Abstract

Abstract

Dynamic graph visualization focuses on the challenge of representing the evolution of relationships between entities in readable, scalable and effective diagrams. This work surveys the growing number of approaches in this discipline. We derive a hierarchical taxonomy of techniques by systematically categorizing and tagging publications. While static graph visualizations are often divided into node-link and matrix representations, we identify the representation of time as the major distinguishing feature for dynamic graph visualizations: either graphs are represented as animated diagrams or as static charts based on a timeline. Evaluations of animated approaches focus on dynamic stability for preserving the viewer's mental map or, in general, compare animated diagrams to timeline-based ones. A bibliographic analysis provides insights into the organization and developmentof the field and its community. Finally, we identify and discuss challenges for future research. We also provide feedback from experts, collected with a questionnaire, which gives a broad perspective of these challenges and the current state of the field.

Abstract

We present a novel dynamic graph visualization technique based on node-link diagrams. The graphs are drawn side-byside from left to right as a sequence of narrow stripes that are placed perpendicular to the horizontal time line. The hierarchically organized vertices of the graphs are arranged on vertical, parallel lines that bound the stripes; directed edges connect these vertices from left to right. To address massive overplotting of edges in huge graphs, we employ a splatting approach that transforms the edges to a pixel-based scalar field. This field represents the edge densities in a scalable way and is depicted by non-linear color mapping. The visualization method is complemented by interaction techniques that support data exploration by aggregation, filtering, brushing, and selective data zooming. Furthermore, we formalize graph patterns so that they can be interactively highlighted on demand. A case study on software releases explores the evolution of call graphs extracted from the JUnit open source software project. In a second application, we demonstrate the scalability of our approach by applying it to a bibliography dataset containing more than 1.5 million paper titles from 60 years of research history producing a vast amount of relations between title words.

The revision history of a software system conveys important information about how and why the system evolved in time.

While there is a considerable amount of research on analyzing the change information stored in software repositories, only few researcher have looked at software changes contained in email archives in form of patches. In this paper we look at the email archives of two open source projects and answer questions like the following: How many emails contain patches? How long does it take for a patch to be accepted? Does the size of the patch influence its chances to be accepted or the duration until it gets accepted? Obviously, the answers to these questions can be helpful for the authors of patches, in particular because some of the answers are surprising.

Refactorings are program transformations which should preserve the program behavior. Consequently, we expect that during phases when there are mostly refactorings in the change history of a system, only few new bugs are introduced. For our case study we analyzed the version histories of several open source systems and reconstructed the refactorings performed. Furthermore, we obtained bug reports from various sources depending on the system. Based on this data we identify phases when the above hypothesis holds and those when it doesn't.