Open source software development (OSS) team members often need to figure out how to initiate a collaboration with a new remote collaborator. An inappropriate strategy could lead to failures in developing cooperation. In this paper, we propose an approach and corresponding intelligent system called IIAG (Initial Interaction Assistant based on Game theory analytics), which identifies and advises its users about strategies for initial interactions with new remote collaborators. IIAG integrates game theory, decision models, and social factors with the collaborative traces mined from empirical project data to achieve this goal. When a user seeks IIAG’s advice, it simulates an individual’s decision processes to find the strategies that yield the best outcomes. Thus, it can advise proper strategies for users. IIAG is evaluated extensively. We design and perform virtual experiments to evaluate IIAG with empirical data collected from three large open source projects. The results show that IIAG can identify the payoff-optimal strategy with over 80% accuracy. We also conduct a lightweight user study to evaluate the IIAG’s usefulness from the potential users’ perspective. The results are also promising. Thus, IIAG can help OSS team members in making informed decisions about interacting with new remote collaborators.

开源软件开发（OSS）团队成员经常需要弄清楚如何与新的远程协作者发起协作。不适当的战略可能会导致发展合作失败。在本文中，我们提出的方法和相应的称为IIAG（智能系统我nitial我nteraction一个基于ssistant g ^ame理论分析），它可以识别并向用户提供有关与新的远程协作者进行初始互动的策略的建议。IIAG将博弈论，决策模型和社会因素与从经验项目数据中挖掘出来的协作痕迹相结合，以实现这一目标。当用户寻求IIAG的建议时，它会模拟个人的决策过程以找到产生最佳结果的策略。因此，它可以为用户提供适当的策略建议。IIAG得到了广泛的评估。我们设计并执行虚拟实验，以使用从三个大型开源项目中收集的经验数据来评估IIAG。结果表明，IIAG可以识别出80％以上的收益最优策略。我们还进行了轻量级用户研究，以从潜在用户的角度评估IIAG的有用性。结果也很有希望。因此，IIAG可以帮助OSS团队成员就与新的远程协作者进行交互做出明智的决策。