13,360 research outputs found
Interactive manipulation of contour data using the layers program
Journal ArticleThe "layers" program is useful for visualizing and editing large sets of contour data. These datasets arise frequently when trying to extract geometry from MRI slices. Due to the imprecise nature of the MR imaging and segmentation processes, the contours extracted may not accurately reflect the human geometry. The "layers" program has been designed to allow the efficient editing of these contours in order to provide human control over the contour generation process
Surface Projection Method for Visualizing Volumetric Data
The goal of this project was to explore, develop, and implement additional visualization methods for volumetric data within MindSeer. This paper discusses the implementation of one such visualization method, the surface projection method, and compares it to other existing methods
The Impact of Systematic Edits in History Slicing
While extracting a subset of a commit history, specifying the necessary
portion is a time-consuming task for developers. Several commit-based history
slicing techniques have been proposed to identify dependencies between commits
and to extract a related set of commits using a specific commit as a slicing
criterion. However, the resulting subset of commits become large if commits for
systematic edits whose changes do not depend on each other exist. We
empirically investigated the impact of systematic edits on history slicing. In
this study, commits in which systematic edits were detected are split between
each file so that unnecessary dependencies between commits are eliminated. In
several histories of open source systems, the size of history slices was
reduced by 13.3-57.2% on average after splitting the commits for systematic
edits.Comment: 5 pages, MSR 201
A document-like software visualization method for effective cognition of c-based software systems
It is clear that maintenance is a crucial and very costly process in a software life cycle. Nowadays there are a lot of software systems particularly legacy systems that are always maintained from time to time as new requirements arise. One important source to understand a software system before it is being maintained is through the documentation, particularly system documentation. Unfortunately, not all software systems developed or maintained are accompanied with their reliable and updated documents. In this case, source codes will be the only reliable source for programmers. A number of studies have been carried out in order to assist cognition based on source codes. One way is through tool automation via reverse engineering technique in which source codes will be parsed and the information extracted will be visualized using certain visualization methods. Most software visualization methods use graph as the main element to represent extracted software artifacts. Nevertheless, current methods tend to produce more complicated graphs and do not grant an explicit, document-like re-documentation environment. Hence, this thesis proposes a document-like software visualization method called DocLike Modularized Graph (DMG). The method is realized in a prototype tool named DocLike Viewer that targets on C-based software systems. The main contribution of the DMG method is to provide an explicit structural re-document mechanism in the software visualization tool. Besides, the DMG method provides more level of information abstractions via less complex graph that include inter-module dependencies, inter-program dependencies, procedural abstraction and also parameter passing. The DMG method was empirically evaluated based on the Goal/Question/Metric (GQM) paradigm and the findings depict that the method can improve productivity and quality in the aspect of cognition or program comprehension. A usability study was also conducted and DocLike Viewer had the most positive responses from the software practitioners
Studying the Emerging Global Brain: Analyzing and Visualizing the Impact of Co-Authorship Teams
This paper introduces a suite of approaches and measures to study the impact
of co-authorship teams based on the number of publications and their citations
on a local and global scale. In particular, we present a novel weighted graph
representation that encodes coupled author-paper networks as a weighted
co-authorship graph. This weighted graph representation is applied to a dataset
that captures the emergence of a new field of science and comprises 614 papers
published by 1,036 unique authors between 1974 and 2004. In order to
characterize the properties and evolution of this field we first use four
different measures of centrality to identify the impact of authors. A global
statistical analysis is performed to characterize the distribution of paper
production and paper citations and its correlation with the co-authorship team
size. The size of co-authorship clusters over time is examined. Finally, a
novel local, author-centered measure based on entropy is applied to determine
the global evolution of the field and the identification of the contribution of
a single author's impact across all of its co-authorship relations. A
visualization of the growth of the weighted co-author network and the results
obtained from the statistical analysis indicate a drift towards a more
cooperative, global collaboration process as the main drive in the production
of scientific knowledge.Comment: 13 pages, 9 figure
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