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Cloud-assisted reliable user datagram protocol
Title
Cloud-assisted reliable user datagram protocol.
Creator
Dimitrov, Volen, Bullard, Lofton A.
Date Issued
2013-04-05
PURL
http://purl.flvc.org/fcla/dt/3361078
Subject Headings
Cloud computing, Internet, TCP/IP (Computer network protocol), File Transfer Protocol (Computer network protocol)
Format
Document (PDF)
Tree-based classification models for analyzing a very large software system
Title
Tree-based classification models for analyzing a very large software system.
Creator
Bullard, Lofton A., Florida Atlantic University, Khoshgoftaar, Taghi M.
Abstract/Description
Software systems that control military radar systems must be highly reliable. A fault can compromise safety and security, and even cause death of military personnel. In this experiment we identify fault-prone software modules in a subsystem of a military radar system called the Joint Surveillance Target Attack Radar System, JSTARS. An earlier version was used in Operation Desert Storm to monitor ground movement. Product metrics were collected for different iterations of an operational...
Show moreSoftware systems that control military radar systems must be highly reliable. A fault can compromise safety and security, and even cause death of military personnel. In this experiment we identify fault-prone software modules in a subsystem of a military radar system called the Joint Surveillance Target Attack Radar System, JSTARS. An earlier version was used in Operation Desert Storm to monitor ground movement. Product metrics were collected for different iterations of an operational prototype of the subsystem over a period of approximately three years. We used these metrics to train a decision tree model and to fit a discriminant model to classify each module as fault-prone or not fault-prone. The algorithm used to generate the decision tree model was TREEDISC, developed by the SAS Institute. The decision tree model is compared to the discriminant model.
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Date Issued
1996
PURL
http://purl.flvc.org/fcla/dt/15315
Subject Headings
Computer software--Quality control, Computer software--Reliability, Software engineering
Format
Document (PDF)
Rough Set-Based Software Quality Models and Quality of Data
Title
Rough Set-Based Software Quality Models and Quality of Data.
Creator
Bullard, Lofton A., Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
In this dissertation we address two significant issues of concern. These are software quality modeling and data quality assessment. Software quality can be measured by software reliability. Reliability is often measured in terms of the time between system failures. A failure is caused by a fault which is a defect in the executable software product. The time between system failures depends both on the presence and the usage pattern of the software. Finding faulty components in the development...
Show moreIn this dissertation we address two significant issues of concern. These are software quality modeling and data quality assessment. Software quality can be measured by software reliability. Reliability is often measured in terms of the time between system failures. A failure is caused by a fault which is a defect in the executable software product. The time between system failures depends both on the presence and the usage pattern of the software. Finding faulty components in the development cycle of a software system can lead to a more reliable final system and will reduce development and maintenance costs. The issue of software quality is investigated by proposing a new approach, rule-based classification model (RBCM) that uses rough set theory to generate decision rules to predict software quality. The new model minimizes over-fitting by balancing the Type I and Type II niisclassiflcation error rates. We also propose a model selection technique for rule-based models called rulebased model selection (RBMS). The proposed rule-based model selection technique utilizes the complete and partial matching rule sets of candidate RBCMs to determine the model with the least amount of over-fitting. In the experiments that were performed, the RBCMs were effective at identifying faulty software modules, and the RBMS technique was able to identify RBCMs that minimized over-fitting. Good data quality is a critical component for building effective software quality models. We address the significance of the quality of data on the classification performance of learners by conducting a comprehensive comparative study. Several trends were observed in the experiments. Class and attribute had the greatest impact on the performance of learners when it occurred simultaneously in the data. Class noise had a significant impact on the performance of learners, while attribute noise had no impact when it occurred in less than 40% of the most significant independent attributes. Random Forest (RF100), a group of 100 decision trees, was the most, accurate and robust learner in all the experiments with noisy data.
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Date Issued
2008
PURL
http://purl.flvc.org/fau/fd/FA00012567
Subject Headings
Computer software--Quality control, Computer software--Reliability, Software engineering, Computer arithmetic
Format
Document (PDF)