Graduate Studies

 

Date of this Version

Spring 4-19-2012

Comments

A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfilment of Requirements For the Degree of Master of Science, Major: Computer Science, Under the Supervision of Professor Myra B. Cohen. Lincoln, Nebraska: May, 2012

Copyright (c) 2012 Amanda Swearngin

Abstract

Numerous tools and techniques for human performance modeling have been introduced in the field of human-computer interaction. With such tools comes the ability to model legacy applications. Models can be used to compare design ideas to existing applications, or to evaluate products against those of competitors. One such modeling tool, CogTool, allows user interface designers and analysts to mock up design ideas, demonstrate tasks, and obtain human performance predictions for those tasks. This is one step towards a simple and complete analysis process, but it still requires a large amount of manual work. Graphical user interface (GUI) testing tools are orthogonal in that they provide automated model extraction of interfaces, methods for test case generation, and test case automation; however, the resulting test cases may not mimic tasks as they are performed by experienced users.

In this thesis, we present CogTool-Helper, a tool that merges automated GUI testing with human performance modeling. It utilizes techniques from GUI testing to automatically create CogTool storyboards and models. We have designed an algorithm to find alternative methods for performing the same task so that the UI designer or analyst can study how a user might interact with the system beyond what they have specified. We have also implemented an approach to generate functional test cases that perform tasks in a way that mimics the user. We evaluate the feasibility of our approach in a human performance regression testing scenario in LibreOffice, and show how CogTool-Helper enhances the UI designer's analysis process. Not only do the generated designs remove the need for manual design construction, but the resulting data allows new analyses that were previously not possible.

Adviser: Myra B. Cohen

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