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ThinkMind // International Journal On Advances in Software, volume 7, numbers 1 and 2, 2014 // View article soft_v7_n12_2014_4


Detecting Software Usability Deficiencies Through Pinpoint Analysis

Authors:
Dan Tamir
Divya Dasari
Oleg Komogortsev
Gregory LaKomski
Carl Mueller

Keywords: Software Development; Software Usability; Human Computer Interaction; Pinpoint Analysis; Pattern Recognition; Clustering

Abstract:
The effort-based model of usability is used for evaluating user interface (UI), development of usable software, and pinpointing software usability defects. In this context, the term pinpoint analysis refers to identifying and locating software usability deficiencies and correlating these deficiencies with the UI software code. For example, often, when users are in a state of confusion and not sure how to proceed using the software, they tend to gaze around the screen trying to find the best way to complete a task. This behavior is referred to as excessive effort. In this paper, the underlying theory of effort-based usability evaluation along with pattern recognition techniques are used to produce an innovative framework for the objective of identifying usability deficiencies in software. Pattern recognition techniques and methods are applied to data gathered throughout user interaction with software in an attempt to identify excessive effort segments via automatic classification of segments of video files containing eye-tracking results. The video files are automatically divided into segments using event-based segmentation, where a segment is the time between two consecutive keyboard/mouse clicks. Subsequently, data reduction programs are run on the segments for generating feature vectors. Several different classification procedures are applied to the features in order to automatically classify each segment into excessive and non-excessive effort segments. This allows developers to focus on the excessive effort segments and further analyze usability deficiencies in these segments. To verify the results of the pattern recognition procedures, the video is manually classified into excessive and non-excessive segments and the results of automatic and manual classification are compared. The paper details the theory of effort-based pinpoint analysis and reports on experiments performed to evaluate the utility of this theory. Experiment results show more than 40% reduction in time for usability testing.

Pages: 44 to 62

Copyright: Copyright (c) to authors, 2014. Used with permission.

Publication date: June 30, 2014

Published in: journal

ISSN: 1942-2628

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