Software Effort Estimation Using Soft Computing Techniques
Various models have been derived by studying large number of completed software projects from various organizations and applications to explore how project sizes mapped into project effort. But, still there is a need to prediction accuracy of the models. As Neuro-fuzzy based system is able to approximate the non-linear function with more precision. So, Neuro-Fuzzy system is used as a soft computing approach to generate model by formulating the relationship based on its training. In this paper, Neuro-Fuzzy technique is used for software estimation modeling of on NASA software project data and performance of the developed models are compared with the Halstead, Walston-Felix, Bailey-Basili and Doty Models mentioned in the literature.
[1] A. Abraham, Adaptation of Fuzzy Inference System Using Neural
Learning, Springer Berlin, ISSN: 1434-9922 (Print) 1860-0808 (Online),
vol. 181, 2005.
[2] A. Abraham and M.R. Khan, Neuro-Fuzzy Paradigms for Intelligent
Energy Management, Innovations in Intelligent Systems: Design,
Management and Applications, Studies in Fuzziness and Soft
Computing, Springer Verlag Germany, Chapter 12, pp. 285-314, 2003.
[3] B. W. Boehm, Software engineering economics, Englewood Cliffs, NJ:
Prentice-Hall, 1981.
[4] C. E. Walston, C. P. Felix, A method of programming measurement and
estimation, IBM Systems Journal, vol. 16, no. 1, pp. 54-73, 1977.
[5] G.N. Parkinson, Parkinson's Law and Other Studies in Administration,
Houghton-Miffin, Boston, 1957.
[6] L. H. Putnam, A general empirical solution to the macro software sizing
and estimating problem, IEEE Trans. Soft. Eng., pp. 345-361, July 1978.
[7] J. R. Herd, J.N. Postak, W.E. Russell, K.R. Steward, Software cost
estimation study: ´ÇáStudy results, Final Technical Report, RADC-TR77-
220, vol. I, Doty Associates, Inc., Rockville, MD, pp. 1-10, 1977.
[8] J. W. Bailey and V. R. Basili, "A meta model for software development
resource expenditure," in Proceedings of the International Conference on
Software Engineering, pp. 107-115, 1981.
[9] R. E. Park, PRICE S: The calculation within and why, Proceedings of
ISPA Tenth Annual Conference, Brighton, England, pp. 231-240, July
1988.
[10] R. Jang, Neuro-Fuzzy Modeling: Architectures, Analyses and
Applications, Ph.D. Thesis, University of California, Berkeley, 1992.
[11] R.K.D. Black, R. P. Curnow, R. Katz, M. D. Gray, BCS Software
Production Data, Final Technical Report, RADC-TR-77-116, Boeing
Computer Services, Inc., March, pp. 5-8, 1977.
[12] R. Tausworthe, Deep Space Network Software Cost Estimation Model,
Jet Propulsion Laboratory Publication 81-7, pp. 67-78, 1981.
[13] W. S. Donelson, Project Planning and Control, Datamation, pp. 73-80,
June 1976.
[1] A. Abraham, Adaptation of Fuzzy Inference System Using Neural
Learning, Springer Berlin, ISSN: 1434-9922 (Print) 1860-0808 (Online),
vol. 181, 2005.
[2] A. Abraham and M.R. Khan, Neuro-Fuzzy Paradigms for Intelligent
Energy Management, Innovations in Intelligent Systems: Design,
Management and Applications, Studies in Fuzziness and Soft
Computing, Springer Verlag Germany, Chapter 12, pp. 285-314, 2003.
[3] B. W. Boehm, Software engineering economics, Englewood Cliffs, NJ:
Prentice-Hall, 1981.
[4] C. E. Walston, C. P. Felix, A method of programming measurement and
estimation, IBM Systems Journal, vol. 16, no. 1, pp. 54-73, 1977.
[5] G.N. Parkinson, Parkinson's Law and Other Studies in Administration,
Houghton-Miffin, Boston, 1957.
[6] L. H. Putnam, A general empirical solution to the macro software sizing
and estimating problem, IEEE Trans. Soft. Eng., pp. 345-361, July 1978.
[7] J. R. Herd, J.N. Postak, W.E. Russell, K.R. Steward, Software cost
estimation study: ´ÇáStudy results, Final Technical Report, RADC-TR77-
220, vol. I, Doty Associates, Inc., Rockville, MD, pp. 1-10, 1977.
[8] J. W. Bailey and V. R. Basili, "A meta model for software development
resource expenditure," in Proceedings of the International Conference on
Software Engineering, pp. 107-115, 1981.
[9] R. E. Park, PRICE S: The calculation within and why, Proceedings of
ISPA Tenth Annual Conference, Brighton, England, pp. 231-240, July
1988.
[10] R. Jang, Neuro-Fuzzy Modeling: Architectures, Analyses and
Applications, Ph.D. Thesis, University of California, Berkeley, 1992.
[11] R.K.D. Black, R. P. Curnow, R. Katz, M. D. Gray, BCS Software
Production Data, Final Technical Report, RADC-TR-77-116, Boeing
Computer Services, Inc., March, pp. 5-8, 1977.
[12] R. Tausworthe, Deep Space Network Software Cost Estimation Model,
Jet Propulsion Laboratory Publication 81-7, pp. 67-78, 1981.
[13] W. S. Donelson, Project Planning and Control, Datamation, pp. 73-80,
June 1976.
@article{"International Journal of Medical, Medicine and Health Sciences:60758", author = "Parvinder S. Sandhu and Porush Bassi and Amanpreet Singh Brar", title = "Software Effort Estimation Using Soft Computing Techniques", abstract = "Various models have been derived by studying large number of completed software projects from various organizations and applications to explore how project sizes mapped into project effort. But, still there is a need to prediction accuracy of the models. As Neuro-fuzzy based system is able to approximate the non-linear function with more precision. So, Neuro-Fuzzy system is used as a soft computing approach to generate model by formulating the relationship based on its training. In this paper, Neuro-Fuzzy technique is used for software estimation modeling of on NASA software project data and performance of the developed models are compared with the Halstead, Walston-Felix, Bailey-Basili and Doty Models mentioned in the literature.
", keywords = "Effort Estimation, Neural-Fuzzy Model, Halstead Model, Walston-Felix Model, Bailey-Basili Model, Doty Model.", volume = "2", number = "10", pages = "361-4", }
