Visualization of graphs and trees for software analysis

A software architecture is an abstraction of a software system, which is indispensable for many software engineering tasks. Unfortunately, in many cases information pertaining to the software architecture is not available, outdated, or inappropriate for the task at hand. The RECONSTRUCTOR project focuses on software architecture reconstruction, i.e., obtaining architectural information from an existing system. Our research, which is part of RECONSTRUCTOR, focuses on interactive visualization and tries to answer the following question: How can users be enabled to understand the large amounts of information relevant for program understanding using visual representations? To answer this question, we have iteratively developed a number of techniques for visualizing software systems. A large number of these cases consists of hierarchically organized data, combined with adjacency relations. Examples are function calls within a hierarchically organized software system and correspondence relations between two different versions of a hierarchically organized software system. Hierarchical Edge Bundles (HEBs) are used to visualize adjacency relations in hierarchically organized data, such as the aforementioned function calls within a software system. HEBs significantly reduce visual clutter by visually bundling relations together. Massive Sequence Views (MSVs) are used in conjunction with HEBs to enable analysis of sequences of relations, such as function-call traces. HEBs are furthermore used to visually compare hierarchically organized data, e.g., two different versions of a software system. HEBs visually emphasize splits, joins, and relocations of subhierarchies and provide for interactive selection of sets of relations. Since HEBs require a hierarchy to perform the bundling, we present Force-Directed Edge Bundles (FDEBs) as an alternative to visually bundle relations together in the absence of a hierarchical component. FDEBs use a self-organizing approach to bundling in which edges are modeled as flexible springs that can attract each other. As a result, visual clutter is reduced and high-level edge patterns are better visible. Finally, in all these methods, a clear depiction of the direction of edges is important. We have therefore performed a separate study in which we evaluated ten representations (including the standard arrow) for depicting directed edges in a controlled user study

Managing the co-evolution of software artifacts

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Visualization of graphs and trees for software analysis

A software architecture is an abstraction of a software system, which is indispensable for many software engineering tasks. Unfortunately, in many cases information pertaining to the software architecture is not available, outdated, or inappropriate for the task at hand. The RECONSTRUCTOR project focuses on software architecture reconstruction, i.e., obtaining architectural information from an existing system. Our research, which is part of RECONSTRUCTOR, focuses on interactive visualization and tries to answer the following question: How can users be enabled to understand the large amounts of information relevant for program understanding using visual representations? To answer this question, we have iteratively developed a number of techniques for visualizing software systems. A large number of these cases consists of hierarchically organized data, combined with adjacency relations. Examples are function calls within a hierarchically organized software system and correspondence relations between two different versions of a hierarchically organized software system. Hierarchical Edge Bundles (HEBs) are used to visualize adjacency relations in hierarchically organized data, such as the aforementioned function calls within a software system. HEBs significantly reduce visual clutter by visually bundling relations together. Massive Sequence Views (MSVs) are used in conjunction with HEBs to enable analysis of sequences of relations, such as function-call traces. HEBs are furthermore used to visually compare hierarchically organized data, e.g., two different versions of a software system. HEBs visually emphasize splits, joins, and relocations of subhierarchies and provide for interactive selection of sets of relations. Since HEBs require a hierarchy to perform the bundling, we present Force-Directed Edge Bundles (FDEBs) as an alternative to visually bundle relations together in the absence of a hierarchical component. FDEBs use a self-organizing approach to bundling in which edges are modeled as flexible springs that can attract each other. As a result, visual clutter is reduced and high-level edge patterns are better visible. Finally, in all these methods, a clear depiction of the direction of edges is important. We have therefore performed a separate study in which we evaluated ten representations (including the standard arrow) for depicting directed edges in a controlled user study

