# Towards Explainable Test Case Prioritisation with Learning-to-Rank Models

arXiv: https://arxiv.org/abs/2405.13786 · HTML: https://mlsanity.com/paper/2405.13786/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 22 May 2024.

## Builds on (most cited first, 9 of 9)

- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications](https://mlsanity.com/paper/2103.04244/) (2021)
- [Explanations of model predictions with live and breakDown packages](https://mlsanity.com/paper/1804.01955/) (2018)
- [Explainable Software Analytics](https://mlsanity.com/paper/1802.00603/) (2018)
- [Test Case Selection and Prioritization Using Machine Learning: A Systematic Literature Review](https://mlsanity.com/paper/2106.13891/) (2021)
- [Practitioners' Perceptions of the Goals and Visual Explanations of Defect Prediction Models](https://mlsanity.com/paper/2102.12007/) (2021)
- [Scalable and Accurate Test Case Prioritization in Continuous Integration Contexts](https://mlsanity.com/paper/2109.13168/) (2021)
- [A Taxonomy of Information Attributes for Test Case Prioritisation: Applicability, Machine Learning](https://mlsanity.com/paper/2201.06044/) (2022)

## Abstract (arXiv)

Test case prioritisation (TCP) is a critical task in regression testing to ensure quality as software evolves. Machine learning has become a common way to achieve it. In particular, learning-to-rank (LTR) algorithms provide an effective method of ordering and prioritising test cases. However, their use poses a challenge in terms of explainability, both globally at the model level and locally for particular results. Here, we present and discuss scenarios that require different explanations and how the particularities of TCP (multiple builds over time, test case and test suite variations, etc.) could influence them. We include a preliminary experiment to analyse the similarity of explanations, showing that they do not only vary depending on test case-specific predictions, but also on the relative ranks.