  Many cost estimation models have been proposed over the last three decades.
In this study, we investigate fuzzy ID3 decision tree as a method for software
effort estimation. Fuzzy ID software effort estimation model is designed by
incorporating the principles of ID3 decision tree and the concepts of the fuzzy
settheoretic; permitting the model to handle uncertain and imprecise data when
presenting the software projects. MMRE (Mean Magnitude of Relative Error) and
Pred(l) (Prediction at level l) are used, as measures of prediction accuracy,
for this study. A series of experiments is reported using ISBSG software
projects dataset. Fuzzy trees are grown using different fuzziness control
thresholds. Results showed that optimizing the fuzzy ID3 parameters can improve
greatly the accuracy of the generated software cost estimate.
