The KIPS Transactions:PartD (정보처리학회논문지D)

Korea Information Processing Society (한국정보처리학회)
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Improving Estimative Capability of Software Development Effort using Radial Basis Function Network

RBF 망 이용 소프트웨어 개발 노력 추정 성능향상

  • 이상운 (국방품질관리소 항공전자장비 및 소프트웨어 품질보증) ;
  • 박영목 (경상대학교 대학원 컴퓨터과학과) ;
  • 박재홍
  • Published : 2001.10.01
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Abstract

An increasingly important facet of software development is the ability to estimated the associated coast and effort of development early in the development life cycle. In spite of the most generally sued procedures for estimation of the software development effort and cost were linear regression analysis. As a result of the software complexity and various development environments, the software effort and cost estimates that are grossly inaccurate. The application of nonlinear methods hold the greatest promise for achieving this objects. Therefore this paper presents an RBF (radial basis function) network model that is able to represent the nonlinear relation for software development effort, The research describes appropriate RBF network modeling in the context of a case study for 24 software development projects. Also, this paper compared the RBF network model with a regression analysis model. The RBF network model is the most accuracy of all.

소프트웨어 개발에서 점점 더 중요시되는 사항은 개발 생명주기의 초기에 개발과 관련된 노력과 비용을 추정하는 능력이다. 소프트웨어 개발노력과 비용추정을 위한 대부분의 모델이 선형회귀분석 절차를 사용하였다. 그러나 소프트웨어의 복잡성, 개발환경의 다양성으로 인해 소프트웨어 개발노력과 비용 추정은 점점 더 부정확해지고 있다. 이 목적을 달성하기 위해서는 비선형 방법을 사용해야 한다. 따라서 본 논문은 소프트웨어 개발 노력을 추정하는데 비선형 관계를 표현 가능한 RBF망 모델을 제안한다. 24개 소프트웨어 사례연구를 통해 적합한 RBF 망 모델을 제시하였다. 또한, 회귀분석 모델과 RBF망 모델을 비교하여 RBF 망 모델의 정확성이 가장 좋음을 보였다.

Keywords

References

  1. L. H. Morin, 'Estimation of Resources for Computer Programming Projects,' M.S. Thesis, Univ. North Carolina, Chapel Hill, NC, 1973
  2. L. H. Putnam, 'A General Empirical Solution to the Macro Software Sizing and Estimating Problem,' IEEE Trans. on Software Eng., Vol.SE-4, No.4, pp.345-361, 1978 https://doi.org/10.1109/TSE.1978.231521
  3. 이상운, '신경망을 이용한 소프트웨어 개발노력 추정', 한국정보처리학회논문지(D), 제8권 제3호, pp.241-246, 2001
  4. G. Cybenko, 'Approximation by Super-positions of A Sigmoidal Function,' Mathematics of Control, Signals and Systems, Vol.2, pp.303-314, 1989 https://doi.org/10.1007/BF02551274
  5. A. R. Barron, 'Neural Net Approximation,' In Proceedings of the Seventh Yale Workshop on Adaptive and Learning Systems. New Haven, CT. Yale University, pp.69-72. 1992
  6. H. Demuth and M. Beale, 'Matlab Neural Network Toolbox, User's Guide Version 3.0,' Math Works, Inc. 1997
  7. B. W. Boehm, 'Software Engineering Economics,' Prentice Hall, 1981
  8. B. W. Boehm, 'Software Engineering Economics,' IEEE Trans. on Software Eng., Vol.10, No.1, pp.7-19, 1984
  9. A. J. Albrecht, 'Measuring Applications Development Productivity,' Proceedings of IBM Application Dev., Joint SHARE/GUIDE Symposium, Monterey, CA, pp.83-92, 1979
  10. A. J. Albrecht and J. E. Gaffney, 'Software Function, Source Line of Code and Development Effort Prediction : A Software Science Validation,' IEEE Trans. on Software Eng., Vol.SE-9, No.6, pp.639-648, 1983 https://doi.org/10.1109/TSE.1983.235271
  11. J. E. Matson, B. E. Barrett, and J. M. Mellichamp, 'Software Development Cost Estimation Using Function Points,' IEEE Trans. on Software Eng., Vol.20, No.4, pp.275-287 https://doi.org/10.1109/32.277575
  12. C. F. Kemerer, 'Reliability of Function Point Measurement-A Field Experiment,' Communications of ACM, Feb. 1993 https://doi.org/10.1145/151220.151230
  13. S. Chen, C. F. N. Cowan, and P. M. Grant, 'Orthogonal Least Square Learning for Radial Basis Function Networks,' IEEE Trans. on Neural Networks, Vol.2, No.2, pp. 302- 309, 1991 https://doi.org/10.1109/72.80341
  14. D. S. Broomhead and D. Lowe, 'Multivariate Functional Interpolation and Adaptive Networks,' Complex Systems, Vol.2, pp.321-355, 1988
  15. ISBSG, 'Worldwide Software Development - The Benchmark Release 6,' Victoria, Australia International Software Benchmarking Standards Group, 2000