A Model for Estimation of Efforts in Development of Software Systems
Software effort estimation is the process of predicting
the most realistic use of effort required to develop or maintain
software based on incomplete, uncertain and/or noisy input. Effort
estimates may be used as input to project plans, iteration plans,
budgets. There are various models like Halstead, Walston-Felix,
Bailey-Basili, Doty and GA Based models which have already used
to estimate the software effort for projects. In this study Statistical
Models, Fuzzy-GA and Neuro-Fuzzy (NF) Inference Systems are
experimented to estimate the software effort for projects. The
performances of the developed models were tested on NASA
software project datasets and results are compared with the Halstead,
Walston-Felix, Bailey-Basili, Doty and Genetic Algorithm Based
models mentioned in the literature. The result shows that the NF
Model has the lowest MMRE and RMSE values. The NF Model
shows the best results as compared with the Fuzzy-GA based hybrid
Inference System and other existing Models that are being used for
the Effort Prediction with lowest MMRE and RMSE values.
[1] Alaa F. Sheta, "Estimation of the COCOMO Model Parameters Using
Genetic Algorithms for NASA Software Projects", Journal of Computer
Science 2 (2): 118-123, 2006.
[2] B. W. Boehm, Software engineering economics, Englewood Cliffs, NJ:
Prentice-Hall, 1981, pp. 50-100.
[3] 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.
[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. Cantone, A. Cimitile, U. De Carlini, A comparison of models for
software cost estimation and management of software projects,
Computer Systems: Performance and Simulation, Elisevier Science
Publishers.
[6] G.N. Parkinson, Parkinson's Law and Other Studies in Administration,
Houghton-Miffin, Boston, 1957.
[7] L. H. Putnam, A general empirical solution to the macro software sizing
and estimating problem, IEEE Trans. Soft. Eng., pp. 345-361, July 1978.
[8] 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.
[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] N. A. Parr, An alternative to the Raleigh Curve Model for Software
development effort, IEEE on Software Eng., pp. 77-85, May 1980.
[11] R. Jang, Neuro-Fuzzy Modeling: Architectures, Analyses and
Applications, Ph.D. Thesis, University of California, Berkeley, 1992.
[12] 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.
[13] R. Nelson, Management Hand Book for the Estimation of Computer
Programming Costs, AD- A648750, Systems Development Corp., pp.
20-34, 1966.
[14] R. Tausworthe, Deep Space Network Software Cost Estimation Model,
Jet Propulsion Laboratory Publication 81-7, pp. 67-78, 1981.
[15] W. S. Donelson, Project Planning and Control, Datamation, pp. 73-80,
June 1976.
[1] Alaa F. Sheta, "Estimation of the COCOMO Model Parameters Using
Genetic Algorithms for NASA Software Projects", Journal of Computer
Science 2 (2): 118-123, 2006.
[2] B. W. Boehm, Software engineering economics, Englewood Cliffs, NJ:
Prentice-Hall, 1981, pp. 50-100.
[3] 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.
[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. Cantone, A. Cimitile, U. De Carlini, A comparison of models for
software cost estimation and management of software projects,
Computer Systems: Performance and Simulation, Elisevier Science
Publishers.
[6] G.N. Parkinson, Parkinson's Law and Other Studies in Administration,
Houghton-Miffin, Boston, 1957.
[7] L. H. Putnam, A general empirical solution to the macro software sizing
and estimating problem, IEEE Trans. Soft. Eng., pp. 345-361, July 1978.
[8] 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.
[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] N. A. Parr, An alternative to the Raleigh Curve Model for Software
development effort, IEEE on Software Eng., pp. 77-85, May 1980.
[11] R. Jang, Neuro-Fuzzy Modeling: Architectures, Analyses and
Applications, Ph.D. Thesis, University of California, Berkeley, 1992.
[12] 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.
[13] R. Nelson, Management Hand Book for the Estimation of Computer
Programming Costs, AD- A648750, Systems Development Corp., pp.
20-34, 1966.
[14] R. Tausworthe, Deep Space Network Software Cost Estimation Model,
Jet Propulsion Laboratory Publication 81-7, pp. 67-78, 1981.
[15] W. S. Donelson, Project Planning and Control, Datamation, pp. 73-80,
June 1976.
@article{"International Journal of Information, Control and Computer Sciences:56384", author = "Parvinder S. Sandhu and Manisha Prashar and Pourush Bassi and Atul Bisht", title = "A Model for Estimation of Efforts in Development of Software Systems", abstract = "Software effort estimation is the process of predicting
the most realistic use of effort required to develop or maintain
software based on incomplete, uncertain and/or noisy input. Effort
estimates may be used as input to project plans, iteration plans,
budgets. There are various models like Halstead, Walston-Felix,
Bailey-Basili, Doty and GA Based models which have already used
to estimate the software effort for projects. In this study Statistical
Models, Fuzzy-GA and Neuro-Fuzzy (NF) Inference Systems are
experimented to estimate the software effort for projects. The
performances of the developed models were tested on NASA
software project datasets and results are compared with the Halstead,
Walston-Felix, Bailey-Basili, Doty and Genetic Algorithm Based
models mentioned in the literature. The result shows that the NF
Model has the lowest MMRE and RMSE values. The NF Model
shows the best results as compared with the Fuzzy-GA based hybrid
Inference System and other existing Models that are being used for
the Effort Prediction with lowest MMRE and RMSE values.", keywords = "Neuro-Fuzzy Model, Halstead Model, Walston-Felix
Model, Bailey-Basili Model, Doty Model, GA Based Model, Genetic
Algorithm.", volume = "3", number = "8", pages = "2014-5", }