Enhancing Usability Inspection Through Data-Mining Techniques: An Automated Approach for Detecting Usability Problem Patterns of Academic Websites

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• 关键词：Usability ; Usability engineering ; Qualitative usability inspection ; Heuristic evaluation ; Context of use ; Usability problem patterns ; Data ; mining knowledge discovering in databases ; Association rules ; Decision trees
• 刊名：Lecture Notes in Computer Science
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：10127
• 期：1
• 页码：229-247
• 丛书名：Intelligent Human Computer Interaction
• ISBN：978-3-319-52503-7
• 卷排序：10127

文摘

Usability is one of important attribute of software quality. It is associated with the “ease of use” of any system. Usability evaluation is becoming significant component of software development. Usability evaluation is performed through qualitative assessments. Qualitative assessments can be attained through Qualitative usability inspection (QUI). QUI methods emphasize on evaluating the interface of a specific system. These methods turn out to be complicated when huge number of systems related to similar context of use, are considered jointly to impart a general diagnosis. The principal cause for this is due to substantial quantity of information that should be conceptualized simultaneously. To handle substantial quantity of information, this paper proposes a novel approach that integrates QUI with automated woorank tool and data-mining techniques (association rules and decision tree). To validate this proposed approach, 50-academic websites are evaluated and usability problems patterns related to academic websites are identified by processing 2475 records.