Developer Cooperation Relationship and Attribute Similarity Based Community Detection in Software Ecosystem

SHEN Xin; DU Junwei; GONG Dunwei; YAO Xiangjuan

doi:10.23919/cje.2021.00.276

Volume 32 Issue 1

Jan. 2023

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Article Navigation > Chinese Journal of Electronics > 2023 > 32(1): 39-50

SHEN Xin, DU Junwei, GONG Dunwei, et al., “Developer Cooperation Relationship and Attribute Similarity Based Community Detection in Software Ecosystem,” Chinese Journal of Electronics, vol. 32, no. 1, pp. 39-50, 2023, doi: 10.23919/cje.2021.00.276

Citation:

SHEN Xin, DU Junwei, GONG Dunwei, et al., “Developer Cooperation Relationship and Attribute Similarity Based Community Detection in Software Ecosystem,” Chinese Journal of Electronics, vol. 32, no. 1, pp. 39-50, 2023, doi: 10.23919/cje.2021.00.276

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Developer Cooperation Relationship and Attribute Similarity Based Community Detection in Software Ecosystem

doi: 10.23919/cje.2021.00.276

1.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
2.
School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China
3.
School of Mathematics, China University of Mining and Technology, Xuzhou 221116, China

Funds: This work was supported by the National Key Research and Development Program of China (2018YFB1003802)

More Information

Author Bio:
Xin SHEN received the M.S. degree from Jiangsu Normal University in 2019. He is a Ph.D. candidate of the School of Information and Control Engineering, China University of Mining and Technology. His research interests include mathematical modelling of complex systems, evolutionary optimization, and software ecosystem. (Email: shenxinpassion@163.com)

Junwei DU received the Ph.D. degree from Tongji University, in 2009. He is a Professor in the School of Information Science and Technology, Qingdao University of Science and Technology. His main research interests include software testing, natural language processing, knowledge graph and knowledge engineering. (Email: djwqd@163.com)

Dunwei GONG (corresponding author) received the Ph.D. degree from China University of Mining and Technology in 1999. He is a Professor in the School of Information and Control Engineering, China University of Mining and Technology. His current research interests include computation intelligence in many-objective optimization, dynamic and uncertain optimization, as well as their applications in software engineering, scheduling, path planning, big data processing and analysis. (Email: dwgong@vip.163.com)

Xiangjuan YAO received the Ph.D. degree from China University of Mining and Technology in 2011. She is a Professor in the School of Mathematics, China University of Mining and Technology. Her main research interests include intelligence optimization and software ecosystem. (Email:yaoxj@cumt.edu.cn)
Received Date: 2021-08-06
Accepted Date: 2022-04-12

Available Online: 2022-05-21

Publish Date: 2023-01-05

Abstract

Abstract

A software ecosystem (SECO) can be described as a special complex network. Previous complex networks in an SECO have limitations in accurately reflecting the similarity between each pair of nodes. The community structure is critical towards understanding the network topology and function. Many scholars tend to adopt evolutionary optimization methods for community detection. The information adopted in previous optimization models for community detection is incomprehensive and cannot be directly applied to the problem of community detection in an SECO. Based on this, a complex network in SECOs is first built. In the network, the cooperation intensity between developers is accurately calculated, and the attribute contained by each developer is considered. A multi-objective optimization model is formulated. A community detection algorithm based on NSGA-II is employed to solve the above model. Experimental results demonstrate that the proposed method of calculating the developer cooperation intensity and our model are advantageous.
- Software ecosystem,
- Complex network,
- Cooperation intensity,
- Attribute similarity,
- Community detection

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References(33)

References

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