Using Machine Learning to Refine Black-Box Test Specifications and Test Suites

In the context of open source development or software evolution, developers are often faced with 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 possibly reduced to meet tight deadlines. We will 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 testing, we propose a methodology based on machine learning that has shown promising results on a case study. Keywords Black-box testing, Category-Partition, Machine Learning.

• Type: Conference
• Year: 2008
• Where: QSIC
• Authors: Lionel C. Briand, Yvan Labiche, Zaheer Bawar
• Keywords: Black-box Testing, Machine Learning, QSIC 2008, Software Engineering, Test Suites