An efficient metamorphic testing technique using genetic algorithm

Abstract

Testing helps in preserving the quality and reliability of the software component thus ensuring its successful functioning. The task of testing components for which the final output for arbitrary input cannot be known in advance is a challenging task; as sometimes conditions or predicates in the software restrict the input domain Metamorphic testing is an effective technique for testing systems that do not have test oracles. In it existing test case input is modified to produce new test cases in such a manner that they satisfy the metamorphic relations. In this paper, we propose a genetically augmented metamorphic testing approach, which integrates genetic algorithms into metamorphic testing, to detect subtle defects and to optimize test cases for the component. We have further verified metamorphic testing results by all path coverage criteria information, which is generated during the metamorphic testing of the program and its mutants. The effectiveness of the approach has been investigated through testing a triangle type determination program. © 2011 Springer-Verlag.