数据驱动的开源贡献度量化评估与持续优化方法

In recent years,open source technologies,open source software and open source communities have become increasingly significant in digital era,and it has become an important trend to study the open source field through quantitative analysis me-thods.Developers are the core of open source projects,and the quantification of their contributions and the strategies to improve their contributions after quantification are the key to the healthy development of open source projects.We propose a data-driven method for quantitative assessment and continuous optimization of open source contributions.Then,we implement it through a practical framework,Rosstor (Robotic Open Source Software Mentor).The framework consists of two main parts.One is a contribution evaluation model,it adopts an entropy-weight approach and can dynamically and objectively evaluate developers' contributions.Another is a model to enhance contributions,it adopts a deep reinforcement learning approach and can maximize develo-pers' contributions.Contributors' data from a number of famous open source projects on GitHub are selected,and through massive and sufficient experiments,it verifies that Rosstor not only makes the developers' contributions on all projects to be greatly improved,but also has a certain degree of immunity,which fully proves the effectiveness of the framework.The Rosstor framework provides methodological and instrumental support for the sustainable health of open source projects and the open source community.