Analytical Structure of a Fuzzy Logic Controller for Software Development Effort Estimation

  • S. Rama SreeEmail author
  • S.N.S.V.S.C. Ramesh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 410)


Most recently, attention has turned towards Machine learning techniques to predict software development cost as they are more apt when vague and inaccurate information is to be used. Based on the existing evidences, it is proved that a few of the problems associated with previous models are addressed by soft computing techniques. But, the need for accurate cost prediction in software project management is a challenge till today. In this paper, the analytical structure of a Takagi-Sugeno Fuzzy Logic Controller with two inputs and one output for software development effort estimation with a case study on NASA 93 dataset is discussed. The analytical study is also presented with two sample inputs. The Fuzzy models are developed using triangular and GBell membership functions. The results are compared using various assessment criteria. It has been observed that the fuzzy model with triangular membership function performed better than the other models.


Fuzzy logic controller Analytical study Fuzzy rules Effort estimation Criteria for assessment 


  1. 1.
    Babuska, R.: Fuzzy Modeling for Control. Kluwer Academic Publishers, Dordrecht (1999)zbMATHGoogle Scholar
  2. 2.
    Attarzadeh, I., Ow, S.H.: Software development effort estimation based on a new fuzzy logic model. IJCTE 1(4) (2009)Google Scholar
  3. 3.
    Seth, K., Sharma, A., Seth, A.: Component selection efforts estimation—a fuzzy logic based approach. IJCSS 3(3), 210–215 (2009)Google Scholar
  4. 4.
    Saliu, M.O.: Adaptive fuzzy logic based framework for software development effort prediction. King Fahd University of Petroleum and Minerals (2003)Google Scholar
  5. 5.
    Prasad Reddy, P.V.G.D., Hari, CH.V.M.K., Jagadeesh, M.: Takagi-Sugeno fuzzy logic for software cost estimation using fuzzy operator. Int. J. Soft. Eng. 4(1) (2011)Google Scholar
  6. 6.
    Xu, Z., Khoshgoftaar, T.M.: Identification of fuzzy models of software cost estimation. Fuzzy Sets Sys. 145, 141–163 (2004)Google Scholar
  7. 7.
    Alwadi, A., et al.: A practical two input two output Takagi-Sugeno fuzzy controller. Int. J. Fuzzy Syst. 5(2), 123–130 (2003)Google Scholar
  8. 8.
    Ying, H.: An analytical study on structure, stability and design on general nonlinear Takagi-Sugeno fuzzy control systems. 34(12), 1617–1623 (1998)Google Scholar
  9. 9.
    Ryder, J.: Fuzzy modeling of software effort prediction. In: Proceeding of IEEE Information Technology Conference, Syracuse, NY, pp: 53–56 (1998)Google Scholar
  10. 10.
    Sharma, V., Verma, H.K.: Optimized fuzzy logic based framework for effort estimation in software development. Int. J. Comput. Sci. Issues 7(2), No 2, 30–38 (2010)Google Scholar
  11. 11.
    Zonglian, F., Xihui, L.: f-COCOMO: fuzzy constructive cost model in software engineering. In: Proceedings of IEEE International Conference on Fuzzy Systems, IEEE, pp. 331–337 (1992)Google Scholar
  12. 12.
    Ding, Y., Ying, H., Shao, S.: Typical Takagi-Sugeno PI and PD fuzzy controllers: analytical structures and stability analysis. Inf. Sci. 151, 245–262 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Prasad Reddy, P.V.G.D., Sudha, K.R., Rama Sree, P.: Application of fuzzy logic approach to software effort estimation. Int. J. Adv. Comput. Sci. Appl. 2(5), 87–92 (2011). ISSN: 2156-5570Google Scholar
  14. 14.
    Sandeep, K., Chopra, V.: Software development effort estimation using soft computing. Int. J. Mach. Learn. Comput. 2(5) (2012)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of CSEAditya Engineering College, JNTUKKakinadaIndia
  2. 2.Department of CSESri Sai Aditya Institute of Science & Technology, JNTUKKakinadaIndia

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