Software map

Abstract: This research applies Lotka's Law to metadata on open source software development. Lotka's Law predicts the proportion of authors at different levels of productivity. Open source software development harnesses the creativity of thousands of programmers worldwide, is important to the progress of the Internet and many other computing environments, and yet has not been widely researched. We examine metadata from the Linux Software Map (LSM), which documents many open source projects, and Sourceforge, one of the largest resources for open source developers. Authoring patterns found are comparable to prior studies of Lotka's Law for scientific and scholarly publishing. Lotka's Law was found to be effective in understanding software development productivity patterns, and offer promise in predicting aggregate behavior of open source developers.

Abstract: A software program can be represented as a map depicting respective software components of the software program. A grid system can be used, and map elements can be contiguous geometric shapes. Affinity between the software components can be represented in the map, such as by placing corresponding map elements close to one other in the map. During navigation through source code, an indication of where within the source code the navigation has occurred can be displayed on the map. Paths can be shown on the map. A technique for building the map can rely on a metric of a software component when generating a corresponding map element in the software map. The map can be depicted as a geographic map to help developers locate components.

Abstract: Software maps provide a general-purpose interactive user interface and information display for software analytics tools. This paper systematically introduces and classifies software maps as a treemap-based technique for software cartography. It provides an overview of advanced visual metaphors and techniques, each suitable for interactive visual analytics tasks, that can be used to enhance the expressiveness of software maps. Thereto, the metaphors and techniques are briefly described, located within a visualization pipeline model, and considered within the software map design space. Consequent applications and use cases w.r.t. different types of software system data and software engineering data are discussed, arguing for a versatile use of software maps in visual software analytics.

Abstract: Tool support is essential to help developers in understanding the memory behavior of complex software systems. Anomalies such as memory leaks can dramatically impact application performance and can even lead to crashes. Unfortunately, most memory analysis tools lack advanced visualizations (especially of the memory evolution over time) that could facilitate developers in analyzing suspicious memory behavior.In this paper, we present Memory Cities, a technique to visualize an application’s heap memory evolution over time using the software city metaphor. While this metaphor is typically used to visualize static artifacts of a software system such as class hierarchies, we use it to visualize the dynamic memory behavior of an application. In our approach, heap objects can be grouped by multiple properties such as their types or their allocation sites. The resulting object groups are visualized as buildings arranged in districts, where the size of a building corresponds to the number of heap objects or bytes it represents. Continuously updating the city over time creates the immersive feeling of an evolving city. This can be used to detect and analyze memory leaks, i.e., to search for suspicious growth behavior. Memory cities further utilize various visual attributes to ease this task. For example, they highlight strongly growing buildings using color, while making less suspicious buildings semi-transparent.We implemented memory cities as a standalone application developed in Unity, with a JSON-based interface to ensure easy data import from external tools. We show how memory cites can use data provided by AntTracks, a trace-based memory monitoring tool, and present case studies on different applications to demonstrate the tool’s applicability and feasibility.