可扩展的协作软件可视化作为一种服务:简短的行业和经验论文

Alexander Krause-Glau, W. Hasselbring
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引用次数: 3

摘要

例如,软件开发人员使用软件可视化来理解程序。在此背景下,研究较少的方面是在线可视化服务的协同使用。本文提出了一个用于程序理解的协同软件可视化服务的概念设计和原型实现。中心思想是一个在线可用的软件即服务应用程序,它分析、保存和可视化用户打算通过软件可视化来探索和理解的软件应用程序。最终实现是对我们的实时跟踪可视化工具ExplorViz的重新开发。与其他方法相比,ExplorViz利用WebGL和其他浏览器技术而不是游戏引擎来呈现其可视化效果。因此,我们实现了与平台无关的互操作性,这对(远程)协作至关重要。在这里,用户可以通过我们的协作可用桌面、虚拟现实和增强现实模式探索相同的可视化。我们的原型遵循十二因素应用程序方法来构建包含多个可扩展微服务的云原生应用程序。由于水平扩展,我们的实现能够分析大量的可视化数据;因此,允许多个用户同时使用软件可视化服务。我们进行了一组初步的性能实验,对原型的可伸缩性进行基准测试。结果表明,评价的服务随负载的增加呈线性扩展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scalable Collaborative Software Visualization as a Service: Short Industry and Experience Paper
Software visualizations are used by software developers, for instance, for program comprehension. In this context, a less researched aspect is the collaborative use of online visual-ization services. This paper presents the conceptual design and a prototype implementation of our approach for a collaborative software visualization service for program comprehension. The central idea is an online available software-as-a-service application that analyzes, persists, and visualizes software applications which users intend to explore and comprehend via software visualization. The resulting implementation is a redevelopment of our live trace visualization tool Explor Viz. In comparison to other approaches, ExplorViz utilizes WebGL and other browser technologies instead of a game engine to render its visualizations. As a result, we achieve a platform-independent interoperability that is crucial for (remote) collaboration. Here, users can explore the same visualization via our collaboratively usable desktop, virtual reality, and also augmented reality modes. Our prototype follows the Twelve-Factor App methodology to build a cloud-native application that comprises multiple scalable microservices. Thanks to horizontal scaling, our implementation is capable to analyze a large amount of visualization data; thus, allowing multiple users to simultaneously use the software visualization service. We conducted a set of preliminary performance exper-iments to benchmark our prototype's scalability. Results show that the evaluated service scales linearly with increasing load.
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