Multi-Objective Crowd Worker Selection in Crowdsourced Testing

Crowdsourced testing is an emerging trend in software testing, which relies on crowd workers to accomplish test tasks. Typically, a crowdsourced testing task aims to detect as many bugs as possible within a limited budget. For a specific test task, not all crowd workers are qualified to perform it, and different test tasks require crowd workers to have different experiences, domain knowledge, etc. Inappropriate workers may miss true bugs, introduce false bugs, or report duplicated bugs, which could not only decrease the quality of test outcomes, but also increase the cost of hiring workers. Thus, how to select the appropriate crowd workers for specific test tasks is a challenge in crowdsourced testing. This paper proposes a Multi-Objective crowd wOrker SElection approach (MOOSE), which includes three objectives: maximizing the coverage of test requirement, minimizing the cost, and maximizing bug-detection experience of the selected crowd workers. Specifically, MOOSE leverages NSGA-II, a widely used multi-objective evolutionary algorithm, to optimize the three objectives when selecting workers. We evaluate MOOSE on 42 test tasks (involve 844 crowd workers and 3,984 test reports) from one of the largest crowdsourced testing platforms in China, and the experimental results show MOOSE could improve the best baseline by 17% on average in bug detection rate.