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.