Mutation testing in test suite generation using separate bacterial memetic evolutionary algorithm in IoT

Abstract

The security of the Internet of Things (IoT) is becoming more important as it is used more frequently and widely in our daily lives. Better security analysis tools are necessary for IoT applications in order to assure information security. Therefore, a framework for evaluating taint-flow analysis tools in the context of IoT applications is presented. In various strategies, the mutation testing has been found in detecting the faults in the test case. Fault-based checking is considered as mutation testing because the flaws are identified actively in the code to ensure that the programmed functions correctly. If the flaws in the programmed are not identified, it indicates that program requires debugging for effective functioning. Testing has made a significant contribution to a set of approaches, tools, improvements, and perfect results; however, it is an expensive test leading to increased bug-finding practices. Automated tools and minimization methods were launched to decrease the expenses of mutation testing. The Separate Bacterial Memetic Evolutionary Algorithm (SBMEA) was examined in this study effort as a worldwide seeker. The suggested algorithm strengthens the reduction cost of mutation testing in all possible ways. It's able to achieve the minimum test case and mutants score for computational and cost, here the enhanced of Memetic algorithm were proposed. The present system improved the surrogate enhancement by lowering the expense and eliminating mutants in the snippets. The Separate Bacterial Memetic Evolutionary Algorithm (SBMEA) method of Memetic algorithm was performed the high mutation score in all testing of java snippets. Compared to proposed algorithm the execution time is saved in 6.95 s better than existing algorithm. It consists of evolution of strategy and program solver and increase the computational efficiency of the solution method. Here the Separate Bacterial Memetic Evolutionary Algorithm (SBMEA) method of Memetic algorithm is capable of lowering computational costs and eliminating all mutants, its best for reduction cost and for mutation testing.