John T. Stasko

Visualization of test information to assist fault localization

One of the most expensive and time-consuming components of the debugging process is locating the errors or faults. To locate faults, developers must identify statements involved in failures and select suspicious statements that might contain faults. This paper presents a new technique that uses visualization to assist with these tasks. The technique uses color to visually map the participation of each program statement in the outcome of the execution of the program with a test suite, consisting of both passed and failed test cases. Based on this visual mapping, a user can inspect the statements in the program, identify statements involved in failures, and locate potentially faulty statements. The paper also describes a prototype tool that implements our technique along with a set of empirical studies that use the tool for evaluation of the technique. The empirical studies show that, for the subject we studied, the technique can be effective in helping a user locate faults in a program.

Toward a Deeper Understanding of the Role of Interaction in Information Visualization

Even though interaction is an important part of information visualization (Infovis), it has garnered a relatively low level of attention from the Infovis community. A few frameworks and taxonomies of Infovis interaction techniques exist, but they typically focus on low-level operations and do not address the variety of benefits interaction provides. After conducting an extensive review of Infovis systems and their interactive capabilities, we propose seven general categories of interaction techniques widely used in Infovis: 1) Select, 2) Explore, 3) Reconfigure, 4) Encode, 5) Abstract/Elaborate, 6) Filter, and 7) Connect. These categories are organized around a users intent while interacting with a system rather than the low-level interaction techniques provided by a system. The categories can act as a framework to help discuss and evaluate interaction techniques and hopefully lay an initial foundation toward a deeper understanding and a science of interaction.

A Meta-Study of Algorithm Visualization Effectiveness

Algorithm visualization (AV) technology graphically illustrates how algorithms work. Despite the intuitive appeal of the technology, it has failed to catch on in mainstream computer science education. Some have attributed this failure to the mixed results of experimental studies designed to substantiate AV technologys educational effectiveness. However, while several integrative reviews of AV technology have appeared, none has focused specifically on the softwares effectiveness by analyzing this body of experimental studies as a whole. In order to better understand the effectiveness of AV technology, we present a systematic meta-study of 24 experimental studies. We pursue two separate analyses: an analysis ofindependent variables , in which we tie each study to a particular guiding learning theory in an attempt to determine which guiding theory has had the most predictive success; and an analysis of dependent variables, which enables us to determine which measurement techniques have been most sensitive to the learning benefits of AV technology. Our most significant finding is that how students use AV technology has a greater impact on effectiveness than what AV technology shows them. Based on our findings, we formulate an agenda for future research into AV effectiveness.

Tango: a framework and system for algorithm animation

The role of animation in understanding and evaluating programs and developing new programs is discussed. A framework for algorithm animation, called Tango, and a system based on that framework are introduced. Related work is reviewed, and the conceptual framework on which Tango is based is examined. The implementation of the Tango system is described. The first-fit bin-packing algorithm is animated to illustrate how the path-transition paradigm simplifies algorithm animation. >

Jigsaw: Supporting Investigative Analysis through Interactive Visualization

Investigative analysts who work with collections of text documents connect embedded threads of evidence in order to formulate hypotheses about plans and activities of potential interest. As the number of documents and the corresponding number of concepts and entities within the documents grow larger, sense-making processes become more and more difficult for the analysts. We have developed a visual analytic system called Jigsaw that represents documents and their entities visually in order to help analysts examine reports more efficiently and develop theories about potential actions more quickly. Jigsaw provides multiple coordinated views of document entities with a special emphasis on visually illustrating connections between entities across the different documents.

Low-level components of analytic activity in information visualization

Existing system level taxonomies of visualization tasks are geared more towards the design of particular representations than the facilitation of user analytic activity. We present a set of ten low level analysis tasks that largely capture peoples activities while employing information visualization tools for understanding data. To help develop these tasks, we collected nearly 200 sample questions from students about how they would analyze five particular data sets from different domains. The questions, while not being totally comprehensive, illustrated the sheer variety of analytic questions typically posed by users when employing information visualization systems. We hope that the presented set of tasks is useful for information visualization system designers as a kind of common substrate to discuss the relative analytic capabilities of the systems. Further, the tasks may provide a form of checklist for system designers.

Focus+context display and navigation techniques for enhancing radial, space-filling hierarchy visualizations

Radial, space-filling visualizations can be useful for depicting information hierarchies, but they suffer from one major problem. As the hierarchy grows in size, many items become small, peripheral slices that are difficult to distinguish. We have developed three visualization/interaction techniques that provide flexible browsing of the display. The techniques allow viewers to examine the small items in detail while providing context within the entire information hierarchy. Additionally, smooth transitions between views help users maintain orientation within the complete information space.

A taxonomy of ambient information systems: four patterns of design

Researchers have explored the design of ambient information systems across a wide range of physical and screen-based media. This work has yielded rich examples of design approaches to the problem of presenting information about a users world in a way that is not distracting, but is aesthetically pleasing, and tangible to varying degrees. Despite these successes, accumulating theoretical and craft knowledge has been stymied by the lack of a unified vocabulary to describe these systems and a consequent lack of a framework for understanding their design attributes. We argue that this area would significantly benefit from consensus about the design space of ambient information systems and the design attributes that define and distinguish existing approaches. We present a definition of ambient information systems and a taxonomy across four design dimensions: Information Capacity, Notification Level, Representational Fidelity, and Aesthetic Emphasis. Our analysis has uncovered four patterns of system design and points to unexplored regions of the design space, which may motivate future work in the field.

Do algorithm animations assist learning?: an empirical study and analysis

Algorithm animations are dynamic graphical illustrations of computer algorithms, and they are used as teaching aids to help explain how the algorithms work. Although many people believe that algorithm animations are useful this way, no empirical evidence has ever been presented supporting this belief. We have conducted an empirical study of a priority queue algorithm animation, and the studys results indicate that the animation only slightly assisted student understanding. In this article, we analyze those results and hypothesize why algorithm animations may not be as helpful as was initially hoped. We also develop guidelines for making algorithm animations more useful in the future.

An evaluation of space-filling information visualizations for depicting hierarchical structures

A variety of information visualization tools have been developed recently, but relatively little effort has been made to evaluate the effectiveness and utility of the tools. This article describes results from two empirical studies of two visualization tools for depicting hierarchies, in particular, computer file and directory structures. The two tools examined implement space-filling methodologies, one rectangular, the Treemap method, and one circular, the Sunburst method. Participants performed typical file/directory search and analysis tasks using the two tools. In general, performance trends favored the Sunburst tool with respect to correct task performance, particularly on initial use. Performance with Treemap tended to improve over time and use, suggesting a greater learning cost that was partially recouped over time. Each tool afforded somewhat different search strategies, which also appeared to influence performance. Finally, participants strongly preferred the Sunburst tool, citing better ability to convey structure and hierarchy.