Neuro-fuzzy approach to calibrate function points
FS'07 Proceedings of the 8th Conference on 8th WSEAS International Conference on Fuzzy Systems - Volume 8
A new calibration for Function Point complexity weights
Information and Software Technology
A neuro-fuzzy model for function point calibration
WSEAS Transactions on Information Science and Applications
Updating weight values for function point counting
International Journal of Hybrid Intelligent Systems
Comparison of weighted grey relational analysis for software effort estimation
Software Quality Control
A Comparison of Metric-Based and Empirical Approaches for Cognitive Analysis of Modeling Languages
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (I)
| Hi-index | 0.01 |
Function Point Analysis (FPA) is a largely used technique to estimate the size of development project, enhancement project or applications already installed. During the point counting process that represents the dimension of a project or an application, each function is classified according to its relative functional complexity. Several studies resulted in FPA extensions, and most of them are mainly aimed at achieving greater precision in the point assessment of systems of greater algorithmic complexity. This work proposes the use of concepts and properties from fuzzy set theory to extend FPA into FFPA (Fuzzy Function Point Analysis). Fuzzy theory seeks to build a formal quantitative structure capable of emulating the imprecision of human knowledge. With the function points generated by FFPA, the functionality of the project is better represented than it was through FPA. Thus, derived values such as costs and terms of development can be more precisely determined.