An empirical validation of software cost estimation models
Communications of the ACM
Computer
Machine Learning Approaches to Estimating Software Development Effort
IEEE Transactions on Software Engineering
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Software Engineering Economics
Software Engineering Economics
Software Cost Estimation with Cocomo II with Cdrom
Software Cost Estimation with Cocomo II with Cdrom
Splitting the Difference: The Historical Necessity of Synthesis in Software Engineering
IEEE Annals of the History of Computing
Function Point Analysis: Difficulties and Improvements
IEEE Transactions on Software Engineering
Software Development Cost Estimation Using Function Points
IEEE Transactions on Software Engineering
Fuzzy Modeling for Function Points Analysis
Software Quality Control
Building A Software Cost Estimation Model Based On Categorical Data
METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
Modification of standard function point complexity weights system
Journal of Systems and Software - Special issue: The new context for software engineering education and training
A soft computing framework for software effort estimation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Improving the COCOMO model using a neuro-fuzzy approach
Applied Soft Computing
Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis
IEEE Transactions on Computers
IEEE Transactions on Software Engineering
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While software development productivity has grown rapidly, the weight values assigned to count standard Function Point (FP) created at IBM twenty-five years ago have never been updated. This obsolescence raises critical questions about the validity of the weight values; it also creates other problems such as ambiguous classification, crisp boundary, as well as subjective and locally defined weight values. All of these challenges reveal the need to calibrate FP in order to reflect both the specific software application context and the trend of today's software development techniques more accurately. We have created a FP calibration model that incorporates the learning ability of neural networks as well as the capability of capturing human knowledge using fuzzy logic. The empirical validation using ISBSG Data Repository (release 8) shows an average improvement of 22% in the accuracy of software effort estimations with the new calibration.