Optimizing design parameters of fuzzy model based COCOMO using genetic algorithms

  • Sonia ChhabraEmail author
  • Harvir Singh
Original Research


Software development process is a series of planned activities undertaken to design a software product. The major concern in this process is estimation of cost and effort. Algorithmic as well as non algorithmic techniques are used to estimate cost and effort. Algorithmic techniques use mathematical equations; however, in case of imprecise information these techniques are overpowered by non algorithmic techniques. Intermediate COCOMO suffers from a problem of imprecise definition of cost drivers resulting in inaccurate estimations. Thus in the current research, implementation of non algorithmic modelling is carried out using soft computing techniques like fuzzy logic and genetic algorithms. The fuzzy approach is implemented to design a fuzzy model for each cost driver. The fuzzy model handles imprecise and ambiguous definition of input ranges of cost drivers. Selection of parameters characterising fuzzy sets in proposed fuzzy model is further optimized using genetic algorithms. The proposed model is tested on COCOMO NASA dataset and COCOMO NASA2 dataset using MATLAB. The improvement in performance of proposed optimized model is measured in terms of mean magnitude of relative error (MMRE) and Pred (25%). A significant improvement in %MMRE and Pred (25%) justifies the suitability of genetic algorithms for optimizing proposed fuzzy model.


Fuzzy logic Genetic algorithms Genetic tuning Software cost estimation Soft computing techniques 



Ant colony optimization


Constructive cost model


Data base


Effort adjustment factor


Effort multiplier


Fuzzy inference system


Genetic algorithms


Knowledge base


Rule base


Membership function


Mean magnitude of relative error


Magnitude of relative error




  1. 1.
    Leung H, Fan Z (2002) Software cost estimation. Handbook of software engineering, Hong Kong Polytechnic University, pp 1–14.
  2. 2.
    Saliu MO, Ahmed M (2004) Soft computing based effort prediction systems—a survey. In: Damiani E, Jain LC (eds) Computational intelligence in software engineering. Springer, BerlinGoogle Scholar
  3. 3.
    Huang X et al (2006) A soft computing framework for software effort estimation. Soft Comput 10(2):170–177CrossRefGoogle Scholar
  4. 4.
    Herrera F (2008) Genetic fuzzy systems: taxonomy, current research trends and prospects. Evol Intell 1(1):27–46CrossRefGoogle Scholar
  5. 5.
    Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison Wesley, New YorkzbMATHGoogle Scholar
  6. 6.
    Boehm BW (1981) Software engineering economics. Prentice Hall, Eagle Wood CliffszbMATHGoogle Scholar
  7. 7.
    George JK, Yuan B (1995) Fuzzy sets and fuzzy logic: theory and applications. Prentice-Hall, Upper Saddle RiverzbMATHGoogle Scholar
  8. 8.
    Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353CrossRefzbMATHGoogle Scholar
  9. 9.
    Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with fuzzy logic controller. Int J Man Mach Stud 7(1):1–13CrossRefzbMATHGoogle Scholar
  10. 10.
    Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modelling and control. IEEE Trans Syst Man Cybern 15:116–132CrossRefzbMATHGoogle Scholar
  11. 11.
    Man KF, Tang KS, Kwong S (2001) Genetic algorithms: concepts and designs. Springer, New YorkzbMATHGoogle Scholar
  12. 12.
    Zadeh LA (1994) Fuzzy logic, neural network and soft computing. Commun ACM 7(3):77–84CrossRefGoogle Scholar
  13. 13.
    Fei Z, Liu X (1992) f-COCOMO-fuzzy constructive cost model in software engineering. In: Proceedings of IEEE international conference on fuzzy system, pp 331–337Google Scholar
  14. 14.
    Idri A, Abran AL (2000) COCOMO cost model using fuzzy logic. In: Proceedings of the 7th international conference on fuzzy theory and technology, pp 1–4Google Scholar
  15. 15.
    Idri A, Abran A, Khoshgoftaar TM (2003) Computational intelligence in empirical software engineering. In: Proceedings of the first USA-Morocco workshop on information technology, pp 1–9Google Scholar
  16. 16.
    Xu Z, Khoshgoftaar TM (2004) Identification of fuzzy models of software cost estimation. Fuzzy Sets Syst 145:141–163MathSciNetCrossRefGoogle Scholar
  17. 17.
    Diaz NG, Martin CL, Chavoya A (2013) A comparative study of two fuzzy logic models for software development effort estimation. Procedia Technol 7:305–314CrossRefGoogle Scholar
  18. 18.
    Sadiq M, Mariyam F, Ali A, Khan S, Tripathi P (2011) Prediction of software project effort using fuzzy logic. In: Proceedings of the 3rd international conference on electronics computer technology, pp 353–358Google Scholar
  19. 19.
    Muzaffar Z, Ahmed MA (2010) Software development effort prediction: a study on the factors impacting the accuracy of fuzzy logic systems. Inf Softw Technol 52:92–109CrossRefGoogle Scholar
  20. 20.
    Malik A, Pandey V, Kaushik A (2013) An analysis of fuzzy approaches for COCOMO II. Int J Intell Syst Appl 5(5):68–75Google Scholar
  21. 21.
    Kondratenko YP, Simon D (2016) Structural and parametric optimization of fuzzy control and decision making systems. In: Proceedings of the 6th world conference soft computing, pp 0–5Google Scholar
  22. 22.
    Kumar G, Bhatia PK (2016) Empirical assessment and optimization of software cost estimation using soft computing techniques. In: Advanced computing and communication techniques, pp 117–130Google Scholar
  23. 23.
    Huang X, Ho D, Ren J, Capretz LF (2007) Improving the COCOMO model using a neuro-fuzzy approach. Appl Soft Comput J 7(1):29–40CrossRefGoogle Scholar
  24. 24.
    Zaidi SA, Katiyar V, Abbas SQ (2017) Development of a framework for software cost estimation: design phase. Int J Tech Res Appl 5(2):68–72Google Scholar
  25. 25.
    Zhang B, Wu Y, Lu I, Du K (2011) Evolutionary computation and its applications in neural and fuzzy systems. Appl Comput Intell Soft Comput article id 938240Google Scholar
  26. 26.
    Reena, Bhatia PK (2017) Application of genetic algorithm in software engineering: a review. Int Refereed J Eng Sci 6(2):63–69Google Scholar
  27. 27.
    Chhabra S, Singh H (2016) Simulink based fuzzified COCOMO. In: 2nd International conference on contemporary computing and informatics, pp 847–85Google Scholar
  28. 28.
    Herrera F, Magdalena L (1997) Genetic fuzzy systems: a tutorial. In: Mesiar R, Riecan B (eds) Fuzzy structures, current trends. Lecture notes of the tutorial: genetic fuzzy systems. Seventh IFSA world congress (IFSA97), vol 13. Tatra Mountains Mathematical Publications, Prage, pp 93–121Google Scholar
  29. 29.
    Cordón O, Herrera F, Hoffmann F, Magdalena L (2014) Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases. In: Advances in fuzzy systems-applications and theory, vol 19Google Scholar
  30. 30.
    Guo C, Yang X (2011) A programming of genetic algorithm in matlab 7.0. Mod Appl Sci 5(1):230–235CrossRefGoogle Scholar
  31. 31.
  32. 32.

Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2019

Authors and Affiliations

  1. 1.Uttrakhand Technical UniversityDehradunIndia
  2. 2.IIMT UniversityMeerutIndia

Personalised recommendations