A methodology for explaining Learning-to-rank models for test case prioritization

Abstract

In machine learning-based test case prioritization (TCP), there is a need for explainable methods that can shed light on the internal mechanisms of the models and clarify why certain test cases are deemed more likely to fail. To address this need, we propose an experimental methodology designed to generate and analyze global explanations for models in TCP. This methodology can help testers and researchers to understand how the impact of different features shifts as the software system evolves.