Updating Weight Values for Function Point Counting
This addresses the problem of inaccurate software effort estimation for developers and project managers, but it is incremental as it builds on existing Function Point methods.
The paper tackled the obsolescence of weight values in Function Point counting, which has not been updated for 25 years, by creating a calibration model using neural networks and fuzzy logic, resulting in a 22% average improvement in software effort estimation accuracy.
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 todays 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.