A Tool for Generation and Minimization of Test Suite by Mutant Gene Algorithm

Abstract: Problem statement: This study proposes a new idea for generation of minimized test suite in the test case generation using the mutant gene algorithm, which not only identifies the best test cases but also reduces the number of test cases generated, selects test cases optimally there improving the performance in testing of software. Test cases are generated by using branch coverage algorithm and a coverage table is created for verifying branch coverage. Approach: The process of minimization was done through Mutant gene algorithm. Mutant gene algorithm combined both the mutation testing process and genetic algorithm. Initially a number of chromosomes were generated in random order. Mutation score was used for finding fitness function. The fitness function was found for all the randomly generated chromosomes by applying the mutant score to the function. Rank based selection was used for selecting the chromosomes. After the selection of the chromosomes one-point crossover was performed. A population of chromosomes obtained, which was given as the input for the next iteration. Large iterations were performed to obtain the best test case with higher fitness value, it was the end condition. Results: Between the measured iterations the value of the mutant score remained constant. The results of the experiments showed that the minimization process was competitive with other methods and even outperforms them for complex cases. Conclusion: The whole generation and minimization process was fully automated; redundant explorations of test case were avoided, resulting in efficient generation of test cases. Key words: Test case generation, test suite minimization, test suite reduction, genetic algorithms, tes