Usage-Based Automatic Detection of Usability Smells

Harms, Patrick; Grabowski, Jens

doi:10.1007/978-3-662-44811-3_13

Cited by 16 publications

(4 citation statements)

References 16 publications

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“…The second work similar to ours is that of Harms et al (Harms & Grabowski 2014), which also use the concept of usability smell to point to high-level problems in specific webpage elements. They use a slightly different definition of usability smell, by referring to them as exceptional user behaviors that may indicate a usability issue.…”

Section: Automated Log Analysis and Evaluationmentioning

confidence: 97%

Automatic detection of usability smells in web applications

Grigera

Garrido

Rivero

et al. 2017

International Journal of Human-Computer Studies

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Usability assessment of web applications continues to be an expensive and often neglected practice. While large companies are able to spare resources for studying and improving usability in their products, smaller businesses often divert theirs in other aspects. To help these cases, researches have devised automatic approaches for user interaction analysis, and there are commercial services that offer automated usability statistics at relatively low fees. However, most existing approaches still fall short in specifying the usability problems concretely enough to identify and suggest solutions. In this work we describe usability smells of user interaction, i.e., hints of usability problems on running web applications, and the process in which they can be identified by analyzing user interaction events. We also describe USF, the tool that implements the process in a fully automated way with minimum setup effort. USF analyses user interaction events on-the-fly, discovers usability smells and reports them together with a concrete solution in terms of a usability refactoring, providing usability advice for deployed web applications.

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Section: Automated Log Analysis and Evaluationmentioning

confidence: 97%

Automatic detection of usability smells in web applications

Grigera

Garrido

Rivero

et al. 2017

International Journal of Human-Computer Studies

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“…that are to be analyzed. Harms and Grabowski [10] present a proposal that processes log files, generates task trees with the user actions, and identifies usability smells. The values that these usability metrics take must be interpreted in such a way that they have an impact on the next set of iterations of the software development process.…”

Section: Background and Work Of Othersmentioning

confidence: 99%

A Framework for Identifying Sequences of Interactions That Cause Usability Problems in Collaborative Systems

et al. 2021

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Collaborative systems support shared spaces, where groups of users exchange interactions. In order to ensure the usability of these systems, an intuitive interactions’ organization and that each user has awareness information to know the activity of others are necessary. Usability laboratories allow evaluators to verify these requirements. However, laboratory usability evaluations can be problematic for reproducing mobile and ubiquitous contexts, as they restrict the place and time in which the user interacts with the system. This paper presents a framework for building software support that it collects human–machine interactions in mobile and ubiquitous contexts and outputs an assessment of the system’s usability. This framework is constructed through learning that is based on neural networks, identifying sequences of interactions related to usability problems when users carry out collaborative activities. The paper includes a case study that puts the framework into action during the development process of a smartphone application that supports collaborative sport betting.

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“…The system records usage data during real use, identifies usage patterns, and indicates potential user interface design problems. Harms and Grabowsky [6] proposed to transform the recorded user interaction in task trees that are then checked to identify usability issues. The goal of such contributions is to identify a method to record user interactions and then further analyse the logs in order to highlight usability problems.…”

Section: Web Analytics Toolsmentioning

confidence: 99%

A Visual Tool for Analysing IoT Trigger/Action Programming

Corcella

Manca

Paternò

et al. 2018

Lecture Notes in Computer Science

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The Trigger-Action programming paradigm has been widely adopted in the last few years, especially in the Internet of Things (IoT) domain because it allows end users without programming experience to describe how their applications should react to the many events that can occur in such very dynamic contexts. Several end user development tools exist, in both the research and industrial fields, which aim to support the increasing need to specify such rules. Thus, it becomes important for application developers and domain experts to enrich such environments with functionalities able to monitor how users actually interact with such rule editors, and show useful information to analyse the end user activity. In this paper, we present a visual tool for monitoring and analysing how users interact with a trigger-action rule editor. The goal is to provide a tool useful to better understand what end users' personalization needs are, how they are expressed, how users actually specify rules, and whether users encounter any issues in interacting with the personalization features offered by the editors. The proposed solution supports the analysis through a dashboard and a set of timelines describing the actual use of the personalization tool, with the possibility to select specific events of interest. It moreover provides data useful for understanding the types of triggers, actions and rules actually composed by users, and whether they effectively exploit the personalization features offered.

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Usage-Based Automatic Detection of Usability Smells

Cited by 16 publications

References 16 publications

Automatic detection of usability smells in web applications

Automatic detection of usability smells in web applications

A Framework for Identifying Sequences of Interactions That Cause Usability Problems in Collaborative Systems

A Visual Tool for Analysing IoT Trigger/Action Programming

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