A Clustering Improved Cost Effective Approach for Mutation Testing

Abstract

Abstract Mutation Testing actually gives the internal structural analysis of a software system based on the data and operator feasibility. One of such metric that represents the module reliability and responsibility is its cost analysis. This cost analysis can be performed under identification of relevant operator and expression identification. In this work, a cluster effective approach is defined for improving the clustering mechanism. The work is here defined in two main stages. In first stage, the extraction of the particular expression is done using code extraction approach. In this stage, the code division is also performed to form the relative clusters. In second stage, the identification of appropriate cluster is done to apply the mutation operators and data. The experimentation results show that the work has effectively improved the mutation testing by reducing the number of dead mutants over the code. The implementation of work is done with the helpofMuJava Tool. The implementation results show the significant improvement.