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.
DeLine proposed four desiderata that should be satisfied by spatial software navigation @cite_11 . In the same work are presented, which satisfy the properties #1 and #4 ( continuous space and global overlays).
@cite_11
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In this paper, we propose a novel appearance-based approach for topological mapping based on a hierarchical decomposition of the environment. In our map, images with similar visual properties are grouped together in nodes, which are represented by means of an average global descriptor and an index of binary features based on a bag-of-words online approach. Each image is represented by means of a global descriptor and a set of local features, and this information is used in a two-level loop closure approach, where first global descriptors are employed to obtain the most likely nodes of the map and then binary image features are used to retrieve the most likely images inside these nodes. This hierarchical scheme enables us to reduce the search space when recognizing places, maintaining high accuracy when creating a map. Our approach is validated using several public datasets and compared against several state-of-the-art techniques. The accuracy and the sparsity of the generated maps are also discussed. This paper presents work in progress on a new technique for visu- alising and manipulating large hierarchies. The information slices approach compactly visualises hierarchical structures using a series of semi-circular discs. The technique is described in the context of our early experience with a prototype file system visualiser based on information slices. Over the last few years, the emerging field of information visuali- sation has resulted in a numerous techniques for helping visualise and make sense of large information spaces (10). Among these are several techniques for visualising and interacting with large hierar- chies which go beyond the traditional approach taken in 2d scrolling browsers such as the Windows Explorer. Treemaps (6, 9) are space-filling visualisations of hierarchies based on successive horizontal and vertical subdivision of screen rectangles. The area of each rectangle is proportional to some at- tribute of the underlying hierarchy such as the (total) size of each subtree. Xdu (4) is a utility for the X window system which displays a graphical disk usage for Unix file systems. Rectangles are stacked from left to right as the directory tree is descended. The current di- rectory is represented by the leftmost rectangle, which is the entire height of the window. Subdirectories are represented by neighbour- ing rectangles in the next column, whose height are proportional to the size of each subdirectory. The hyperbolic browser (7) uses a focus and context technique based on hyperbolic geometry. A hierarchy is laid out uniformly on the hyperbolic plane and then mapped to the unit disc for display on screen. Nodes in the centre of the disc are largest and nodes are assigned progressively less space towards the perimeter of the disc. Cheops (3) is based on multiple re-use of overlaid triangles in the display. Working top-down, the selection of a node (triangle) at a particular level designates that node's children are to be represented Traditional approaches to simultaneous localization and mapping (SLAM) rely on low-level geometric features such as points, lines, and planes. They are unable to assign semantic labels to landmarks observed in the environment. Furthermore, loop closure recognition based on low-level features is often viewpoint-dependent and subject to failure in ambiguous or repetitive environments. On the other hand, object recognition methods can infer landmark classes and scales, resulting in a small set of easily recognizable landmarks, ideal for view-independent unambiguous loop closure. In a map with several objects of the same class, however, a crucial data association problem exists. While data association and recognition are discrete problems usually solved using discrete inference, classical SLAM is a continuous optimization over metric information. In this paper, we formulate an optimization problem over sensor states and semantic landmark positions that integrates metric information, semantic information, and data associations, and decompose it into two interconnected problems: an estimation of discrete data association and landmark class probabilities, and a continuous optimization over the metric states. The estimated landmark and robot poses affect the association and class distributions, which in turn affect the robot-landmark pose optimization. The performance of our algorithm is demonstrated on indoor and outdoor datasets. Developers often find themselves lost as they navigate around large programs, particularly when those programs are unfamiliar. This paper presents a new visualization, called a software terrain map, intended to keep a programmer oriented as she navigates around source code in the editor. The design is based on the metaphor of cartographic maps, which are continuous (no wasted space), have enough visual landmarks to allow the user to find her location perceptually rather than cognitively, and lend themselves to overlaying data. Although an optimal layout for software terrain maps is computationally intractable, the paper presents an efficient, heuristic algorithm that produces good