The intension of software testing is to finding the errors in software. Software testing is the process of validating and verifying that a program functions correctly. Random testing generates test inputs randomly from the input space of the software under test. To generate random test cases each time, which will contain some similarity. In order to overcome these issues, we will propose a technique for reducing the faults based on optimal test cases generated from directed random testing. In

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... oposed method, we will generate an efficient random testing test case based on the object behavior dependence model. In this research, the optimal inputs will be generated based on Adaptive Genetic Algorithm (AGA) which will reduce the illegal inputs and equivalent inputs. To reduce the fault proneness, AGA uses the coverage metrics of the test cases. Our proposed method will prunes the input space by combining the previous input with the current one and also increase scalability and effectiveness in the era of software testing.