Genetic Algorithm for Improving the Mutation Testing

Abstract

Software Testing is an approach where different errors and bugs in the software are identified. To test software we need the test data and test cases. In our research work, we proposed an approach for refining the test case using Evolutionary Algorithms (EA) and test the software to detect the presence of errors, if any. We have taken two measures viz. path coverage and adequacy criterion to test the validation of our approach. In our approach, we have used Genetic Algorithm (GA) to refine the test cases. We are executing the test cases on three mutation operators using genetic algorithm. Our method combines random generation and refinement. Each test case is generated randomly in the first step, and then a set of test cases is refined by the genetic algorithm. To measure the adequacy of the test case set, we have used mutation scores, which are based on the mutation analysis of software testing. In our proposed method, it is applied on a C language program, automatically generates test case sets with 100% branch and boundary value coverage. We are using genetic algorithm to get the optimal test cases that covers all the feasible test paths from some initial random test case. Path coverage based testing approach generates reliable test cases. A test case set is reliable if its execution ensures that the program is correct on all its inputs. But, Adequacy requires that the test case set detect faults rather than show correctness. Hence, for adequacy based testing we are using the concept of mutation analysis. For this we are using mutation score by injecting mutants in the resultant effective test cases.