# Test Case Selection and Prioritization Using Machine Learning: A Systematic Literature Review

arXiv: https://arxiv.org/abs/2106.13891 · HTML: https://mlsanity.com/paper/2106.13891/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2021.

## Built on by (most cited first, 12 of 12)

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- [Test2Vec: An Execution Trace Embedding for Test Case Prioritization](https://mlsanity.com/paper/2206.15428/) (2022)
- [A Taxonomy of Information Attributes for Test Case Prioritisation: Applicability, Machine Learning](https://mlsanity.com/paper/2201.06044/) (2022)
- [Assessing Project-Level Fine-Tuning of ML4SE Models](https://mlsanity.com/paper/2206.03333/) (2022)
- [Systematic Literature Review on Application of Machine Learning in Continuous Integration](https://mlsanity.com/paper/2305.12695/) (2023)
- [Towards Explainable Test Case Prioritisation with Learning-to-Rank Models](https://mlsanity.com/paper/2405.13786/) (2024)
- [Test Case Prioritization: A Snowballing Literature Review and TCPFramework with Approach Combinators](https://mlsanity.com/paper/2603.00183/) (2026)
- [Impact Is Not Invalidation: Ask About the Claim, Not the Diff](https://mlsanity.com/paper/2609.25130/) (2026)
- [FastCI: Efficient GPU-Intensive CI for LLM Training Frameworks](https://mlsanity.com/paper/2610.01967/) (2026)

## Abstract (arXiv)

Regression testing is an essential activity to assure that software code changes do not adversely affect existing functionalities. With the wide adoption of Continuous Integration (CI) in software projects, which increases the frequency of running software builds, running all tests can be time-consuming and resource-intensive. To alleviate that problem, Test case Selection and Prioritization (TSP) techniques have been proposed to improve regression testing by selecting and prioritizing test cases in order to provide early feedback to developers. In recent years, researchers have relied on Machine Learning (ML) techniques to achieve effective TSP (ML-based TSP). Such techniques help combine information about test cases, from partial and imperfect sources, into accurate prediction models. This work conducts a systematic literature review focused on ML-based TSP techniques, aiming to perform an in-depth analysis of the state of the art, thus gaining insights regarding future avenues of research. To that end, we analyze 29 primary studies published from 2006 to 2020, which have been identified through a systematic and documented process. This paper addresses five research questions addressing variations in ML-based TSP techniques and feature sets for training and testing ML models, alternative metrics used for evaluating the techniques, the performance of techniques, and the reproducibility of the published studies. We summarize the results related to our research questions in a high-level summary that can be used as a taxonomy for classifying future TSP studies.