Towards the Detection of UX Smells: The Support of Visualizations

Daily experiences in working with various types of computer systems show that, despite the offered functionalities, users have many difficulties, which affect their overall User eXperience (UX). The UX focus is on aesthetics, emotions and social involvement, but usability has a great influence on UX. Usability evaluation is acknowledged as a fundamental activity of the entire development process in software practices. Research in Human-Computer Interaction has proposed methods and tools to support usability evaluation. However, when performing an evaluation study, novice evaluators still have difficulties to identify usability problems and to understand their causes: they would need easier to use and possibly automated tools. This article describes four visualization techniques whose aim is to support the work of evaluators when performing usability tests to evaluate websites. Specifically, they help detect "usability smells", i.e. hints on web pages that might present usability problems, by visualizing the paths followed by the test participants when navigating in a website to perform a test task. A user study with 15 participants compared the four techniques and revealed that the proposed visualizations have the potential to be valuable tools for novice usability evaluators. These first results should push researchers towards the development of further tools that are capable to support the detection of other types of UX smells in the evaluation of computer systems and that can be translated into common industry practices

Website Clickstream Data Visualization Using Improved Markov Chain Modelling In Apache Flume

Clickstream data analysis is considered as the process of collecting, analysing and reporting the aggregate data about the web pages a visitor clicks. Visualizing the clickstream data has gained significant importance in many applications like web marketing, customer prediction, product management, etc. Most existing works employ different tools for visualizing along with techniques like Markov chain modelling. However the accuracy of the methods can be improved when the shortcomings are resolved. Markov chain modelling has problems of occlusion and unable to provide clear display of data visualizing. These issues can be resolved by improving the Markov chain model by introducing a heuristic method of Kolmogorov– Smirnov distance and maximum likelihood estimator for visualizing. These concepts are employed between the underlying distribution states to minimize the Markov distribution. The proposed model named as WebClickviz is performed in Hadoop Apache Flume which is a highly advanced tool. The clickstream data visualization accuracy can be improved when Apache Flume tools are used. The performance evaluation are made on a specific website clickstream data which shows the proposed model of visualization has better performance than existing models like VizClick