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Novel similarity measures, entropy of intuitionistic fuzzy sets and their application in software quality evaluation

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Abstract

Intuitionistic fuzzy sets (IFSs), including member and nonmember functions, have many applications in managing uncertain information. The similarity measures of IFSs proposed to represent the similarity between different types of sensitive fuzzy information. However, some existing similarity measures do not meet the axioms of similarity. Moreover, in some cases, they could not be applied appropriately. In this study, we proposed some novel similarity measures of IFSs constructed by combining the exponential function of membership functions and the negative function of non-membership functions. In this paper, we also proposed a new entropy measure as a stepping-stone to calculate the weights of the criteria in the proposed multi-criteria decision-making (MCDM) model. The similarity measures used to rank alternatives in the model. Finally, we used this MCDM model to evaluate the quality of software projects.

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Acknowledgements

This work was supported in part by the Center for Cyber-physical System Innovation from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education in Taiwan.

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Authors and Affiliations

  1. Faculty of Information Technology, Vietnam National University of Agriculture, Trau Quy, Gia Lam, Hanoi, Vietnam

    Nguyen Xuan Thao

  2. Department of Industrial Management, National Taiwan University of Science and Technology, 43, Sect. 4, Keelung Road, Taipei, 10607, Taiwan

    Shuo-Yan Chou

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  1. Nguyen Xuan Thao
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  2. Shuo-Yan Chou
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Correspondence to Nguyen Xuan Thao.

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Thao, N.X., Chou, SY. Novel similarity measures, entropy of intuitionistic fuzzy sets and their application in software quality evaluation. Soft Comput 26, 2009–2020 (2022). https://doi.org/10.1007/s00500-021-06373-1

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