Fuzzy Classical Analysis for the Cost Effective Software Evolution Effort Appreciation

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-37 Number-3
Year of Publication : 2016
Authors : Mr. Lakshmana Rao Padala, Dr. E.Mohan
  10.14445/22312803/IJCTT-V37P122

MLA

Mr. Lakshmana Rao Padala, Dr. E.Mohan "Fuzzy Classical Analysis for the Cost Effective Software Evolution Effort Appreciation". International Journal of Computer Trends and Technology (IJCTT) V37(3):117-122, July 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Software Cost Estimation is the most significant and demanding activity in Software development effort prediction. Software effort estimation is unpredictable in nature as it hugely depends upon some variables that are not known at the initial phase of development. Fuzzy analysis plays a main role to analyze and predict the Software cost estimation. Fuzzification is the one of the key attribute which includes the size of the project, incorporates expert’s knowledge in a well-defined manner, allows total transparency in the prediction system by prediction of results through rules or other means, adaptability towards continually changing development technologies and environments[10]. Properly addressing all these issues would position soft computing based prediction techniques as models of choice for effort prediction, considering the promising features already present in them are made. Cost Effective Software Evolution Exertion Appreciation is one of the most challenging tasks in software sector. Our most intension in this paper is to present the Fuzzy Analysis for the Cost Effective Software Evolution Exertion Appreciation [12].

References
[1] J.-M. Desharnais. Analyse statistique de la productivitie des projects de development en informatique apartir de la technique des points des fontion. Master’s Thesis, Universite du Montreal,1989.
[2] A.J. Albrecht. Measuring application development productivity. In Proceedings of the IBM Applications Development Joint SHARE/GUIDE Symposium, Monterey, CA, 83-92, 1979.
[3] J.E. Matson, B.E. Barrett, and J.M. Mellichamp. Software development cost estimation using function points. IEEE Transactions on Software Engineering, 20(4):275-287, 1994.
[4] A.L. Lederer, R. Mirani, B.S. Neo, C. Pollard, J. Prasad, and K. Ramamurthy. Information system cost estimating: a management perspective. MIS Quarterly,159-176, June, 1990.
[5] B. Londeix. Deploying realistic estimation (field situation analysis). Information and Software Technology, 37:665-670, 1995.
[6] S. Kumar, B.A. Krishna, and P.S. Satsangi. Fuzzy systems and neural networks in software engineering project management. Journal of Applied Intelligence,4:31-52, 1994.
[7] K. Srinivasan and D. Fisher. Machine learning approaches to estimating software development effort, IEEE Transactions on Software Engineering, 21:126-137, 1995.
[8] T. Mukhopadhyay, S.S. Vicinanza, and M.J. Prietula. Examining the feasibility of a case-based reasoning model for software effort estimation. MIS Quarterly, 16:155-171, 1992.
[9] A.R. Gray, and S.G. MacDonell. A comparison of model building techniques to develop predictive equations for software metrics. Information and Software Technology, to appear, 1997.
[10] Andrew R. Gray and Stephen G. Mac Donell, Applications of Fuzzy Logic to Software Metric Models for Development Effort Estimation The Information Science, Discussion Paper Series Number 97/10 July 1997 ISSN 1177-455X
[11] S.G. MacDonell and A.R. Gray. Alternatives to regression models for estimating software projects. In Proceedings of the IFPUG Fall Conference, Dallas TX, IFPUG 279.1-279.15, 1996.
[12] N. Fenton. Software Metrics, a Rigorous Approach. Chapman & Hall, London, 1991.
[13] T. Mukhopadhyay and S. Kekre. Software effort models for early estimation of process control applications. IEEE Transactions on Software Engineering, 18(10):915-924, 1992.
[14] J.J. Dolado. A study of the relationships among Albrecht and Mark II function points, lines of code 4GL and effort. Journal of Systems and Software, 37:161-173, 1997.
[15] R.I. Kilgour, A.R. Gray, P.J. Sallis, and S.G. MacDonell. A fuzzy logic approach to computer software source code authorship analysis. Submitted to The Fourth International Conference on Neural Information Processing -- The Annual Conference of the Asian Pacific Neural Network Assembly (ICONIP'97).
[16] Y.I. Liou. Knowledge acquisition: issues, techniques and methodology. DATABASE, 59-64, Winter 1992.

Keywords
Fuzzy Analysis, Fuzzification, Cost Effective, Software Evolution, Exertion.