Software visualizations can provide a concise overview of a complex software system. Unfortunately, since software has no physical shape, there is no natural'' mapping of software to a two-dimensional space. As a consequence most visualizations tend to use a layout in which position and distance have no meaning, and consequently layout typical diverges from one visualization to another. We propose a consistent layout for software maps in which the position of a software artifact reflects its vocabulary, and distance corresponds to similarity of vocabulary. We use latent semantic indexing (LSI) to map software artifacts to a vector space, and then use multidimensional scaling (MDS) to map this vector space down to two dimensions.The resulting consistent layout allows us to develop a variety of thematic software maps that express very different aspects of software while making it easy to compare them. The approach is especially suitable for comparing views of evolving software, since the vocabulary of software artifacts tends to be stable over time.
MetricView is an exploratory environment featuring UML diagram visualizations @cite_9 . The third dimension is used to extend UML with polymetric views @cite_14 . The diagrams use arbitrary layout, so do not reflect meaningful distance or position.
@cite_9 @cite_14
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We present MetricView, a software visualization and exploration tool that combines traditional UML diagram visualization with metric visualization in an effective way. MetricView is very easy and natural to use for software architects and developers yet offers a powerful set of mechanisms that allow fine customization of the visualizations for getting specific insights. We discuss several visual and architectural design choices which turned out to be important in the construction of MetricView, and illustrate our approach with several results using real-life datasets In this paper we present a hierarchy-based visualization approach for software metrics using Treemaps. Contrary to existing rectangle-based Treemap layout algorithms, we introduce layouts based on arbitrary polygons that are advantageous with respect to the aspect ratio between width and height of the objects and the identification of boundaries between and within the hierarchy levels in the Treemap. The layouts are computed by the iterative relaxation of Voronoi tessellations. Additionally, we describe techniques that allow the user to investigate software metric data of complex systems by utilizing transparencies in combination with interactive zooming. Metric graphs are special types of metric spaces used to model and represent simple, ubiquitous, geometric relations in data such as biological networks, social networks, and road networks. We are interested in giving a qualitative description of metric graphs using topological summaries. In particular, we provide a complete characterization of the one-dimensional intrinsic Cech persistence diagrams for finite metric graphs using persistent homology. Together with complementary results by , which imply the results on intrinsic Cech persistence diagrams in all dimensions for a single cycle, our results constitute the important steps toward characterizing intrinsic Cech persistence diagrams for arbitrary finite metric graphs across all dimensions. Metric learning aims at finding a distance that approximates a task-specific notion of semantic similarity. Typically, a Mahalanobis distance is learned from pairs of data labeled as being semantically similar or not. In this paper, we learn such metrics in a weakly supervised setting where "bags" of instances are labeled with "bags" of labels. We formulate the problem as a multiple instance learning (MIL) problem over pairs of bags. If two bags share at least one label, we label the pair positive, and negative otherwise. We propose to learn a metric using those labeled pairs of bags, leading to MildML, for multiple instance logistic discriminant metric learning. MildML iterates between updates of the metric and selection of putative positive pairs of examples from positive pairs of bags. To evaluate our approach, we introduce a large and challenging data set, Labeled Yahoo! News, which we have manually annotated and contains 31147 detected faces of 5873 different people in 20071 images. We group the faces detected in an image into a bag, and group the names detected in the caption into a corresponding set of labels. When the labels come from manual annotation, we find that MildML using the bag-level annotation performs as well as fully supervised metric learning using instance-level annotation. We also consider performance in the case of automatically extracted labels for the bags, where some of the bag labels do not correspond to any example in the bag. In this case MildML works substantially better than relying on noisy instance-level annotations derived from the bag-level annotation by resolving face-name associations in images with their captions.