Using machine learning to refine Category-Partition test specifications and test suites
Information and Software Technology
Linking software testing results with a machine learning approach
Engineering Applications of Artificial Intelligence
A learning-based method for combining testing techniques
Proceedings of the 2013 International Conference on Software Engineering
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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 testing, we propose a methodology based on machine learning that has shown promising results on a case study.