An effective approach for determining the class integration test order using reinforcement learning

Within the software testing domain, determining the order in which classes are tested in integration testing is an important problem called class integration test order identification (CITO). This problem is useful in integration testing, as it contributes to reducing the time needed for testing a software system and refers to the process of identifying an optimal order in which the application classes should be combined and tested as a group. This paper introduces a novel approach based on reinforcement learning for class integration test order optimization in the context of integration testing. The experimental evaluation is performed on four synthetic examples and on six existing software systems often used in the literature for this problem. The results obtained are analyzed and compared to similar related work from the literature, highlighting the potential of the current proposal. The proposed reinforcement learning based approach outperforms most methods existing in the software engineering literature for optimizing the test order for class-based integration. Moreover, it is general, and can be easily extended for optimizing the order in which software components should be tested during component integration testing of a software system.