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.