A user study on visualizing directed edges in graphs

Graphs are often visualized using node-link representations: vertices are depicted as dots, edges are depicted as (poly)lines connecting two vertices. A directed edge running from vertex A to B is generally visualized using an arrow representation: a (poly)line with a triangular arrowhead at vertex B. Although this representation is intuitive, it is not guaranteed that a user is able to determine edge direction as quickly and unambiguously as possible; alternative representations that exhibit less occlusion and visual clutter might be better suited. To investigate this, we developed five additional directed-edge representations using combinations of shape and color. We performed a user study in which subjects performed different tasks on a collection of graphs using these representations and combinations thereof to investigate which representation is best in terms of speed and accuracy. We present our initial hypotheses, the outcome of the user studies, and recommendations regarding directed-edge visualization

Force-directed edge bundling for graph visualization

Graphs depicted as node-link diagrams are widely used to show relationships between entities. However, node-link diagrams comprised of a large number of nodes and edges often suffer from visual clutter. The use of edge bundling remedies this and reveals high-level edge patterns. Previous methods require the graph to contain a hierarchy for this, or they construct a control mesh to guide the edge bundling process, which often results in bundles that show considerable variation in curvature along the overall bundle direction. We present a new edge bundling method that uses a self-organizing approach to bundling in which edges are modeled as flexible springs that can attract each other. In contrast to previous methods, no hierarchy is used and no control mesh. The resulting bundled graphs show significant clutter reduction and clearly visible high-level edge patterns. Curvature variation is furthermore minimized, resulting in smooth bundles that are easy to follow. Finally, we present a rendering technique that can be used to emphasize the bundling

A perceptually based spectral model for isotropic textures

Color is routinely used to visualize interval and ordinal data, while texture is not. For color, a variety of perceptually based models is available, which can be used to convey data via straightforward mapping. The dimensionality of texture is less well understood and there are almost no perceptually based and validated models available to generate textures on demand. We present a perceptually based texture synthesis model for isotropic textures. The model uses additive synthesis of band-limited noise in the spectral domain, comparable to the RGB (red, green, blue) model for color. Via user experiments, we have derived a three-dimensional model to control the amplitudes per band in a perceptually intuitive way, comparable to the HSV (hue, saturation, value) model for color. The three dimensions used are contrast, spatial frequency, and spectral purity. Besides a mapping to the amplitudes, we have derived a mapping to a perceptually equidistant space. We show how such textures can be combined with color; applications are presented

A perceptually based spectral model for isotropic textures

Color is routinely used to visualize interval and ordinal data, while texture is not. For color, a variety of perceptually based models is available, which can be used to convey data via straightforward mapping. The dimensionality of texture is less well understood and there are almost no perceptually based and validated models available to generate textures on demand. We present a perceptually based texture synthesis model for isotropic textures. The model uses additive synthesis of band-limited noise in the spectral domain, comparable to the RGB (red, green, blue) model for color. Via user experiments, we have derived a three-dimensional model to control the amplitudes per band in a perceptually intuitive way, comparable to the HSV (hue, saturation, value) model for color. The three dimensions used are contrast, spatial frequency, and spectral purity. Besides a mapping to the amplitudes, we have derived a mapping to a perceptually equidistant space. We show how such textures can be combined with color; applications are presented

Trace visualization using hierarchical edge bundles and massive sequence views

One way of gaining understanding of a software system is the analysis of dynamic information, i.e., program execution traces. A problem regarding the analysis of such traces is the fact that these are often extremely large: hundreds of thousands and even millions of calls within a single trace are no exception. To aid a user in navigating and understanding these vast amounts of information, we propose to visualize execution traces using two linked views. One is an element interaction view that shows part of a trace, i.e., those calls within a specific time window, using hierarchical edge bundles (HEBs). The other is a more detailed massive sequence view that is synchronized with the HEB view and offers detailed information regarding the temporal location and interleaving of the calls within the current time window. This view also introduces a novel way of antialiasing based on the importance of calls. In this paper, we focus- on the technical aspects of the visualization and rendering techniques that are necessary to produce scalable visualizations using HEBs and massive sequence views