Embedding Spatial Software Visualization in the IDE: an Exploratory Study

Software visualization can be of great use for understanding and exploring a software system in an intuitive manner. Spatial representation of software is a promising approach of increasing interest. However, little is known about how developers interact with spatial visualizations that are embedded in the IDE. In this paper, we present a pilot study that explores the use of Software Cartography for program comprehension of an unknown system. We investigated whether developers establish a spatial memory of the system, whether clustering by topic offers a sound base layout, and how developers interact with maps. We report our results in the form of observations, hypotheses, and implications. Key findings are a) that developers made good use of the map to inspect search results and call graphs, and b) that developers found the base layout surprising and often confusing. We conclude with concrete advice for the design of embedded software maps.Comment: To appear in proceedings of SOFTVIS 2010 conferenc

Topology, homogeneity and scale factors for object detection: application of eCognition software for urban mapping using multispectral satellite image

The research scope of this paper is to apply spatial object based image analysis (OBIA) method for processing panchromatic multispectral image covering study area of Brussels for urban mapping. The aim is to map different land cover types and more specifically, built-up areas from the very high resolution (VHR) satellite image using OBIA approach. A case study covers urban landscapes in the eastern areas of the city of Brussels, Belgium. Technically, this research was performed in eCognition raster processing software demonstrating excellent results of image segmentation and classification. The tools embedded in eCognition enabled to perform image segmentation and objects classification processes in a semi-automated regime, which is useful for the city planning, spatial analysis and urban growth analysis. The combination of the OBIA method together with technical tools of the eCognition demonstrated applicability of this method for urban mapping in densely populated areas, e.g. in megapolis and capital cities. The methodology included multiresolution segmentation and classification of the created objects.Comment: 6 pages, 12 figures, INSO2015, Ed. by A. Girgvliani et al. Akaki Tsereteli State University, Kutaisi (Imereti), Georgi

Topic Similarity Networks: Visual Analytics for Large Document Sets

We investigate ways in which to improve the interpretability of LDA topic models by better analyzing and visualizing their outputs. We focus on examining what we refer to as topic similarity networks: graphs in which nodes represent latent topics in text collections and links represent similarity among topics. We describe efficient and effective approaches to both building and labeling such networks. Visualizations of topic models based on these networks are shown to be a powerful means of exploring, characterizing, and summarizing large collections of unstructured text documents. They help to "tease out" non-obvious connections among different sets of documents and provide insights into how topics form larger themes. We demonstrate the efficacy and practicality of these approaches through two case studies: 1) NSF grants for basic research spanning a 14 year period and 2) the entire English portion of Wikipedia.Comment: 9 pages; 2014 IEEE International Conference on Big Data (IEEE BigData 2014

Automated Software Architecture Extraction Using Graph-based Clustering

As the size and complexity of software grows developers have an ever-increasing need to understand software in a modular way. Most complex software systems can be divided into smaller modules if the developer has domain knowledge of the code or up-to-date documentation. If neither of these exist discovery of code modules can be a tedious, manual process. This research hypothesizes that graph-based clustering can be used effectively for automated software architecture extraction. We propose methods of representing relationships between program artifacts as graphs and then propose new partitional algorithms to extract software modules from those graphs. To validate our hypothesis and the partitional algorithms a new set of tools, including a software data miner, cluster builder, graph viewer, and cluster score calculator, were created. This toolset was used to implement partitional algorithms and analyze their performance in extracting modules. The Xinu operating system was used as a case study because it has defined modules that can be compared to the results of the partitional algorithm

Dynamic Interactions for Network Visualization and Simulation

Most network visualization suites do not interact with a simulator, as it executes. Nor do they provide an effective user interface that includes multiple visualization functions. The subject of this research is to improve the network visualization presented in the previous research [5] adding these capabilities to the framework. The previous network visualization did not have the capability of altering specific visualization characteristics, especially when detailed observations needed to be made for a small part of a large network. Searching for a network event in this topology might cause large delays leading to lower quality user interface. In addition to shortfalls in handling complex network events, [5] did not provide dynamic user interactions since it did not have real-time interaction with a simulator. These shortfalls motivate the development of a new network visualization framework design that provides a more robust user interface, network observation tools and an interaction with the simulator. Our research presents the design, development and implementation of this new network visualization framework to enhance network scenarios and provide interaction with NS-2, as it executes. From the interface design perspective, this research presents a prototype design to ease the implementation process of the framework. The visualization functions such as clustering, filtering, labeling and color coding help accessing network objects and events, supporting four tabs consisting of buttons, menus, and sliders. The new network visualization framework design gives the ability to handle the inherent complexity of large networks, allowing the user to interact with the current display of the framework, alter visualization parameters and control the network through the visualization. In our application, multiple visualizations are linked to NS-2 to build execution scenarios which let to test clustering, filtering, labeling functionalities on separate visualization screens, as NS-2 progresses