Towards Explainable Test Case Prioritisation with Learning-to-Rank Models
| Autor: | Ramírez, Aurora, Berrios, Mario, Romero, José Raúl, Feldt, Robert |
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| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Proc. 2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), pp. 66-69 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/ICSTW58534.2023.00023 |
| Popis: | 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. Comment: 3rd International Workshop on Artificial Intelligence in Software Testing (AIST) - International Conference on Software Testing and Validation (ICST) |
| Databáze: | arXiv |
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