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Software crowdsourcing platforms employ extrinsic rewards such as rating orranking systems to motivate workers. Such rating systems are noisy and providelimited knowledge about workers' preferences and performance. To develop betterunderstanding of worker reliability and trustworthiness in softwarecrowdsourcing, this paper reports an empirical study conducted on more than oneyear's real-world data from TopCoder, one of the leading software crowdsourcingplatforms.  To do so, first, we create a bipartite network of active workers based oncommon task registrations. Then, we use the Clauset-Newman-Moore graphclustering algorithm to identify worker clusters in the network. Finally, weconduct an empirical evaluation to measure and analyze workers' behavior peridentified community in the platform by workers' rating. More specifically,workers' behavior is analyzed based on their performances in terms ofreliability, trustworthiness, and success; their preferences in terms ofefficiency and elasticity; and strategies in terms of comfort, confidence, anddeceitfulness. The main result of this study identified four communities ofactive workers: mixed-ranked, high-ranked, mid-ranked, and low-ranked. Thisstudy shows that the low-ranked community associates with the highest reliableworkers with an average reliability of 25%, while the mixed-ranked communitycontains the most trustworthy workers with average trustworthiness of 16%. Suchempirical evidence is beneficial to help exploring resourcing options whileunderstanding the relations among unknown resources to improve task success.

An Empirical Investigation of Worker Communities in TopCoder