Software is intangible and knowledge about software systems is typically tacit. The mental model of software developers is thus an important factor in software engineering. It is our vision that developers should be able to refer to code as being "up in the north", "over in the west", or "down-under in the south". We want to provide developers, and everyone else involved in software development, with a *shared*, spatial and stable mental model of their software project. We aim to reinforce this by embedding a cartographic visualization in the IDE (Integrated Development Environment). The visualization is always visible in the bottom-left, similar to the GPS navigation device for car drivers. For each development task, related information is displayed on the map. In this paper we present CODEMAP, an eclipse plug-in, and report on preliminary results from an ongoing user study with professional developers and students.
In the software visualization literature however, topic maps are rarely used. Except for the use of graph splatting in RE Toolkit by Telea al @cite_10 , we are unaware of their prior application in software visualization.
@cite_10
2030016008 2008388219 137863291 2087382273
Information visualization has been acknowledged as an important tool in software decision support. But usually visualizations are static and just used for presentation rather than exploration. Interactive statistical data visualization can be used as a powerful tool which reaches beyond the limits of static graphs. We developed code mapping, a software platform for visualization and manipulation of complex and large software portfolios. Code mapping builds data-rich, graphical representations that are color-coded for function, call position and experimental interaction data. The functions implemented in code mapping allow collaboration and organization of software architecture diagrams in a visual model format; this feature is especially useful for Web services development and other Internet application code. The aim of this paper is to describe a tool which helps to visualize the software development and to present a new way to visualize software in a three-dimensional CAVE spl reg environment. Science mapping is discussed in the general context of information visualization. Attempts to construct maps of science using citation data are reviewed, focusing on the use of co-citation clusters. New work is reported on a dataset of about 36,000 documents using simplified methods for ordination, and nesting maps hierarchically. An overall map of the dataset shows the multidisciplinary breadth of the document sample, and submaps allow drilling down to the document level. An effort to visualize these data using advanced virtual reality software is described, and the creation of document pathways through the map is seen as a realization of Bush's (1945) associative trails. Many real-world domains can be represented as large node-link graphs: backbone Internet routers connect with 70,000 other hosts, mid-sized Web servers handle between 20,000 and 200,000 hyperlinked documents, and dictionaries contain millions of words defined in terms of each other. Computational manipulation of such large graphs is common, but previous tools for graph visualization have been limited to datasets of a few thousand nodes. Visual depictions of graphs and networks are external representations that exploit human visual processing to reduce the cognitive load of many tasks that require understanding of global or local structure. We assert that the two key advantages of computer-based systems for information visualization over traditional paper-based visual exposition are interactivity and scalability. We also argue that designing visualization software by taking the characteristics of a target user's task domain into account leads to systems that are more effective and scale to larger datasets than previous work. This thesis contains a detailed analysis of three specialized systems for the interactive exploration of large graphs, relating the intended tasks to the spatial layout and visual encoding choices. We present two novel algorithms for specialized layout and drawing that use quite different visual metaphors. The H3 system for visualizing the hyperlink structures of web sites scales to datasets of over 100,000 nodes by using a carefully chosen spanning tree as the layout backbone, 3D hyperbolic geometry for a Focus+Context view, and provides a fluid interactive experience through guaranteed frame rate drawing. The Constellation system features a highly specialized 2D layout intended to spatially encode domain-specific information for computational linguists checking the plausibility of a large semantic network created from dictionaries. The Planet Multicast system for displaying the tunnel topology of the Internet's multicast backbone provides a literal 3D geographic layout of arcs on a globe to help MBone maintainers find misconfigured long-distance tunnels. Each of these three systems provides a very different view of the graph structure, and we evaluate their efficacy for the intended task. We generalize these findings in our analysis of the importance of interactivity and specialization for graph visualization systems that are effective and scalable. Topic modeling has been widely used for analyzing text document collections. Recently, there have been significant advancements in various topic modeling techniques, particularly in the form of probabilistic graphical modeling. State-of-the-art techniques such as Latent Dirichlet Allocation (LDA) have been successfully applied in visual text analytics. However, most of the widely-used methods based on probabilistic modeling have drawbacks in terms of consistency from multiple runs and empirical convergence. Furthermore, due to the complicatedness in the formulation and the algorithm, LDA cannot easily incorporate various types of user feedback. To tackle this problem, we propose a reliable and flexible visual analytics system for topic modeling called UTOPIAN (User-driven Topic modeling based on Interactive Nonnegative Matrix Factorization). Centered around its semi-supervised formulation, UTOPIAN enables users to interact with the topic modeling method and steer the result in a user-driven manner. We demonstrate the capability of UTOPIAN via several usage scenarios with real-world document corpuses such as InfoVis VAST paper data set and product review data sets.