In terms of software maintenance and comprehension, the fields of software engineering and software visualization have produced several methods and tools. However, they are typically separate tools in practice. In this paper, we present a novel methodology of combining software analysis and software visualization tools via an interactive visual workflow modeling approach. Standard software analysis tools are also limited in that they support only well-known metrics or are too complicated to use for generating custom software metrics. To address these shortcomings, our approach focuses on visual elements, their configurations, and interconnectivity rather than a data ontology and querying language. In order to test and validate our methodology, we developed a prototype tool called VIMETRIK (Visual Specification of Metrics). Our preliminary evaluation study illustrates the intuitiveness and ease-of-use of our approach with regard to understanding software measurement and analysis data
An essential component in the evolution and maintenance of large-scale software systems is to track the structure of a software system to explain how a system has evolved to its present state and to predict its future development. Current mainstream tools facilitating the structural evolution of software architecture by visualization are confined with easy to integrate visualization techniques such as node-link diagrams, while more applicable solutions have been proposed in academic research. To bridge this gap, we have incorporated additional views to a conventional tool that integrates an interactive evolving city layout and a combination of charts. However, due to a limited access to the stakeholders it was not possible to solicit them for a formal modeling process. Instead, an early prototype was developed and a controlled experiment was conducted to illustrate the vital role of such in-situ visualization techniques when aiming to understanding the evolution of software architecture.
Computer supported collaborative work supports the interaction and joint task solving between humans by setting a machine or computer in-between. Similarly, collaborative environments support decisionmakings incorporating different points of view and a variety of competences. As such, the identification and selection of the underlying requirements involved in collaboration is a challenging task. In order to facilitate this task, we performed a thorough literature review with the aim to discern such requirements. The result is a set of qualitative criteria that can be applied to generic computer supported collaboration environments. Furthermore, the mapping of the generic catalogue to a specific collaboration task is demonstrated.
Visual analytics has been widely studied in the past decade both in academia and industry to improve data exploration, minimize the overall cost, and improve data analysis. In this chapter, we explore the idea of visual analytics in the context of simulation data. This would then provide us with the capability to not only explore our data visually but also to apply machine learning models in order to answer high-level questions with respect to scheduling, choosing optimal simulation parameters, finding correlations, etc. More specifically, we examine state-of-the-art tools to be able to perform these above-mentioned tasks. Further, to test and validate our methodology we followed the human-centered design process to build a prototype tool called ViDAS (Visual Data Analytics of Simulated Data). Our preliminary evaluation study illustrates the intuitiveness and ease-of-use of our approach with regards to visual analysis of simulated data.
The high processing capabilities of current smartphones and the availability of wearable Virtual Reality (VR) toolkits make it possible for normal users to use VR environments on go for gaming or learning. However, users normally need to use their head movement for interacting or moving the objects inside these VR environments. This can cause the dizzy and nauseas feelings amongst the users as well as neck/head muscle tiredness effect, which restrict them to use it for longer time. Targeting this problem, we propose to use the 3D accelerometer inside the current smartwatches. In this way, users would easily interact or control the movement inside the mobile VR environment through their wrist movement. For this purpose, we built MoCon-VR framework that provides the motion control in Google Cardboard-based mobile VR environment through smartwatch's 3D accelerometer sensor. A preliminary conducted study with 10 participants shows less dizzy and nausea feelings amongst the participants as well as less neck/head muscle pains, in compared to the standard head-movement-based approach. Virtual reality. Mobile devices. Mobile VR applications. Smartwatch. Google cardboard.
Abstract. In recent times, visual analysis has become increasingly important, especially in the area of software measurement, as most of the data from software measurement is multivariate. In this regard, standard software analysis tools are limited by their lack of ability to process huge collections of multidimensional data sets; current tools are designed to either support only wellknown metrics or are too complicated to use for generating custom software metrics. Furthermore, the analyst requires extensive knowledge of the underlying data schemas and the relevant querying language. To address these shortcomings, we propose an interactive visual approach that focuses on visual elements, their configurations, and interconnectivity rather than a data ontology and querying language. In order to test and validate our methodology, we developed a prototype tool called VIM ETRIK (Visual Specification of M etrics). Our preliminary evaluation study illustrates the intuitiveness and ease-of-use of our approach to understand software measurement and analysis data.
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