A Taxonomy of Information Attributes for Test Case Prioritisation: Applicability, Machine Learning
Aurora Ramírez,
Robert Feldt,
José Raúl Romero
Abstract:Most software companies have extensive test suites and re-run parts of them continuously to ensure recent changes have no adverse effects. Since test suites are costly to execute, industry needs methods for test case prioritisation (TCP). Recently, TCP methods use machine learning (ML) to exploit the information known about the system under test (SUT) and its test cases. However, the value added by ML-based TCP methods should be critically assessed with respect to the cost of collecting the information. This p… Show more
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