Reinforcement Learning Method for Class Integration Test Order Determination
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%.
test orderReinforcement Learning(RL)node importance valuereward functionintegration test
国家科技期刊平台
NSTL
万方数据
维普
