Software development: A paradigm for the future

A new paradigm for software development that treats software development as an experimental activity is presented. It provides built-in mechanisms for learning how to develop software better and reusing previous experience in the forms of knowledge, processes, and products. It uses models and measures to aid in the tasks of characterization, evaluation and motivation. An organization scheme is proposed for separating the project-specific focus from the organization's learning and reuse focuses of software development. The implications of this approach for corporations, research and education are discussed and some research activities currently underway at the University of Maryland that support this approach are presented

An evaluation framework to drive future evolution of a research prototype

The Open Source Component Artefact Repository (OSCAR) requires evaluation to confirm its suitability as a development environment for distributed software engineers. The evaluation will take note of several factors including usability of OSCAR as a stand-alone system, scalability and maintainability of the system and novel features not provided by existing artefact management systems. Additionally, the evaluation design attempts to address some of the omissions (due to time constraints) from the industrial partner evaluations. This evaluation is intended to be a prelude to the evaluation of the awareness support being added to OSCAR; thus establishing a baseline to which the effects of awareness support may be compared

Project management for cloud computing development

This article deals with the impact of employing cloud computing architectures in the field of software systems development. We analyze the individual influence of the cloud computing model characteristics on the project development process.

Measuring the impact of computer resource quality on the software development process and product

The availability and quality of computer resources during the software development process was speculated to have measurable, significant impact on the efficiency of the development process and the quality of the resulting product. Environment components such as the types of tools, machine responsiveness, and quantity of direct access storage may play a major role in the effort to produce the product and in its subsequent quality as measured by factors such as reliability and ease of maintenance. During the past six years, the NASA Goddard Space Flight Center has conducted experiments with software projects in an attempt to better understand the impact of software development methodologies, environments, and general technologies on the software process and product. Data was extracted and examined from nearly 50 software development projects. All were related to support of satellite flight dynamics ground-based computations. The relationship between computer resources and the software development process and product as exemplified by the subject NASA data was examined. Based upon the results, a number of computer resource-related implications are provided

Improving Software Performance in the Compute Unified Device Architecture

This paper analyzes several aspects regarding the improvement of software performance for applications written in the Compute Unified Device Architecture CUDA). We address an issue of great importance when programming a CUDA application: the Graphics Processing Unit’s (GPU’s) memory management through ranspose ernels. We also benchmark and evaluate the performance for progressively optimizing a transposing matrix application in CUDA. One particular interest was to research how well the optimization techniques, applied to software application written in CUDA, scale to the latest generation of general-purpose graphic processors units (GPGPU), like the Fermi architecture implemented in the GTX480 and the previous architecture implemented in GTX280. Lately, there has been a lot of interest in the literature for this type of optimization analysis, but none of the works so far (to our best knowledge) tried to validate if the optimizations can apply to a GPU from the latest Fermi architecture and how well does the Fermi architecture scale to these software performance improving techniques.Compute Unified Device Architecture, Fermi Architecture, Naive Transpose, Coalesced Transpose, Shared Memory Copy, Loop in Kernel, Loop over Kernel