Skip to main content
SpringerLink
Log in

Applying fuzzy neural network to estimate software development effort

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

The ability to accurately and consistently estimate software development efforts is required by the project managers in planning and conducting software development activities. Since software effort drivers are vague and uncertain, software effort estimates, especially in the early stages of the development life cycle, are prone to a certain degree of estimation errors. A software effort estimation model which adopts a fuzzy inference method provides a solution to fit the uncertain and vague properties of software effort drivers. The present paper proposes a fuzzy neural network (FNN) approach for embedding artificial neural network into fuzzy inference processes in order to derive the software effort estimates. Artificial neural network is utilized to determine the significant fuzzy rules in fuzzy inference processes. We demonstrated our approach by using the 63 historical project data in the well-known COCOMO model. Empirical results showed that applying FNN for software effort estimates resulted in slightly smaller mean magnitude of relative error (MMRE) and probability of a project having a relative error of less than or equal to 0.25 (Pred(0.25)) as compared with the results obtained by just using artificial neural network and the original model. The proposed model can also provide objective fuzzy effort estimation rule sets by adopting the learning mechanism of the artificial neural network.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Boehm BW (1981) Software engineering economics. Prentice-Hall, Englewood Cliffs

    MATH  Google Scholar 

  2. Chiu NH, Huang SJ (2007) The adjusted analogy-based software effort estimation based on similarity distances. J Syst Softw 80:628–640

    Article  Google Scholar 

  3. Fei Z, Liu X (1992) f-COCOMO: fuzzy constructive cost model in software engineering. In: IEEE international conference on fuzzy systems, pp 331–337

  4. Heiat A (2002) Comparison of artificial neural network and regression models for estimating software development effort. Inf Softw Technol 44:911–922

    Article  Google Scholar 

  5. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 79(8):2554–2558

    Article  MathSciNet  Google Scholar 

  6. Huang SJ, Chiu NH (2006) Optimization of analogy weights by genetic algorithm for software effort estimation. Inf Softw Technol 48:1034–1045

    Article  Google Scholar 

  7. Huang SJ, Lin CY, Chiu NH (2006) Fuzzy decision tree approach for embedding risk assessment information into software cost estimation model. J Inf Sci Eng 22:297–313

    Google Scholar 

  8. Idri A, Kjiri L, Abran A (2000) COCOMO cost model using fuzzy logic. In: 7th international conference on fuzzy theory & technology, pp 219–225

  9. Ishibuchi H, Kwon K, Tanaka H (1995) A learning algorithm of fuzzy neural network with triangular fuzzy weights. Fuzzy Sets Syst 71(3):277–293

    Article  Google Scholar 

  10. Jeffery R, Ruhe M, Wieczorek I (2000) A comparative study of two software development cost modeling techniques using multi-organizational and company-specific data. Inf Softw Technol 42:1009–1016

    Article  Google Scholar 

  11. Jeffery R, Ruhe M, Wieczorek I (2001) Using public domain metrics to estimate software development effort. In: Seventh IEEE international software metrics symposium, pp 16–27

  12. Jorgenson M, Sjoberg DI (2004) The impact of customer expectation on software development effort estimates. Int J Proj Manag 22:317–325

    Article  Google Scholar 

  13. Jorgenson M, Indahl U, Sjoberg D (2003) Software effort estimation by analogy and regression toward the mean. J Syst Softw 68:253–262

    Article  Google Scholar 

  14. Juang CF, Lin CT (2001) Noisy speech processing by recurrently adaptive fuzzy filters. IEEE Trans Fuzzy Syst 9:139–152

    Article  Google Scholar 

  15. Kaczmarek J, Kucharski M (2004) Size and effort estimation for applications written in Java. Inf Softw Technol 46:589–601

    Article  Google Scholar 

  16. Kaikhah K, Doddameti S (2006) Discovering trends in large datasets using neural networks. Appl Intell 24(10):51–60

    Article  Google Scholar 

  17. Kemerer CF (1987) An empirical validation of software cost estimation models. Commun. ACM 30(5):416–429

    Article  Google Scholar 

  18. Kuo RJ, Ho LM, Hu CM (2002) Integration of self-organizing feature map and K-means algorithm for marketing segmentation. J Comput Oper Res 29(11):1475–1493

    Article  MATH  Google Scholar 

  19. Park JH, Im KH, Shin CK, Park SC (2004) MBNR: case-based reasoning with local feature weighting by neural network. Appl Intell 21(3):265–276

    Article  MATH  Google Scholar 

  20. Pedrycz W, Sosnowski ZA (2001) The design of decision trees in the framework of granular data and their application to software quality models. Fuzzy Sets Syst 123(4):271–290

    Article  MATH  MathSciNet  Google Scholar 

  21. Rasmani KA, Shen Q (2006) Data-driven fuzzy rule generation and its application for student academic performance evaluation. Appl Intell 25(3):305–319

    Article  Google Scholar 

  22. Roger JS (1993) ANFIS: adaptive network based fuzzy inference systems. IEEE Trans Syst Man Cybern 23(3):665–685

    Article  Google Scholar 

  23. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Parallel distributed processing: explorations in the microstructures of cognition. MIT Press, Cambridge, pp 318–362

    Google Scholar 

  24. Srinivasan K, Douglas F (1995) Machine learning approaches to estimating software development effort. IEEE Trans Softw Eng 21(2):126–137

    Article  Google Scholar 

  25. Xua Z, Khoshgoftaar TM (2004) Identification of fuzzy models of software cost estimation. Fuzzy Sets Syst 145(1):141–163

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Management, National Taiwan University of Science and Technology, 43, Sec. 4, Keelung Rd., Taipei, 106, Taiwan

    Sun-Jen Huang

  2. Department of Information Management, Ching Yun University, 229, Chien-Hsin Rd., Jung-Li, Taoyuan, Taiwan

    Nan-Hsing Chiu

Authors
  1. Sun-Jen Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nan-Hsing Chiu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sun-Jen Huang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Huang, SJ., Chiu, NH. Applying fuzzy neural network to estimate software development effort. Appl Intell 30, 73–83 (2009). https://doi.org/10.1007/s10489-007-0097-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-007-0097-4

Keywords

Search

Navigation