Significantly Increasing the Usability of Model Analysis Tools through Visual Feedback

National audienceA plethora of theoretical results are available which make possible the use of dynamic analysis and model-checking for software and system models expressed in high-level modeling languages like UML, SDL or AADL. Their usage is hindered by the complexity of information processing demanded from the modeler in order to apply them and to effectively exploit their results. Our thesis is that by improving the visual presentation of the analysis results, their exploitation can be highly improved. To support this thesis, we define a trace analysis approach based on the extraction of high-level semantics events from the low-level output of a simulation or model-checking tool. This extraction offers the basis for new types of scenario visualizations, improving scenario understanding and exploration. This approach was implemented in our UML/SysML analyzer and was validated in a controlled experiment that shows a significant increase in the usability of our tool, both in terms of task performance speed and in terms of user satisfaction

Bridging the gap: building better tools for game development

The following thesis is about questioning how we design game making tools, and how developers may build easier tools to use. It is about the highlighting the inadequacies of current game making programs as well as introducing Goal-Oriented Design as a possible solution. It is also about the processes of digital product development, and reflecting on the necessity for both design and development methods to work cohesively for meaningful results. Interaction Design is in essence the abstracting of key relations that matter to the contextual environment. The result of attempting to tie the Interaction Design principles, Game Design issues together with Software Development practices has led to the production of the User-Centred game engine, PlayBoard

Structuring visual exploratory analysis of skill demand

The analysis of increasingly large and diverse data for meaningful interpretation and question answering is handicapped by human cognitive limitations. Consequently, semi-automatic abstraction of complex data within structured information spaces becomes increasingly important, if its knowledge content is to support intuitive, exploratory discovery. Exploration of skill demand is an area where regularly updated, multi-dimensional data may be exploited to assess capability within the workforce to manage the demands of the modern, technology- and data-driven economy. The knowledge derived may be employed by skilled practitioners in defining career pathways, to identify where, when and how to update their skillsets in line with advancing technology and changing work demands. This same knowledge may also be used to identify the combination of skills essential in recruiting for new roles. To address the challenges inherent in exploring the complex, heterogeneous, dynamic data that feeds into such applications, we investigate the use of an ontology to guide structuring of the information space, to allow individuals and institutions to interactively explore and interpret the dynamic skill demand landscape for their specific needs. As a test case we consider the relatively new and highly dynamic field of Data Science, where insightful, exploratory data analysis and knowledge discovery are critical. We employ context-driven and task-centred scenarios to explore our research questions and guide iterative design, development and formative evaluation of our ontology-driven, visual exploratory discovery and analysis approach, to measure where it adds value to users’ analytical activity. Our findings reinforce the potential in our approach, and point us to future paths to build on

Spatial audio in small display screen devices

Our work addresses the problem of (visual) clutter in mobile device interfaces. The solution we propose involves the translation of technique-from the graphical to the audio domain-for expliting space in information representation. This article presents an illustrative example in the form of a spatialisedaudio progress bar. In usability tests, participants performed background monitoring tasks significantly more accurately using this spatialised audio (a compared with a conventional visual) progress bar. Moreover, their performance in a simultaneously running, visually demanding foreground task was significantly improved in the eye-free monitoring condition. These results have important implications for the design of multi-tasking interfaces for mobile devices

ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning

To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically search for good models. Due to the huge model search space, it is impossible to try all models. Users tend to distrust automatic results and increase the search budget as much as they can, thereby undermining the efficiency of AutoML. To address these issues, we design and implement ATMSeer, an interactive visualization tool that supports users in refining the search space of AutoML and analyzing the results. To guide the design of ATMSeer, we derive a workflow of using AutoML based on interviews with machine learning experts. A multi-granularity visualization is proposed to enable users to monitor the AutoML process, analyze the searched models, and refine the search space in real time. We demonstrate the utility and usability of ATMSeer through two case studies, expert interviews, and a user study with 13 end users.Comment: Published in the ACM Conference on Human Factors in Computing Systems (CHI), 2019, Glasgow, Scotland U