Code Flows: Visualizing Structural Evolution of Source Code

Understanding detailed changes done to source code is of great importance in software maintenance. We present Code Flows, a method to visualize the evolution of source code geared to the understanding of fine and mid-level scale changes across several file versions. We enhance an existing visual metaphor to depict software structure changes with techniques that emphasize both following unchanged code as well as detecting and highlighting important events such as code drift, splits, merges, insertions and deletions. The method is illustrated with the analysis of a real-world C++ code system.

Code Flows: Visualizing Structural Evolution of Source Code

Understanding detailed changes done to source code is of great importance in software maintenance. We present Code Flows, a method to visualize the evolution of source code geared to the understanding of fine and mid-level scale changes across several file versions. We enhance an existing visual metaphor to depict software structure changes with techniques that emphasize both following unchanged code as well as detecting and highlighting important events such as code drift, splits, merges, insertions and deletions. The method is illustrated with the analysis of a real-world C++ code system.

Traumatic Brain Injury

Magnetic Resonance (MR) imaging of the brain following head injury is used in two distinct clinical contexts, (1) acutely, within days of the injury, to evaluate an unexplained neurologic deficit or to obtain prognostic information, and (2) chronically, to assess the degree of brain injury and explain neurologic or neuropsychologic findings. In this unit, two basic protocols are presented, one for acute imaging and the other for chronic imaging. Advanced MR imaging sequences, such as MR spectroscopy (MRS) and diffusion‐weighted (DW) imaging can provide additional prognostic information in the acute setting and are also described. MR angiography (MRA) and direct vessel wall imaging techniques are mentioned briefly.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145414/1/cpmia0404.pd

Infectious Diseases of the Brain

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145416/1/cpmia0400.pd

Visually Mining the Datacube using a Pixel-Oriented Technique

International audienceThis paper introduces a new technique easing the navigation and interactive exploration of huge multidimensional datasets. Following the pixel-oriented paradigm, the key ingredients enabling the interactive navigation of extreme volumes of data rely on a set of functions bijectively mapping data elements to screen pixels. The use of the mapping from data elements to pixels constrain the computational complexity for the rendering process to be linear with respect to the number of rendered pixels on the screen as opposed to the dataset size. Our method furthermore allows the implementation of usual information visualization techniques such as zoom and pan, anamorphosis and texturing. As a proof-of-concept, we show how our technique can be adapted to interactively explore the Datacube, turning our approach into an efficient system for visual datamining. We report experiments conducted on a Datacube containing 50 millions of items. To our knowledge, our technique outperforms all existing ones and push the scalability limit close to the billion of elements. Supporting all basic navigation techniques, and being moreover flexible makes it easily reusable for a large number of applications

Visually Mining the Datacube using a Pixel-Oriented Technique

International audienceThis paper introduces a new technique easing the navigation and interactive exploration of huge multidimensional datasets. Following the pixel-oriented paradigm, the key ingredients enabling the interactive navigation of extreme volumes of data rely on a set of functions bijectively mapping data elements to screen pixels. The use of the mapping from data elements to pixels constrain the computational complexity for the rendering process to be linear with respect to the number of rendered pixels on the screen as opposed to the dataset size. Our method furthermore allows the implementation of usual information visualization techniques such as zoom and pan, anamorphosis and texturing. As a proof-of-concept, we show how our technique can be adapted to interactively explore the Datacube, turning our approach into an efficient system for visual datamining. We report experiments conducted on a Datacube containing 50 millions of items. To our knowledge, our technique outperforms all existing ones and push the scalability limit close to the billion of elements. Supporting all basic navigation techniques, and being moreover flexible makes it easily reusable for a large number of applications