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Towards Explainable...
Towards Explainable Test Case Prioritisation with Learning-to-Rank Models
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- Ramírez, Aurora (author)
- Universidad de Córdoba
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- Berrios, Mario (author)
- Universidad de Córdoba
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- Romero, José Raúl (author)
- Universidad de Córdoba
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- Feldt, Robert, 1972 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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(creator_code:org_t)
- 2023
- 2023
- English.
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In: Proceedings - 2023 IEEE 16th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2023. ; , s. 66-69
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Keyword
- machine learning
- explainable artificial intelligence
- learning-to-rank
- test case prioritisation
Publication and Content Type
- kon (subject category)
- ref (subject category)
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