多个软件层次的同时可视化分析

C. Schulz, Adrian Zeyfang, M. V. Garderen, Houssem Ben Lahmar, Melanie Herschel, D. Weiskopf
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引用次数: 2

摘要

我们提出了一种树形可视化技术,用于跨多个层次结构和属性的比较。许多软件系统都是按设计分层构建的。例如,开发人员将源代码细分为库、模块和函数。这种设计扩展到软件配置和业务流程,使得软件层次结构更加重要。通常,这些结构元素都带有引用计数、代码质量度量等属性。在整个软件生命周期中,这些层次结构被不同的人多次审查、集成、调试和更改,因此结构元素及其属性的身份无法清晰地跟踪。我们认为,由于缺乏概述,对相似树进行两两比较是一项乏味的任务,特别是当应用于大量层次结构时。因此,我们努力通过将多个相似的树合并成一个超级树来将它们作为一个整体可视化。为了合并结构和组合来自不同树的属性,我们利用Jaccard相似性并在跟踪结构元素及其属性的起源的同时解决匹配问题。我们的可视化方法允许用户使用节点链接图和缩进树图来检查这些超级树。这些图中的节点描述聚合属性,并使用单词大小的线形图描述详细数据。我们通过探索软件存储库的演变和使用来源数据调试数据处理管道来证明我们的方法的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simultaneous Visual Analysis of Multiple Software Hierarchies
We propose a tree visualization technique for comparison of structures and attributes across multiple hierarchies. Many software systems are structured hierarchically by design. For example, developers subdivide source code into libraries, modules, and functions. This design propagates to software configuration and business processes, rendering software hierarchies even more important. Often these structural elements are attributed with reference counts, code quality metrics, and the like. Throughout the entire software life cycle, these hierarchies are reviewed, integrated, debugged, and changed many times by different people so that the identity of a structural element and its attributes is not clearly traceable. We argue that pairwise comparison of similar trees is a tedious task due to the lack of overview, especially when applied to a large number of hierarchies. Therefore, we strive to visualize multiple similar trees as a whole by merging them into one supertree. To merge structures and combine attributes from different trees, we leverage the Jaccard similarity and solve a matching problem while keeping track of the origin of a structure element and its attributes. Our visualization approach allows users to inspect these supertrees using node-link diagrams and indented tree plots. The nodes in these plots depict aggregated attributes and, using word-sized line plots, detailed data. We demonstrate the usefulness of our method by exploring the evolution of software repositories and debugging data processing pipelines using provenance data.
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