Abstract: How to assign tasks to appropriate workers to get better testing results at a lower cost is an important problem.This paper modeled the CWS testing task assignment as a Markov decision process-based problem,and used Deep Q Network to learn and perform real-time online testing task assignment.The proposed approach based on reinforcement learning is named WTA-C.In addition,this paper calculated the probability of the testing worker completing the task within the duration through statistical conditional probability in accordance with the time of the worker’s historical execution of tasks,and used it as the workers’ reputation value to reflect their quality.The worker’s reputation is updated after each assignment.The experimental results show that WTA-C is superior to other real-time assignment methods based on heuristic strategies in controlling the “quality-cost” trade-off of testing tasks and ensuring worker quality,and its assignment effect is more than 18% higher than that of each heuristic strategy,which demonstrates that WTA-C can better adapt to the structure of the CWS and the characteristics of Crowdsourcing environment.

Key words: Composite Web service testing, Crowdtesting, Reinforcement learning, Web service testing