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| 一种多特征融合的软件开发者推荐 |
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谢新强1,2, 杨晓春1, 王斌1, 张霞1,2, 纪勇2, 黄治纲2
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1.东北大学 计算机科学与工程学院, 辽宁 沈阳 110004;2.软件架构国家重点实验室(东软集团), 辽宁 沈阳 110179
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| 摘要: |
| 软件开发者能力评价和协作关系推荐，是大数据环境下软件智能化开发领域的一个研究热点.通过分析互联网开发者社区和企业内部开发环境，设计出基于模糊综合评价的开发者能力模型.随后，通过挖掘开发者与任务的动态交互行为、静态匹配度以及开发者能力这3个不同维度的特征并结合矩阵分解技术，提出一种能力与行为感知的多特征融合协同过滤开发者推荐方法，最终解决开发者推荐面临的评价矩阵稀疏性和冷启动问题，提升个性化精准推荐效率.从系统层面给出适合大数据环境的多特征融合开发者推荐原型系统实践并对现有开源技术框架的优化改进，实验过程分别基于互联网问答社区StackOverflow和企业内部GitLab环境进行了实验分析.最后，对未来研究可能的问题及思路进行了展望. |
| 关键词: 多特征融合 协作推荐 能力评价 行为感知 大数据 |
| DOI：10.13328/j.cnki.jos.005525 |
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| 基金项目:国家自然科学基金（61272178，61572122）；国家重点研发计划（2016YFB1000804） |
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| Multi-Feature Fused Software Developer Recommendation |
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XIE Xin-Qiang1,2, YANG Xiao-Chun1, WANG Bin1, ZHANG Xia1,2, JI Yong2, HUANG Zhi-Gang2
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1.School of Computer Science and Technology, Northeastern University, Shenyang 110004, China;2.State Key Laboratory of Software Architecture(Neusoft Corporation), Shenyang 110179, China
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| Abstract: |
| The capability evaluation and collaborative relationship recommendation of software developers is a hot topic in the field of software intelligent development in big data environment. By analyzing the internet developer community and the enterprise internal development environment, a developer ability model based on fuzzy comprehensive evaluation is designed in this paper. Subsequently, the three different dimensions of the dynamic interaction behavior, static matching, and developer capabilities are extracted by mining the dynamic interaction between the developer and the task. Furthermore, by combining matrix decomposition techniques, a multi-feature fusion enhanced method based on capability and behavior for collaborative filtering developer recommendation is proposed. The method ultimately solves the evaluation matrix sparseness and cold start problem of developer recommendation, and improves the personalized precision recommendation efficiency. From the system level, a prototype of multi feature fusion recommendation system suitable for big data environment is presented, and the optimization of existing open source technology framework is improved. Experiment is conducted based on the internet Q&A community StackOverflow and the internal institution GitLab environment. Finally, the possible issues and ideas for future research are addressed. |
| Key words: multi-feature fusion collaborative recommendation capability evaluation behavior perception big data |
