Towards Explainable Test Case Prioritisation with Learning-to-Rank Models
Paper i proceeding, 2023

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.

machine learning

explainable artificial intelligence

learning-to-rank

test case prioritisation

Författare

Aurora Ramírez

Universidad de Córdoba

Mario Berrios

Universidad de Córdoba

José Raúl Romero

Universidad de Córdoba

Robert Feldt

Chalmers, Data- och informationsteknik, Software Engineering

Proceedings - 2023 IEEE 16th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2023

66-69
9798350333350 (ISBN)

16th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2023
Dublin, Ireland,

Ämneskategorier

Datavetenskap (datalogi)

Datorsystem

DOI

10.1109/ICSTW58534.2023.00023

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Senast uppdaterat

2024-01-03