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Selecting reviewers for code changes is a critical step for an efficient code review process. Recent studies propose automated reviewer recommendation algorithms to support developers in this task. However, the evaluation of recommendation algorithms, when done apart from their target systems and users (i.e., code review tools and change authors), leaves out important aspects: perception of recommendations, influence of recommendations on human choices, and their effect on user experience. Thisdoi:10.5281/zenodo.1404813 fatcat:bbledx56mjcp7ou5fyf6g5kp3y