计算机工程2024，Vol.50Issue(1)：68-78,11.DOI:10.19678/j.issn.1000-3428.0066797
面向类集成测试序列确定的强化学习方法
Reinforcement Learning Method for Class Integration Test Order Determination
摘要
Abstract
The Reinforcement Learning(RL)strategy for class integration test order is one of the key technologies for test optimization.It can adaptively adjust the integration strategy according to the system integration state.However,the existing schemes have high computational costs,is unsuitable for large-scale software systems,and ignore the risk of testing,which greatly reduces their applicability and reliability.To address these issues,this study proposes a test order-based RL method with important value weighted rewards.First,the RL modeling is optimized,specific position of the node in the test order is ignored,correlation between states is weakened,and usability of the model is improved.Based on this,the test strategy can then be updated end-to-end by combining the deep RL model to reduce the value function error and be more accurate.Finally,the modified software node importance is introduced in the reward function to achieve a multi-objective optimization solution with low Overall Complexity(OCplx)and increased priority of key classes.The comparison and analysis of the models on the SIR open-source system proves that the proposed method can effectively reduce the complexity of the overall test stub and is suitable for large-scale software systems.Furthermore,the proposed reward function incorporating the modified node importance can effectively improve the priority of key classes in test orders,with an average increase of 55.38%.关键词
测试序列/强化学习/节点重要值/奖励函数/集成测试Key words
test order/Reinforcement Learning(RL)/node importance value/reward function/integration test分类
信息技术与安全科学引用本文复制引用
张晓天,王雅文,谢志庆,金大海,宫云战..面向类集成测试序列确定的强化学习方法[J].计算机工程,2024,50(1):68-78,11.基金项目
国家自然科学基金(U1736110) （U1736110）
广西密码学与信息安全重点实验室研究课题(GCIS202103). （GCIS202103）
