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Software is unequivocally the foremost and indispensable entity in this technologically driven world. Therefore quality assurance, and in particular, software testing is a crucial step in the software development cycle. This paper presents an effective test selection strategy that uses a Spectrum of Complexity Metrics (SCM). Our aim in this paper is to increase the efficiency of the testing process by significantly reducing the number of test cases without having a significant drop in test effectiveness. The strategy makes use of a comprehensive taxonomy of complexity metrics based on the product level (class, method, statement) and its characteristics.We use a series of experiments based on three applications with a significant number of mutants to demonstrate the effectiveness of our selection strategy.For further evaluation, we compareour approach to boundary value analysis. The results show the capability of our approach to detect mutants as well as the seeded errors.

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