Optimal Group Size for Software Change Tasks: A Social Information Foraging Perspective
将开源软件变更任务中开发者的集体解决视为社会信息觅食问题，预测最优团队规模并量化其对个人绩效的影响，发现实际规模与最优规模不匹配，且最优性与生产率提升相关。
Group size is a key factor in collaborative software development and many other cybernetic applications where task assignments are important. While methods exist to estimate its value for proprietary projects, little is known about how group size affects distributed and decentralized cybernetic applications and in particular open source software (OSS) development. This paper presents a novel approach in which we frame developers' collective resolution of OSS change tasks as a social information foraging problem. This new perspective enables us to predict the optimal group size and quantify group size's effect on individual performance. We test the theory with data mined from two projects: 1) Firefox and 2) Mylyn. This paper not only uncovers the mismatch of optimal and actual group sizes, but also reveals the association of optimality with improved productivity. In addition, the social-level productivity gain is observed as project evolves. We show this paper's impact by extending the frontiers of knowledge in two areas: 1) social coding and 2) recommendation systems.