A Neuro-Fuzzy Model for Function Point Calibration
This work addresses software industry needs for more accurate cost estimation, but it is incremental as it applies an existing hybrid method to a specific calibration task.
The authors tackled the problem of calibrating Function Point complexity weights for software cost estimation by applying a Neuro-Fuzzy model, resulting in a 22% improvement in effort estimation accuracy as validated on the ISBSG dataset.
The need to update the calibration of Function Point (FP) complexity weights is discussed, whose aims are to fit specific software application, to reflect software industry trend, and to improve cost estimation. Neuro-Fuzzy is a technique that incorporates the learning ability from neural network and the ability to capture human knowledge from fuzzy logic. The empirical validation using ISBSG data repository Release 8 shows a 22% improvement in software effort estimation after calibration using Neuro-Fuzzy technique.