritization for Continuous Integration Testing](https://mlsanity.com/paper/2204.10899/) (2022)

## Abstract (arXiv)

Context: Test case prioritization (TCP) is a technique widely used by software development organizations to accelerate regression testing. Objectives: We aim to systematize existing TCP knowledge and to propose and empirically evaluate a new TCP approach. Methods: We conduct a snowballing review (SR) on TCP, implement a~comprehensive platform for TCP research (TCPFramework), analyze existing evaluation metrics and propose two new ones (rAPFDc and ATR), and develop a~family of ensemble TCP methods called approach combinators. Results: The SR helped identify 324 studies related to TCP. The techniques proposed in our study were evaluated on the RTPTorrent dataset, consistently outperforming their base approaches across the majority of subject programs, and achieving performance comparable to the current state of the art for heuristical algorithms (in terms of rAPFDc, NTR, and ATR), while using a distinct approach. Conclusions: The proposed methods can be used efficiently for TCP, reducing the time spent on regression testing by up to 2.7%. Approach combinators offer significant potential for improvements in future TCP research, due to their composability.