摘要
类集成测试确保软件系统中多个类之间正常交互和协作,合理的类集成测试序列可以降低测试成本。为了降低程序中类集成测试序列的测试成本,国内外研究人员提出了多种类集成测试序列生成方法,但已有的方法生成的类集成测试序列的测试成本过高。针对上述问题,提出一种融合深度强化学习和图卷积神经网络的类集成测试序列生成方法。该方法首先将图卷积神经网络作为深度强化学习中的神经网络部分,并对智能体的网络结构和环境状态等方面进行改进,使环境和智能体可以基于图结构的数据进行交互;然后通过设计强化学习中的动作空间和奖励函数等基本要素,完成类集成测试序列的生成场景;最终实现智能体在不断地学习和尝试中得到最佳的类集成测试序列。实验结果表明,在以总体测试桩复杂度作为度量指标时,该方法能够在一定程度上降低生成类集成测试序列所需的测试桩代价。
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.
作者
王晨源
张艳梅
袁冠
WANG Chenyuan;ZHANG Yanmei;YUAN Guan(School of Computer Science and Technology,China University of Mining and Technology,Xuzhou,Jiangsu 221116,China;Mine Digitization Engineering Research Center of the Ministry of Education,China University of Mining and Technology,Xuzhou,Jiangsu 221116,China)
出处
《计算机科学》
北大核心
2025年第6期58-65,共8页
Computer Science
基金
徐州市科技计划项目(KC22047)
徐州市重点研发计划(社会发展)项目(KC23296)
国家级大学生创新创业训练计划项目(202010290060Z)。
关键词
类集成测试序列
深度强化学习
图卷积神经网络
测试桩
测试桩复杂度
Class integration test order
Deep reinforcement learning
Graph convolutional neural network
Test stubs
Stubbing complexity
