# A Taxonomy of Information Attributes for Test Case Prioritisation: Applicability, Machine Learning

arXiv: https://arxiv.org/abs/2201.06044 · HTML: https://mlsanity.com/paper/2201.06044/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 133 references that are in the map.
- First posted on arXiv 16 Jan 2022.

## Builds on (most cited first, 7 of 7)

- [Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration](https://mlsanity.com/paper/1811.04122/) (2018)
- [Test Case Selection and Prioritization Using Machine Learning: A Systematic Literature Review](https://mlsanity.com/paper/2106.13891/) (2021)
- [A taxonomy of risk-based testing](https://mlsanity.com/paper/1912.11519/) (2019)
- [Regression Test Case Prioritization by Code Combinations Coverage](https://mlsanity.com/paper/2007.00370/) (2020)
- [Test case generation for agent-based models: A systematic literature review](https://mlsanity.com/paper/2103.07370/) (2021)
- [Incorporating fault-proneness estimations into coverage-based test case prioritization methods](https://mlsanity.com/paper/1908.06502/) (2019)
- [How Do Static and Dynamic Test Case Prioritization Techniques Perform on Modern Software Systems? An Extensive Study on GitHub Projects](https://mlsanity.com/paper/1806.09774/) (2018)

## Built on by (most cited first, 2 of 2)

- [UnitTenX: Generating Tests for Legacy Packages with AI Agents Powered by Formal Verification](https://mlsanity.com/paper/2510.05441/) (2025)
- [Towards Explainable Test Case Prioritisation with Learning-to-Rank Models](https://mlsanity.com/paper/2405.13786/) (2024)

## Abstract (arXiv)

Most software companies have extensive test suites and re-run parts of them continuously to ensure recent changes have no adverse effects. Since test suites are costly to execute, industry needs methods for test case prioritisation (TCP). Recently, TCP methods use machine learning (ML) to exploit the information known about the system under test (SUT) and its test cases. However, the value added by ML-based TCP methods should be critically assessed with respect to the cost of collecting the information. This paper analyses two decades of TCP research, and presents a taxonomy of 91 information attributes that have been used. The attributes are classified with respect to their information sources and the characteristics of their extraction process. Based on this taxonomy, TCP methods validated with industrial data and those applying ML are analysed in terms of information availability, attribute combination and definition of data features suitable for ML. Relying on a high number of information attributes, assuming easy access to SUT code and simplified testing environments are identified as factors that might hamper industrial applicability of ML-based TCP. The TePIA taxonomy provides a reference framework to unify terminology and evaluate alternatives considering the cost-benefit of the information attributes.