Volume 32 Issue 1
Jan.  2023
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Article Contents
SHEN Xin, DU Junwei, GONG Dunwei, YAO Xiangjuan. Developer Cooperation Relationship and Attribute Similarity Based Community Detection in Software Ecosystem[J]. Chinese Journal of Electronics, 2023, 32(1): 39-50. doi: 10.23919/cje.2021.00.276
Citation: SHEN Xin, DU Junwei, GONG Dunwei, YAO Xiangjuan. Developer Cooperation Relationship and Attribute Similarity Based Community Detection in Software Ecosystem[J]. Chinese Journal of Electronics, 2023, 32(1): 39-50. doi: 10.23919/cje.2021.00.276

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

doi: 10.23919/cje.2021.00.276
Funds:  This work was supported by the National Key Research and Development Program of China (2018YFB1003802)
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  • 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
  • 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.
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  • [1]
    O. Franco-Bedoya, D. Ameller, D. Costal, et al., “Open source software ecosystems: A Systematic mapping,” Information and Software Technology, vol.91, pp.160–185, 2017. doi: 10.1016/j.infsof.2017.07.007
    [2]
    X. X. Zeng, W. Wang, C. Chen, et al., “A consensus community-based particle swarm optimization for dynamic community detection,” IEEE Transactions on Cybernetics, vol.50, no.6, pp.2502–2513, 2020. doi: 10.1109/TCYB.2019.2938895
    [3]
    W. J. Luo, D. F. Zhang, H. Jiang, et al., “Local community detection with the dynamic membership function,” IEEE Transactions on Fuzzy Systems, vol.26, no.5, pp.3136–3150, 2018. doi: 10.1109/TFUZZ.2018.2812148
    [4]
    J. Y. Chen, L. H. Chen, Y. X. Chen, et al., “GA-based Q-attack on community detection,” IEEE Transactions on Computational Social Systems, vol.6, no.3, pp.491–503, 2019. doi: 10.1109/TCSS.2019.2912801
    [5]
    J. Chen, R. Li, S. Zhao, et al., “A new clustering cover algorithm based on graph representation for community detection,” Acta Electronica Sinica, vol.48, no.9, pp.1680–1687, 2020. (in Chinese)
    [6]
    F. F. Wang, B. H. Zhang, and S. C. Chai, “Deep auto-encoded clustering algorithm for community detection in complex networks,” Chinese Journal of Electronics, vol.28, no.3, pp.489–496, 2019. doi: 10.1049/cje.2019.03.019
    [7]
    J. D. Fan, W. X. Xie, and Z. X. Liu, “A low complexity distributed multitarget detection and tracking algorithm,” Chinese Journal of Electronics, in press, DOI: 10.23919/cje.2021.00.282, 2022.
    [8]
    R. Bana and A. Arora, “Influence indexing of developers, repositories, technologies and programming languages on social coding community GitHub,” in Proceedings of the 11th International Conference on Contemporary Computing, Noida, India, pp.1–6, 2018.
    [9]
    T. T. Hou, X. J. Yao, and D. W. Gong, “Community detection in software ecosystem by comprehensively evaluating developer cooperation intensity,” Information and Software Technology, vol.130, article no.106451, 2021.
    [10]
    M. Girven and M.E.J. Newman, “Community structure in social and biological networks,” Proceedings of the National Academy of Sciences of the United States of America, vol.99, no.12, pp.7821–7826, 2002. doi: 10.1073/pnas.122653799
    [11]
    B. W. Kernighan and S. Lin, “An efficient heuristic procedure for partitioning graphs,” Bell System Technical Journal, vol.49, no.2, pp.291–307, 1970. doi: 10.1002/j.1538-7305.1970.tb01770.x
    [12]
    M. E. J. Newman, “Fast algorithm for detecting community structure in networks,” Physical Review E, vol.69, article no.066133, 2004. doi: 10.1103/PhysRevE.69.066133
    [13]
    V. D. Blondel, J. L. Guillaume, R. Lambiotte, et al., “Fast unfolding of communities in large networks,” Journal of Statistical Mechanics: Theory and Experiment, vol.2008, no.10, article no.P10008, 2008. doi: 10.1088/1742-5468/2008/10/P10008
    [14]
    C. Pizzuti, “GA-net: A genetic algorithm for community detection in social networks,” in Proceedings of International Conference on Parallel Problem Solving from Nature, Dortmund, Rende, Italy, pp.1081–1090, 2008.
