A novel way to identify effective test-case in software testing

Test data generation is one of the costly parts of the software testing, which is performed according to the designed test cases. The problem of designing test cases and then generating optimized test data is one of the challenges of the software testing, including the mutation testing technique. mutation testing has the ability to measure the test cases quality and determine the adequate test cases. However, to perform mutation testing, you need a test set that provides the maximize Coverage of source code and thus have the ability to identify the program errors. In this work, we use code coverage techniques to design test cases and automatically generate optimized test data using the meta-heuristic FA-MABC algorithm. The results are a test suite that cover and test the maximum number of source code lines. Such test suite is more likely to identify errors and get a higher score in the mutation testing. In the proposed method to obtain effective test cases, first generated test cases are applied to mutation testing and then effective test cases are extracted using the Extinguished mutation table. The results of the evaluation show that the FA-MABC algorithm reduces the time of the test data generation, and “modified condition / decision coverage”, increases the mutation score.