Date of Conferral

2017

Degree

Doctor of Information Technology (D.I.T.)

School

Information Systems and Technology

Advisor

Timothy J. Perez

Abstract

Software engineering projects that utilize inappropriate pathfinding algorithms carry a

significant risk of poor runtime performance for customers. Using social network theory,

this experimental study examined the impact of algorithms, frameworks, and map

complexity on elapsed time and computer memory consumption. The 1,800 2D map

samples utilized were computer random generated and data were collected and processed

using Python language scripts. Memory consumption and elapsed time results for each of

the 12 experimental treatment groups were compared using factorial MANOVA to

determine the impact of the 3 independent variables on elapsed time and computer

memory consumption. The MANOVA indicated a significant factor interaction between

algorithms, frameworks, and map complexity upon elapsed time and memory

consumption, F(4, 3576) = 94.09, p < .001, h2 = .095. The main effects of algorithms,

F(4, 3576) = 885.68, p < .001, h2 = .498; and frameworks, F(2, 1787) = 720,360.01, p

.001, h2 = .999; and map complexity, F(2, 1787) = 112,736.40, p < .001, h2 = .992, were

also all significant. This study may contribute to positive social change by providing

software engineers writing software for complex networks, such as analyzing terrorist

social networks, with empirical pathfinding algorithm results. This is crucial to enabling

selection of appropriately fast, memory-efficient algorithms that help analysts identify

and apprehend criminal and terrorist suspects in complex networks before the next attack.

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