  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.
