Comparative analysis and development of new approach for random testing

Testing is the process of finding errors in a system or its component. It runs a system in order to find gaps or bugs. It also checks that system is fulfilling its requirement or not. During execution of software testing randomized algorithm is used to generate test cases. It generates random number of choices during test cases execution to produce a result. It selects test cases from the whole input set randomly. The process of test cases generation can be minimized using randomized algorithm. For generating test cases uses two types of randomized algorithm. We used different randomized algorithm on the basis of complexity of code. If the complexity is low then use Monte Carlo randomized algorithm because it has deterministic running time, but whose output may be incorrect with little probability. Las Vegas randomized algorithm is use for high complexity uses because it always gives the correct result. This algorithm compares the correlation between two far inputs and failure-causing inputs. It can avoid complex analysis and calculation of code. In this we focuses on various randomized testing algorithms and its working and functions. We present a method that improves performance random test generation by incorporating result obtained from executing test cases.