Context-aware in-process crowdworker recommendation

Abstract

Identifying and optimizing open participation is essential to the success of open software development. Existing studies highlighted the importance of worker recommendation for crowdtesting tasks in order to detect more bugs with fewer workers. However, these studies mainly focus on one-time recommendations with respect to the initial context at the beginning of a new task. This paper argues the need for in-process crowdtesting worker recommendation. We motivate this study through a pilot study, revealing the prevalence of long-sized non-yielding windows, i.e., no new bugs are revealed in consecutive test reports during the process of a crowdtesting task. This indicates the potential opportunity for accelerating crowdtesting by recommending appropriate workers in a dynamic manner, so that the non-yielding windows could be shortened. To that end, this paper proposes a context-aware in-process crowdworker recommendation approach, iRec, to detect more bugs earlier and potentially shorten the non-yielding windows. It consists of three main components: 1) the modeling of dynamic testing context, 2) the learning-based ranking component, and 3) the diversity-based re-ranking component. The evaluation is conducted on 636 crowdtesting tasks from one of the largest crowdtesting platforms, and results show the potential of iRec in improving the cost-effectiveness of crowdtesting by saving the cost and shortening the testing process.