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山东大学学报 (工学版) ›› 2023, Vol. 53 ›› Issue (2): 77-86.doi: 10.6040/j.issn.1672-3961.0.2022.342

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基于网络表征学习的软件系统演化分析

邓文涛1,章梦怡1,何鹏1*,曾张帆1,李兵2   

  1. 1.湖北大学计算机与信息工程学院, 湖北 武汉 430062;2.武汉大学计算机学院, 湖北 武汉 430072
  • 收稿日期:2022-10-12 出版日期:2023-04-22 发布日期:2023-04-21
  • 作者简介:邓文涛(1998— ),男,湖北孝感人,硕士研究生,主要研究方向为软件工程、复杂网络. E-mail:endadeng@stu.hubu.edu.cn. *通信作者简介:何鹏(1988— ),男,江西宜春人,副教授, 主要研究方向为软件工程、复杂网络. E-mail:penghe@hubu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(61572371);国家重点研发计划(2017YFB1400602);湖北省技术创新重大专项(2018ACA13);湖北省教育厅青年人才项目(Q20171008)

Software system evolution analysis based on network representation learning

DENG Wentao1, ZHANG Mengyi1, HE Peng1*, ZENG Zhangfan1, LI Bing2   

  1. 1. School of Computer Science and Information Engineering, Hubei University, Wuhan 430062, Hubei, China;
    2. School of Computer Science, Wuhan University, Wuhan 430072, Hubei, China
  • Received:2022-10-12 Online:2023-04-22 Published:2023-04-21

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摘要： 从包、类和方法3个粒度构建软件元素的网络模型。利用经典的网络表征学习方法Node2vec学习节点特征,并从网络距离、增长特性、更新率、模块度等方面对3个开源软件系统进行演化分析。试验结果表明:3种粒度下的演化特性不尽相同,包粒度下的演化更加稳定且高效;相比先前研究,本研究方法得到的软件演化特性与Lehman定律更契合;当软件系统迭代累计到最大阈值时其体系架构将重新部署,此时软件系统的鲁棒性最差且易产生峭壁。

关键词: 软件系统, 软件网络, 网络嵌入, 表征学习, 软件演化

中图分类号: 

  • TP242.6
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