A class integration test order generation approach based on Sarsa algorithm

Class integration test order generation is a key step in integration testing, researching this problem can help find unknown bugs and improve the efficiency of software testing. The challenge of this problem is ordering the classes to be integrated to minimize the cost of required stubs. However, the existing approaches of generating class integration test orders cannot satisfy this requirement well. Considering the excellent performance of reinforcement learning in sequence decision problems, this paper proposes a class integration test order generation approach based on Sarsa algorithm, which is a data-driven model-free reinforcement learning algorithm. This approach takes the stubbing complexity as the indicator to evaluate the stubbing cost and uses it to measure the quality of a class integration test order. The Sarsa algorithm is used to train the agent, and three indicators such as test return, dependency complexity, and the number of cycles are integrated into the design of the reward function to evaluate the merits of the current action. By recording an action path of the agent from its initial state to its termination state, a class integration test order can be obtained. The experimental results on 10 systems show that the class integration test order generation approach based on Sarsa algorithm can generate the class integration test orders with lower stubbing cost.