Testing the Usability and Accessibility of Smart TV Applications Using an Automated Model-Based Approach

Abstract

As the popularity of Smart Televisions (TVs) and interactive Smart TV applications (apps) has recently grown, the usability of these apps has become an important quality characteristic. Previous studies examined Smart TV apps from a usability perspective. However, these methods are mainly manual, and the potential of automated model-based testing methods for usability testing purposes has not yet been fully explored. In this paper, we propose an approach to test the usability of Smart TV apps based on the automated generation of a Smart TV user interaction model from an existing app by a specialized automated crawler. By means of this model, defined user tasks in the Smart TV app can be evaluated automatically in terms of their feasibility and estimated user effort, which reflects the usability of the analyzed app. This analysis can be applied in the context of regular users and users with various specific needs. The findings from this model-based automated analysis approach can be used to optimize the user interface of a Smart TV app to increase its usability, accessibility, and quality.

A Systematic Mapping Study of Software Usability Studies

“…It is widely considered to be a far-reaching research area at large [3] and is used in domains such as aerospace [8], computer-based medical devices [9], defense [10], mobile devices [11], web applications [12] etc. Various research and review studies were conducted on software usability [3,[11][12][13][14][15][16][17][18][19][20][21]. However, based on our findings from the existing literature, we observed that a systematic mapping study (SMS) in this domain is lacking, confirming the claim of Bitkina,et al [9] regarding the absence of UX/usability studies.…”

Applied Awareness: Test-Driven GUI Development using Computer Vision and Cryptography

“…There are many automated approaches to UI testing, including applying computer vision techniques to determine quality of a result or to identify affordances in order to create interaction sequences that better cover the application state space [11,4,[6][7][8]2]. These generally seek to compare the final rendering to an expected rendering -after code has been changed, or after states have been traversed.…”

“…Finally, navigating the state transitions of a UI is a distinct challenge, particularly for generating broad case coverage. There is an established line of research into automatically identifying affordances and generating user interactions to trigger comprehensive state changes [8,11,4,5,7,6,2]. In fact, a state-change bug was the original motivation for the current work, although our focus is not specifically on finding or triggering state changes.…”

AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease

“…Initially, the handcrafted feature computation approaches were used for the classification of several CXR abnormalities. Such methods are simple in nature and can work well-with a small amount of data ( 11 , 12 ). However, the handcrafted key points calculation methods need extensive domain information and take huge time to produce accurate results.…”

“…Furthermore, there remains always a trade-off between time complexity and classification results for such techniques. The employment of huge key points enhances the recognition power of these methods but at the cost of the increased computational burden ( 12 ). The usage of small key points causes increase in the efficiency of the hand-coded approaches but results in missing acquiring the significant aspect of image modality which in turn decreases the classification results.…”