Staffing Open Collaborative Projects Based on the Degree of Acquaintance

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We consider the team formation problem in open collaborative projects existing in large community setting such as the Open Source Software (OSS) community. Given a query specifying a set of required skills for an open project and an upper bound of team size, the goal is to find a team that maximizes the Degree of Acquaintance (DoA) and covers all the required skills in the query. We define the DoA in terms of the team graph connectivity and edge weights, corresponding to the local Clustering Coefficient for each team member and the strength of social ties between the team members, respectively. We perform a statistical analysis on historical data to show the importance of the connectivity and social tie strength to the overall productivity of the teams in open projects. We show that the problem defined is NP-hard and present three algorithms, namely, PSTA, STA and NFA, to solve the problem. We experiment the algorithms on a dataset from the OSS community. The results show the effectiveness of the proposed algorithms to find a well acquainted teams satisfying a given query.