With the complexity of the composition process and the rapid growth of candidate services,realizing optimal or near-optimal service composition is an urgent problem.Currently,the static service composition chain is ri...With the complexity of the composition process and the rapid growth of candidate services,realizing optimal or near-optimal service composition is an urgent problem.Currently,the static service composition chain is rigid and cannot be easily adapted to the dynamic Web environment.To address these challenges,the geographic information service composition(GISC) problem as a sequential decision-making task is modeled.In addition,the Markov decision process(MDP),as a universal model for the planning problem of agents,is used to describe the GISC problem.Then,to achieve self-adaptivity and optimization in a dynamic environment,a novel approach that integrates Monte Carlo tree search(MCTS) and a temporal-difference(TD) learning algorithm is proposed.The concrete services of abstract services are determined with optimal policies and adaptive capability at runtime,based on the environment and the status of component services.The simulation experiment is performed to demonstrate the effectiveness and efficiency through learning quality and performance.展开更多
Partition testing is one of the most fundamental and popularly used software testing techniques.It first divides the input domain of the program under test into a set of disjoint partitions,and then creates test cases...Partition testing is one of the most fundamental and popularly used software testing techniques.It first divides the input domain of the program under test into a set of disjoint partitions,and then creates test cases based on these partitions.Motivated by the theory of software cybernetics,some strategies have been proposed to dynamically select partitions based on the feedback information gained during testing.The basic intuition of these strategies is to assign higher probabilities to those partitions with higher fault-detection potentials,which are judged and updated mainly according to the previous test results.Such a feedback-driven mechanism can be considered as a learning processit makes decisions based on the observations acquired in the test execution.Accordingly,advanced learning techniques could be leveraged to empower the smart partition selection,with the purpose of further improving the effectiveness and efficiency of partition testing.In this paper,we particularly leverage reinforcement learning to enhance the state-of-the-art adaptive partition testing techniques.Two algorithms,namely RLAPT_Q and RLAPT_S,have been developed to implement the proposed approach.Empirical studies have been conducted to evaluate the performance of the proposed approach based on seven object programs with 26 faults.The experimental results show that our approach outperforms the existing partition testing techniques in terms of the fault-detection capability as well as the overall testing time.Our study demonstrates the applicability and effectiveness of reinforcement learning in advancing the performance of software testing.展开更多
基金Supported by the National Natural Science Foundation of China(No.41971356,41671400,41701446)National Key Research and Development Program of China(No.2017YFB0503600,2018YFB0505500)Hubei Province Natural Science Foundation of China(No.2017CFB277)。
文摘With the complexity of the composition process and the rapid growth of candidate services,realizing optimal or near-optimal service composition is an urgent problem.Currently,the static service composition chain is rigid and cannot be easily adapted to the dynamic Web environment.To address these challenges,the geographic information service composition(GISC) problem as a sequential decision-making task is modeled.In addition,the Markov decision process(MDP),as a universal model for the planning problem of agents,is used to describe the GISC problem.Then,to achieve self-adaptivity and optimization in a dynamic environment,a novel approach that integrates Monte Carlo tree search(MCTS) and a temporal-difference(TD) learning algorithm is proposed.The concrete services of abstract services are determined with optimal policies and adaptive capability at runtime,based on the environment and the status of component services.The simulation experiment is performed to demonstrate the effectiveness and efficiency through learning quality and performance.
基金supported by the National Natural Science Foundation of China under Grant Nos.62272037 and 61872039the Beijing Natural Science Foundation under Grant No.4162040+2 种基金the Aeronautical Science Foundation of China under Grant No.2016ZD74004the Fundamental Research Funds for the Central Universities of China under Grant No.FRF-GF-19-B19the Australian Research Council Discovery Project under Grant No.DP210102447.
文摘Partition testing is one of the most fundamental and popularly used software testing techniques.It first divides the input domain of the program under test into a set of disjoint partitions,and then creates test cases based on these partitions.Motivated by the theory of software cybernetics,some strategies have been proposed to dynamically select partitions based on the feedback information gained during testing.The basic intuition of these strategies is to assign higher probabilities to those partitions with higher fault-detection potentials,which are judged and updated mainly according to the previous test results.Such a feedback-driven mechanism can be considered as a learning processit makes decisions based on the observations acquired in the test execution.Accordingly,advanced learning techniques could be leveraged to empower the smart partition selection,with the purpose of further improving the effectiveness and efficiency of partition testing.In this paper,we particularly leverage reinforcement learning to enhance the state-of-the-art adaptive partition testing techniques.Two algorithms,namely RLAPT_Q and RLAPT_S,have been developed to implement the proposed approach.Empirical studies have been conducted to evaluate the performance of the proposed approach based on seven object programs with 26 faults.The experimental results show that our approach outperforms the existing partition testing techniques in terms of the fault-detection capability as well as the overall testing time.Our study demonstrates the applicability and effectiveness of reinforcement learning in advancing the performance of software testing.
