5,145 research outputs found
Software Reuse in Cardiology Related Medical Database Using K-Means Clustering Technique
Software technology based on reuse is identified as a process of designing
software for the reuse purpose. The software reuse is a process in which the
existing software is used to build new software. A metric is a quantitative
indicator of an attribute of an item or thing. Reusability is the likelihood
for a segment of source code that can be used again to add new functionalities
with slight or no modification. A lot of research has been projected using
reusability in reducing code, domain, requirements, design etc., but very
little work is reported using software reuse in medical domain. An attempt is
made to bridge the gap in this direction, using the concepts of clustering and
classifying the data based on the distance measures. In this paper cardiologic
database is considered for study. The developed model will be useful for
Doctors or Paramedics to find out the patients level in the cardiologic
disease, deduce the medicines required in seconds and propose them to the
patient. In order to measure the reusability K means clustering algorithm is
used.Comment: 5 pages. arXiv admin note: text overlap with arXiv:1212.031
Knowledge-based reusable software synthesis system
The Eli system, a knowledge-based reusable software synthesis system, is being developed for NASA Langley under a Phase 2 SBIR contract. Named after Eli Whitney, the inventor of interchangeable parts, Eli assists engineers of large-scale software systems in reusing components while they are composing their software specifications or designs. Eli will identify reuse potential, search for components, select component variants, and synthesize components into the developer's specifications. The Eli project began as a Phase 1 SBIR to define a reusable software synthesis methodology that integrates reusabilityinto the top-down development process and to develop an approach for an expert system to promote and accomplish reuse. The objectives of the Eli Phase 2 work are to integrate advanced technologies to automate the development of reusable components within the context of large system developments, to integrate with user development methodologies without significant changes in method or learning of special languages, and to make reuse the easiest operation to perform. Eli will try to address a number of reuse problems including developing software with reusable components, managing reusable components, identifying reusable components, and transitioning reuse technology. Eli is both a library facility for classifying, storing, and retrieving reusable components and a design environment that emphasizes, encourages, and supports reuse
From a Competition for Self-Driving Miniature Cars to a Standardized Experimental Platform: Concept, Models, Architecture, and Evaluation
Context: Competitions for self-driving cars facilitated the development and
research in the domain of autonomous vehicles towards potential solutions for
the future mobility.
Objective: Miniature vehicles can bridge the gap between simulation-based
evaluations of algorithms relying on simplified models, and those
time-consuming vehicle tests on real-scale proving grounds.
Method: This article combines findings from a systematic literature review,
an in-depth analysis of results and technical concepts from contestants in a
competition for self-driving miniature cars, and experiences of participating
in the 2013 competition for self-driving cars.
Results: A simulation-based development platform for real-scale vehicles has
been adapted to support the development of a self-driving miniature car.
Furthermore, a standardized platform was designed and realized to enable
research and experiments in the context of future mobility solutions.
Conclusion: A clear separation between algorithm conceptualization and
validation in a model-based simulation environment enabled efficient and
riskless experiments and validation. The design of a reusable, low-cost, and
energy-efficient hardware architecture utilizing a standardized
software/hardware interface enables experiments, which would otherwise require
resources like a large real-scale test track.Comment: 17 pages, 19 figues, 2 table
A neural net-based approach to software metrics
Software metrics provide an effective method for characterizing software. Metrics have traditionally been composed through the definition of an equation. This approach is limited by the fact that all the interrelationships among all the parameters be fully understood. This paper explores an alternative, neural network approach to modeling metrics. Experiments performed on two widely accepted metrics, McCabe and Halstead, indicate that the approach is sound, thus serving as the groundwork for further exploration into the analysis and design of software metrics
SOFTWARE REUSE: ISSUES AND RESEARCH DIRECTIONS
Software reuse has been considered as a means to help solve the
software development crisis. This paper surveys recent work based on
the broad framework of software reusability research, and suggests
directions for future research. We address general, technical, and non-technical
issues of software reuse, and conclude that reuse needs to be
viewed in the context of a total systems approach. We also envision a
software system or reuse support system(RSS) that helps document and
elucidate existing application systems so that the ideas and design
decisions involved in their creation can be reused either in the
context of maintenance or when building new systems.Information Systems Working Papers Serie
Using Neural Networks In Software Metrics
Software metrics provide effective methods for characterizing software. Metrics have traditionally been composed through the definition of an equation, but this approach is limited by the fact that all the interrelationships among all the parameters be fully understood. Derivation of a polynomial providing the desired characteristics is a substantial challenge.
In this paper instead of using conventional methods for obtaining software metrics, we will try to use a neural network for that purpose. Experiments performed in the past on two widely
known metrics, McCabe and Halstead, indicate that this approach is feasible.neural networks, software metrics, halstead, mccabe
A Model-Driven Engineering Approach for ROS using Ontological Semantics
This paper presents a novel ontology-driven software engineering approach for
the development of industrial robotics control software. It introduces the
ReApp architecture that synthesizes model-driven engineering with semantic
technologies to facilitate the development and reuse of ROS-based components
and applications. In ReApp, we show how different ontological classification
systems for hardware, software, and capabilities help developers in discovering
suitable software components for their tasks and in applying them correctly.
The proposed model-driven tooling enables developers to work at higher
abstraction levels and fosters automatic code generation. It is underpinned by
ontologies to minimize discontinuities in the development workflow, with an
integrated development environment presenting a seamless interface to the user.
First results show the viability and synergy of the selected approach when
searching for or developing software with reuse in mind.Comment: Presented at DSLRob 2015 (arXiv:1601.00877), Stefan Zander, Georg
Heppner, Georg Neugschwandtner, Ramez Awad, Marc Essinger and Nadia Ahmed: A
Model-Driven Engineering Approach for ROS using Ontological Semantic
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