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Interactive Role Stereotype-Based Visualization To Comprehend Software Architecture

机译:基于交互角色刻板印象的可视化理解软件架构

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Motivation: Software visualization can be helpful in comprehending the architecture of large software systems. Traditionally, software visualisation focuses on representing the structural perspectives of systems. In this paper we enrich this perspective by adding the notion of role-stereotype. This rolestereotype carries information about the type of functionality that a class has in the system as well as the types of collaborations with other classes that it typically has.Objective: We propose an interactive visualization called RoleViz, that visualizes system architectures in which architectural elements are annotated with their role-stereotypes.Method: We conducted a user-study in which developers use RoleViz and Softagram (a commercial tool for software architecture comprehension) to solve two separate comprehension tasks on a large open source system. We compared RoleViz against Softagram in terms of participant’s: (i) perceived cognitive load, (ii) perceived usability, and (iii) understanding of the system. Result: In total, 16 developers participated in our study. Six of the participants explicitly indicated that visualizing roles helped them complete the assigned tasks. Our observations indicate significant differences in terms of participant’s perceived usability and understanding scores.Conclusion: The participants achieved better scores on completing software understanding tasks with RoleViz without any cognitive-load penalty.Demo: https://youtu.be/HqCUAlai4qw?t=258
机译:动机:软件可视化有助于理解大型软件系统的体系结构。传统上,软件可视化专注于表示系统的结构视角。在本文中,我们通过添加角色定型观念来丰富这种观点。这个角色刻板印象包含有关类在系统中具有的功能类型以及与它通常具有的其他类的协作类型的信息。目的:我们提出了一种交互式的可视化方法,称为RoleViz,用于可视化系统架构,其中体系结构元素是方法:我们进行了一项用户研究,开发人员使用RoleViz和Softagram(用于软件体系结构理解的商业工具)来解决大型开源系统上的两个单独的理解任务。我们根据参与者的以下方面将RoleViz与Softagram进行了比较:(i)感知的认知负担,(ii)感知的可用性,以及(iii)对系统的理解。结果:总共有16个开发人员参加了我们的研究。六位参与者明确表示,可视化角色有助于他们完成分配的任务。我们的观察结果表明,参与者在感知的可用性和理解分数方面存在显着差异。结论:参与者在使用RoleViz完成软件理解任务时获得了较好的分数,而没有任何认知负担。演示:https://youtu.be/HqCUAlai4qw?t = 258

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