    [15]
    L. L. Li, L. C. Jiao, J. Q. Zhao, et al., “Quantum-behaved discrete multi-objective particle swarm optimization for complex network clustering,” Pattern Recognition, vol.63, pp.1–14, 2017. doi: 10.1016/j.patcog.2016.09.013
    [16]
    M. G. Gong, B. Fu, L. C. Jiao, et al., “Memetic algorithm for community detection in networks,” Physical Review E, vol.84, article no.056101, 2011. doi: 10.1103/PhysRevE.84.056101
    [17]
    F. Folino and C. Pizzuti, “An evolutionary multiobjective approach for community discovery in dynamic networks,” IEEE Transactions on Knowledge and Data Engineering, vol.26, no.8, pp.1838–1852, 2014. doi: 10.1109/TKDE.2013.131
    [18]
    S. Tahmasebi, P. Moradi, S. Ghodsi, et al., “An ideal point based many-objective optimization for community detection of complex networks,” Information Sciences, vol.502, pp.125–145, 2019. doi: 10.1016/j.ins.2019.06.010
    [19]
    Z. T. Li, J. Liu, and K. Wu, “A multi-objective evolutionary algorithm based on structural and attribute similarities for community detection in attributed networks,” IEEE Transactions on Cybernetics, vol.48, no.7, pp.1963–1976, 2018. doi: 10.1109/TCYB.2017.2720180
    [20]
    A. Reihanian, M. Feizai-Derakhshi, and H. S. Aghdasi, “Community detection in social networks with node attributes based on multi-objective biogeography based optimization,” Engineering Application of Artificial Intelligence, vol.62, pp.51–67, 2017. doi: 10.1016/j.engappai.2017.03.007
    [21]
    A. Idu, T. V. D. Zande, and S. Jansen, “Multi-homing in the Apple ecosystem: Why and how developers target multiple Apple app stores,” in Proceedings of International Conference on Management of Emergent Digital Ecosystems, San Francisco, USA, pp.122–128, 2011.
    [22]
    K. Plakidas, D. Schall, and U. Zdun, “Evolution of the R software ecosystem: Metrics, relationships, and their impact on qualities,” Journal of Systems and Software, vol.132, pp.119–146, 2017. doi: 10.1016/j.jss.2017.06.095
    [23]
    S. Jansen, “A focus area maturity model for software ecosystem governance,” Information and Software Technology, vol.118, article no.106219, 2020. doi: 10.1016/j.infsof.2019.106219
    [24]
    W. Ma, L. Chen, X. Y. Zhang, et al., “How do developers fix cross-project correlated bugs? A case study on the GitHub scientific Python ecosystem,” in Proceedings of the 39th 2017 IEEE/ACM International Conference on Software Engineering, Buenos Aires, Argentina, pp.382–392, 2017.
    [25]
    Z. Y. Liu and Y. H. Ma, “A divide and agglomerate algorithm for community detection in social networks,” Information Sciences, vol.482, pp.321–333, 2019. doi: 10.1016/j.ins.2019.01.028
    [26]
    X. M. You, Y. H. Ma, and Z. Y. Liu, “A three-stage algorithm on community detection in social networks,” Knowledge-Based Systems, vol.187, article no.104822, 2020.
    [27]
    C. Pizzuti, “Evolutionary computation for community detection in networks: a review,” IEEE Transactions on Evolutionary Computation, vol.22, no.3, pp.464–483, 2018. doi: 10.1109/TEVC.2017.2737600
    [28]
    S. He, G. B. Jia, Z. X. Zhu, et al., “Cooperative co-evolutionary module identification with application to cancer disease module discovery,” IEEE Transactions on Evolutionary Computation, vol.20, no.6, pp.874–891, 2016.
    [29]
    K. Deb, A. Pratap, S. Agarwal, et al., “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol.6, no.2, pp.182–197, 2002. doi: 10.1109/4235.996017
    [30]
    D. Jin, J. Liu, B. Yang, et al., “Genetic algorithm with local Search for community detection in large-scale complex networks,” Acta Automatica Sinica, vol.37, no.7, pp.873–882, 2011. (in Chinese)
    [31]
    X. Huang, H. Cheng, and J. X. Yu, “Dense community detection in multi-valued attributed networks,” Information Sciences, vol.314, pp.77–99, 2015. doi: 10.1016/j.ins.2015.03.075
    [32]
    X. Y Zhang, K. F. Zhou, H. B. Pan, et al., “A network reduction-based multiobjective evolutionary algorithm for community detection in large-scale complex networks,” IEEE Transactions on Cybernetics, vol.50, no.2, pp.703–716, 2020. doi: 10.1109/TCYB.2018.2871673
    [33]
    B. A Attea and H. S. Khoder, “A new multi-objective evolutionary framework for community mining in dynamic social networks,” Swarm and Evolutionary Computation, vol.31, pp.90–109, 2016. doi: 10.1016/j.swevo.2016.09.001
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