Software engineers waste a lot of time during software testing. The goal of testing is to determine error in a system. Test case generation is the procedure of developing test suites for identifying system errors. A test set is a collection of applicable test cases bunched together. It is also seen in the industry with large amount of funds being used during the software process. During software testing, we have used test case as input and has determined the final output. So, our first objective is to choose the right test case for the software testing process. In order to give correct output, it is very difficult to select test cases. So, the test case generation is an NP (non-deterministic polynomial-time hardness) problem. There are numbers of algorithms available for software testing but to choose the best algorithm as per the requirement is mostly needed. In this research work, to solve the NP hard problem of software testing, we have used Fuzzy logic classifier. Fuzzy logic is a rule based algorithm that works on if - else statement. The test input is applied as an input to the fuzzy membership function. The classifier works on the defined rules and provides us a rule based output. Fuzzy classifier helps to find error in less time on the basis of rule set. To determine the performance of the designed test case generation system the performance parameters such as accuracy, FAR (False acceptance rate) and FRR (False Rejection rate) are evaluated in MATLAB.

Software engineering, software testing, test case prioritization, fuzzy logic, Accuracy

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