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An improved functional link artificial neural networks with intuitionistic fuzzy clustering for software cost estimation

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Abstract

Software cost estimation is an important task for any software development firm. Its inaccurate estimates can lead to catastrophic results for both the developers and the customers. This paper provides an improved approach to software cost estimation using functional link artificial neural networks (FLANN) with intutionistic fuzzy c-means clustering (IFCM). The IFCM has more clustering accuracy as compare to conventional fuzzy c-means (FCM) thereby improving the software prediction results using FLANN. The work is validated with four software datasets i.e. COCOMO81, NASA93, Maxwell and China datasets. The experimental results show the effectiveness of the proposed technique in contrast to the use of conventional FCM with FLANN as reported in the literature. This work also proposes the use of leave one out (LOO) validation technique instead of 3-way.

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Authors and Affiliations

  1. Department of IT, Maharaja Surajmal Institute of Technology, Janakpuri, New Delhi, India

    Anupama Kaushik

  2. School of Engineering and Technology, Sharda University, Greater Noida, India

    A. K. Soni

  3. Department of C. S. and Applications, DAV College, Yamunanagar, India

    Rachna Soni

Authors
  1. Anupama Kaushik
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  2. A. K. Soni
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  3. Rachna Soni
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Correspondence to Anupama Kaushik.

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Kaushik, A., Soni, A.K. & Soni, R. An improved functional link artificial neural networks with intuitionistic fuzzy clustering for software cost estimation. Int J Syst Assur Eng Manag 7 (Suppl 1), 50–61 (2016). https://doi.org/10.1007/s13198-014-0298-2

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  • DOI: https://doi.org/10.1007/s13198-014-0298-2

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