Class Integration Test Order Generation Approach Fused with Deep Reinforcement Learning andGraph Convolutional Neural Network

Class integration testing ensures normal interaction and collaboration between multiple classes in the software system and a reasonable class integration test order can reduce testing costs.Therefore,in order to reduce the testing cost of class integration test orders in programs,domestic and foreign researchers have proposed a variety of methods for generating class integration test orders.However,the testing cost of class integration test orders generated by existing methods is too high.To solve this problem,a class integration test order generation approach combining deep reinforcement learning and graph convolutional neural network is proposed.This approach first uses graph convolutional network as the neural network part of deep reinforcement learning,and improves the network structure of the agent and environmental status,so that the environment and the agent can interact based on graph-structured data,and then through design the basic elements such as action space and reward function in reinforcement learning,and complete the generation scenario of the class integration test order.Ultimately,the agent can obtain the best class integration test order through continuous learning and trying.Experimental results show that when the overall stubbing complexity is used as the evaluation metric,this approach can reduce the stubbing cost required to generate class integration test order to a certain extent.