Testing android applications using multi-objective evolutionary algorithms with a stopping criteria

The ever increasing usage of Android devices and apps has created a demand for faster and reliable testing techniques. While the quality of test cases can be summed up based on the amount of code they cover, fault detection in applications is one of the main objectives for testing. We introduce an Android app testing approach which uses multiobjective genetic algorithm with elitism which finds optimal test cases by minimizing their length, maximizes the code coverage and fault detection capability, and minimizes the whole test suite for re-usability. In addition to that, we also incorporate a progress indicator which checks for improvements in test suite quality after subsequent generations and use it as a stopping criterion. The effectiveness of our approach is shown in our evaluation where it is able to perform better than the existing state-of-The-Art tools.