Measurement of the Functional Size of Web Analytics Implementation: A COSMIC-Based Case Study Using Machine Learning

Ammar Abdallah (), Alain Abran, Munthir Qasaimeh, Malik Qasaimeh and Bashar Abdallah
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Ammar Abdallah: King Talal School of Business Technology, Princess Sumaya University for Technology, Al-Jubeiha, P.O. Box 1438, Amman 11941, Jordan
Alain Abran: Department of Software Engineering and Information Technology, École de Technologie Supérieure (ÉTS), Montréal, QC H3C 1K3, Canada
Munthir Qasaimeh: Department of Computer Information Systems, Jordan University of Science and Technology, Irbid 22110, Jordan
Malik Qasaimeh: Department of Cyber Security, Jordan University of Science and Technology, Irbid 22110, Jordan
Bashar Abdallah: Department of Artificial Intelligence, Polytechnique Montréal, Université de Montréal, Montréal, QC H3T 0A3, Canada

Future Internet, 2025, vol. 17, issue 7, 1-31

Abstract: To fully leverage Google Analytics and derive actionable insights, web analytics practitioners must go beyond standard implementation and customize the setup for specific functional requirements, which involves additional web development efforts. Previous studies have not provided solutions for estimating web analytics development efforts, and practitioners must rely on ad hoc practices for time and budget estimation. This study presents a COSMIC-based measurement framework to measure the functional size of Google Analytics implementations, including two examples. Next, a set of 50 web analytics projects were sized in COSMIC Function Points and used as inputs to various machine learning (ML) effort estimation models. A comparison of predicted effort values with actual values indicated that Linear Regression, Extra Trees, and Random Forest ML models performed well in terms of low Root Mean Square Error (RMSE), high Testing Accuracy, and strong Standard Accuracy (SA) scores. These results demonstrate the feasibility of applying functional size for web analytics and its usefulness in predicting web analytics project efforts. This study contributes to enhancing rigor in web analytics project management, thereby enabling more effective resource planning and allocation.

Keywords: web analytics; common software measurement international consortium (COSMIC); machine learning; google analytics; google tag manager; software effort estimation (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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