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

TL;DR

This paper explores the use of learning-to-rank models for test case prioritisation in regression testing, emphasizing the importance of explainability and analyzing how explanations vary across different scenarios and rankings.

Contribution

It introduces a discussion on explainability challenges in learning-to-rank models for TCP and presents a preliminary experiment analyzing explanation similarities.

Findings

Explanations vary with test case predictions and relative ranks.

Different scenarios influence the type of explanations needed.

Preliminary results highlight the complexity of explainability in TCP.

Abstract

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.