Towards effective developer recommendation in software crowdsourcing

Abstract

Crowdsourcing has attracted increasing attention from both industry and academia since it was proposed. Now a lot of work is finished by crowdsourcing, such as logo design, website promotion, industrial design, copywriting, software development, translation and image annotation. Although software crowdsourcing achieves positive results in practice, we still face a challenge of assigning suitable developers to specific tasks. In this paper, we propose a novel approach that recommends developers. In particular, our approach supports: comprehensively measuring the tasks and developers in software crowdsourcing, and recommending developers on the basis of the developer-task competence, task-task similarity, and soft power.