Project comprehension questions, such as “which modified artifacts can affect my work?” and “how can I identify the developers who should be assigned to a given task?” are difficult to answer, require an analysis of the project and its data, are context specific, and cannot always be pre-defined. Current research approaches are restricted to post hoc analyses over software repositories. Very few interactive exploratory tools exist since the large amount of data that need to be analyzed prohibits its exploration at interactive rates. Moreover, such analyses typically require the user to create complex scripts or queries to extract the desired information from data. Here we present Dominoes, a tool for interactive data exploration aimed at end users (i.e., project managers or developers). Dominoes allows users to interact with different types and units of data to investigate project relationships and view intermediate results as charts, tables, and graphs. Additionally, it allows users to save the derived data as well as their exploration paths for later use. In a scenario-based evaluation study, participants achieved a success rate of 86 percent in their explorations, with a mean time of 7.25 minutes for answering a set of (project) exploration questions.

Version control is considered to be a vital component for supporting professional software development. While it has been widely used for textual artifacts, such as source code or documentation, little attention has been given to binary artifacts. This omission can place huge restrictions on projects in the game and media industries as they contain large amounts of binary data, such as images, videos, three-dimensional models, and animations, along with their source code. For these kinds of artifacts, existing strategies such as storing the file as a whole for each revision or saving conventional binary deltas consume significant storage space with duplicate data and, even worse, do not provide any understandable information on which modifications were made. As a response to this problem, this paper introduces a change-set model infrastructure to support version control of image artifacts using a specialized data structure. Additionally, our approach can deal with the maintenance of duplicate nearly identical images through a merge operation. Because of the amount of data that has to be processed, we designed our solution based on a parallel architecture, which permits a massively parallel approach to version control. The paper also compares our approach with some popular open-source version control systems, showing their repository growth in relation to ours as well as the time required to process image artifacts. Finally, we demonstrate that our architecture requires less storage space and runs much faster than current methods. Copyright © 2015 John Wiley & Sons, Ltd.

Identifying expertise in a project is essential for task allocation, knowledge dissemination, and risk management, among other activities. However, keeping a detailed record of such expertise at class and method levels is cumbersome due to project size, evolution, and team turnover. Existing approaches that automate this task have limitations in terms of the number and granularity of elements that can be analyzed and the analysis timeframe. In this paper, we introduce a novel technique to identify expertise for a given project, package, file, class, or method by considering not only the total number of edits that a developer has made, but also the spread of their changes in an artifact over time, and thereby the breadth of their expertise. We use Dominoes - our GPU-based approach for exploratory repository analysis - for expertise identification over any given granularity and time period with a short processing time. We evaluated our approach through Apache Derby and observed that granularity and time can have significant influence on expertise identification.

In this paper, we present Dominoes, an approach for analyzing software repositories with thousands of artifacts by considering multiple perspectives of the software development data. In order to achieve computational power we model the data and its relationships as matrices, making possible to efficiently process them with a GPUs (Graphics Processing Unit) based architectures. Dominoes can support automated exploration of different relationships among project artifacts, where users have the flexibility to interactively combine and compose them. Our solution organizes data extracted from software repositories into multiple matrices that can be treated as domino pieces (e.g. [commit|method]). The connection of such pieces corresponds to a set of matrices operations, which derive additional domino pieces. These derived domino pieces represent specific project entity relationships (e.g. number of commits in which two methods co-occurred) and can be used for further explorations. As an evaluation of the Dominoes framework we present two exploratory case studies based on Apache Derby. First, we use Dominoes to show how dependencies among artifacts can be derived. Then, we identify expertise of developers by considering the commits that developers make to artifacts. We show that identifying relationships among 34,335 elements along 7,578 commits takes about 0.2 minutes in GPU, while the same processing in CPU takes about 413 minutes. Besides, identifying expertise of developer on a set of 34,335 files and 36 developers takes about 0.1 minute in GPU, whereas in CPU it takes 324 minutes.

Fluid simulation is very important for the study of natural phenomena. Most of these phenomena could not be simulated computationally some years ago and for its study the research only had available the manual calculation of its govern equation or a small physical simulation of the phenomena to be studied. With the technology advance, most of these phenomena could be simulate even in real time coming out a new research field called Computational Fluid Dynamic (DFC) only to deal with it. Many researches in DFC use Graphics Processor Unity (GPU) to simulate fluids, forgiving the existence of the CPU, which became idle most time during the simulation. Due the presented fact, we propose a method for using GPU and CPU together during the fluid simulation. Fluid simulation in this work is based on Smoothed Particle Hydrodynamics (SPH), a mesh free Newtonian method for fluid simulation. Also, the rigid body and fluid interaction is dealt at triangle level, archiving more precise results.