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How to Overcome the Equivalent Mutant Problem and Achieve Tailored Selective Mutation Using Co-evolution
Konstantinos Adamopoulos, Mark Harman, and Robert M. Hierons
Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex, UB8 3PH, UKkonstantinos.adamopoulos@brunel.ac.uk
Abstract. The use of Genetic Algorithms in evolution of mutants and test cases offers new possibilities in addressing some of the main problems of mutation testing. Most specifically the problem of equivalent mutant detection, and the problem of the large number of mutants produced. In this paper we describe the above problems in detail and introduce a new methodology based on co-evolutionary search techniques using Genetic Algorithms in order to address them effectively. Co-evolution allows the parallel evolution of mutants and test cases. We discuss the advantages of this approach over other existing mutation testing techniques, showing details of some initial experimental results carried out.
LNCS 3103, p. 1338 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2004