Research Papers

A Generative Network Model for Product Evolution

[+] Author and Article Information
Qize Le

School of Mechanical and Materials Engineering,
Washington State University,
Pullman, WA 99164

Zhenghui Sha

School of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47907

Jitesh H. Panchal

Assistant Professor
School of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47907
e-mail: panchal@purdue.edu

1Corresponding author.

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINNERING. Manuscript received October 9, 2012; final manuscript received October 23, 2013; published online January 10, 2014. Editor: Bahram Ravani.

J. Comput. Inf. Sci. Eng 14(1), 011003 (Jan 10, 2014) (12 pages) Paper No: JCISE-12-1178; doi: 10.1115/1.4025856 History: Received October 09, 2012; Revised October 23, 2013

Modeling the structure and evolution of products is important from the standpoint of improving quality and maintainability. With the increasing popularity of open-source processes for developing both software and physical systems, there is a need to develop computational models of product evolution in such dynamic product developments scenarios. Existing studies on the evolution of products involve modeling products as networks, taking snapshots of the structure at different time steps, and comparing the structural characteristics. Such approaches are limited because they do not capture the underlying dynamics through which products evolve. In this paper, we take a step toward addressing this gap by presenting a generative network model for product evolution. The generative model is based on different mechanisms though which networks evolve—addition and removal of nodes, addition and removal of links. The model links local network observations to global network structures. It is utilized for modeling and analyzing the evolution of a software product (Drupal) and a physical product (RepRap) developed by open source processes. For the software product, the generated networks are compared with the actual product structures using various network measures including average degree, density, clustering coefficients, average shortest path, propagation cost, clustered cost, and degree distributions. For the physical product, the product evolution is analyzed in terms of the proposed mechanisms. The proposed model has three general applications: longitudinal studies of a product's evolution, cross-sectional studies of evolution of different products, and predictive analyzes.

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Grahic Jump Location
Fig. 1

Six mechanisms for the evolution of product networks

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Fig. 2

Probabilities of creation of links between new and existing nodes (mechanism (c))

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Fig. 3

Probabilities of new nodes linking with each other (mechanism (d))

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Fig. 4

Probabilities of existing nodes linking with each other (mechanism (e))

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Fig. 5

Probabilities of removal of existing links (mechanism (f))

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Fig. 6

Degree distribution of Drupal product structure

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Fig. 7

Relationship between the number of functions and interfaces

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Fig. 8

The execution of the model based on mechanisms

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Fig. 9

Comparison of evolutionary characteristics at product level between Drupal product and the models. (i) The product, (ii) model with initial version 2 network, (iii) model with initial random network, (iv) model with initial scale-free network

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Fig. 10

Comparison of degree distribution between Drupal product and the models

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Fig. 13

Degree distributions of the three versions of RepRap



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