Quantitatively Analysing Parametric Models. (September 2014)

• Record Type: Journal Article
• Authors: Davis, Daniel
• Is Part Of: International journal of architectural computing. Volume 12:Number 3(2014)
• Page Start: 307
• Page End: 319
• Publication Date: 2014-09
• Subjects: Architecture -- Data processing -- Periodicals; Architecture -- Informatique -- Périodiques; Virtual reality in architecture -- Periodicals; Computer-aided design -- Periodicals; Architecture -- Data processing; Periodicals; 720.2840285536
• DOI: 10.1260/1478-0771.12.3.307
• Languages: English
• ISSNs: 1478-0771

Abstract

Architectural practices regularly work with parametric models, yet almost nothing is known about the general properties of these models. We do not know how large a typical model is, or how complicated, or even what the typical parametric model does. These knowledge gaps are the focus of this article, which documents the first large-scale quantitative parametric model survey. In this paper three key quantitative metrics – dimensionality, size, and cyclomatic complexity – are applied to a collection of 2002 parametric models created by 575 designers. The results show that parametric models generally exhibit a number of strong correlations, which reveal a practice of parametric modelling that has as much to do with the management of data as it does with the modelling of geometry. These findings demonstrate the utility of software engineering metrics in the description and analysis of parametric models.