Updating Weight Values for Function Point Counting

TL;DR

This paper introduces a neural network and fuzzy logic-based model to update Function Point weights, improving effort estimation accuracy by 22% over traditional static values.

Contribution

It presents a novel FP calibration model combining neural networks and fuzzy logic to adapt weight values to modern software development contexts.

Findings

22% improvement in effort estimation accuracy

Effective calibration of FP weights using ISBSG data

Addresses obsolescence and ambiguity in traditional FP weights

Abstract

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.