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
2623510711
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.