RepoLike: a multi-feature-based personalized recommendation approach for open-source repositories

Cheng YANG; Qiang FAN; Tao WANG; Gang YIN; Xun-hui ZHANG; Yue YU; Hua-min WANG

doi:10.1631/FITEE.1700196

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RepoLike: a multi-feature-based personalized recommendation approach for open-source repositories

Regular Papers | Updated：2022-05-19

- RepoLike: a multi-feature-based personalized recommendation approach for open-source repositories
- RepoLike:一种基于多特性的开源代码仓库个性化推荐方法
- Frontiers of Information Technology & Electronic Engineering Vol. 20, Issue 2, Pages: 222-237(2019)
- Affiliations：
  
  Key Laboratory of Parallel and Distributed Computing, National University of Defense Technology, Changsha 410073, China
- Author bio：
  
  Cheng YANG, E-mail: delpiero710@126.com
- Funds：
- DOI：10.1631/FITEE.1700196
  CLC： TP391.7
- Received：13 February 2017，
  
  Revised：2017-;7-20，
  
  Published：2019-02
- Accepted：
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Cheng YANG, Qiang FAN, Tao WANG, et al. RepoLike: a multi-feature-based personalized recommendation approach for open-source repositories[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(2): 222-237.
DOI：

Cheng YANG, Qiang FAN, Tao WANG, et al. RepoLike: a multi-feature-based personalized recommendation approach for open-source repositories[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(2): 222-237. DOI： 10.1631/FITEE.1700196.

摘要

随着软件协同开发与社交网络深度融合，社会化编程代表一种新型的软件生产与创造范式。由于其良好的灵活性和开放性，开源社区吸引了大量外部贡献者，他们在开源开发中扮演着重要角色。然而，在线开源开发是一项全球化、分布式的合作工作，如不加以监督，贡献过程可能导致效率低下。贡献者需花费大量时间从社区数千个开源项目中找到合适的项目或任务。本文提出一种名为"RepoLike"的新方法，为开发人员推荐基于线性组合和学习排序的开源项目。使用项目受欢迎程度、项目间技术依赖关系和开发人员间的社会关系来度量开发人员与给定开源项目间的相关性。实验结果表明，该方法在推荐20个候选者时可达25%以上命中率，这意味着它可以向贡献者推荐紧密相关的开源仓库。

Abstract

With the deep integration of software collaborative development and social networking

social coding represents a new style of software production and creation paradigm. Because of their good flexibility and openness

a large number of external contributors have been attracted to the open-source communities. They are playing a significant role in open-source development. However

the open-source development online is a globalized and distributed cooperative work. If left unsupervised

the contribution process may result in inefficiency. It takes contributors a lot of time to find suitable projects or tasks from thousands of open-source projects in the communities to work on. In this paper

we propose a new approach called ``RepoLike

'' to recommend repositories for developers based on linear combination and learning to rank. It uses the project popularity

technical dependencies among projects

and social connections among developers to measure the correlations between a developer and the given projects. Experimental results show that our approach can achieve over 25% of hit ratio when recommending 20 candidates

meaning that it can recommend closely correlated repositories to social developers.

关键词

Keywords

references

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