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

Author

Listed:
  • Anupama Kaushik

    (Maharaja Surajmal Institute of Technology)

  • A. K. Soni

    (Sharda University)

  • Rachna Soni

    (DAV College)

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.

Suggested Citation

  • Anupama Kaushik & A. K. Soni & Rachna Soni, 2016. "An improved functional link artificial neural networks with intuitionistic fuzzy clustering for software cost estimation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 7(1), pages 50-61, December.
    • Handle: RePEc:spr:ijsaem:v:7:y:2016:i:1:d:10.1007_s13198-014-0298-2
    DOI: 10.1007/s13198-014-0298-2
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    References listed on IDEAS

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    1. Jorgensen, Magne, 2007. "Forecasting of software development work effort: Evidence on expert judgement and formal models," International Journal of Forecasting, Elsevier, vol. 23(3), pages 449-462.
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