DIFFERENTIAL GENETIC ALGORITHM (DGA) BASED OPTIMAL DIRECTED RANDOM TESTING FOR REDUCING INTERACTIVE FAULTS

The goal of software testing is to identify software flaws. Software testing is the process of confirming that a program works as intended. Test inputs are generated at random from the software's input space during random testing. to consistently provide test instances that are random and have some similarities. We will provide a method for minimizing the errors based on the best test cases produced by directed random testing in order to get around these problems. Using the object behavior dependence model as a basis, we will create an effective random testing test case in the suggested approach. The Differential Genetic Algorithm (DGA) will be used in this study to generate the best inputs, minimizing both equivalent and illicit inputs. AGA makes advantage of the test case coverage metrics to lessen fault proneness. By merging the old input with the present one, our suggested approach will reduce the input space and improve scalability and efficacy in the software testing age.