id: Simula.SE.641
authors: Lionel Briand, Yvan Labiche, Zaheer Bawar, and Nadia Spido
title: Using Machine Learning to Refine Category-Partition  Test Specifications and Test Suites
publication_year: 2009
abstract: In the context of open source development or software evolution, developers often face test suites  which have been developed with no apparent rationale and which may need to be augmented or  refined to ensure sufficient dependability, or even reduced to meet tight deadlines. We refer to this  process as the re-engineering of test suites. It is important to provide both methodological and tool  support to help people understand the limitations of test suites and their possible redundancies, so as  to be able to refine them in a cost effective manner. To address this problem in the case of black-box,  Category-Partition testing, we propose a methodology and a tool based on machine learning that has  shown promising results on a case study involving students as testers.
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journal: Information and Software Technology (Elsevier)
volume: 21
number: 11
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publication_state: Published
simula_ou: [<Department at /simula/department/certus>, <Department at /simula/research/approve>]
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