Towards Explainable Test Case Prioritisation with Learning-to-Rank Models
This addresses the problem of making test case prioritisation more interpretable for software developers, but it is incremental as it focuses on analyzing explanation variations without introducing a new method.
The paper tackles the challenge of explainability in test case prioritisation using learning-to-rank models, showing through a preliminary experiment that explanations vary based on test case-specific predictions and relative ranks.
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.