摘要
在当今数字化时代,开源技术、开源软件和开源社区日益重要,而通过量化分析方法研究开源领域的问题也已经成为一个重要的趋势。开发者是开源项目中的核心,其贡献度的量化以及量化后的贡献度提升策略,是开源项目能够健康发展的关键。文中提出了一种数据驱动的开源贡献度量化评估与持续优化方法,并通过一个实际的工具框架Rosstor(Robotic Open Source Software Mentor)进行了实现。该框架包含两个主要部分:1)贡献度评估模型,采取了熵权法,可以动态客观地评估开发者的贡献度;2)贡献度持续优化模型,采取了深度强化学习方法,最大化了开发者的贡献度。文中选取了GitHub上若干著名的开源项目的贡献者数据,通过大量且充分的实验验证了Rosstor不仅能够使所有项目上开发者的贡献度得到大幅度提升,而且还具有一定的抗干扰性,充分证明了所提方法和框架的有效性。Rosstor框架为当下广泛开展的开源项目和开源社区的可持续健康发展提供了方法和工具方面的支持。
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.
作者
范家宽
王皓月
赵生宇
周添一
王伟
FAN Jia-kuan;WANG Hao-yue;ZHAO Sheng-yu;ZHOU Tian-yi;WANG Wei(School of Data Science and Engineering,East China Normal University,Shanghai 200062,China;College of Electronical and Information Engineering,Tongji University,Shanghai 201804,China)
出处
《计算机科学》
CSCD
北大核心
2021年第5期45-50,共6页
Computer Science
基金
2020年度重庆市出版专项资金资助项目。
关键词
开源软件
贡献度测量
贡献增强
深度强化学习
模仿学习
Open source software
Contribution measurement
Contribution enhancement
Deep reinforcement learning
Imitation learning
