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Title: A Comparison of Model Checking Tools for Service Oriented Architectures.
Creator: Venkat, Raghava, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Recently most of the research pertaining to Service-Oriented Architecture (SOA) is based on web services and how secure they are in terms of efficiency and effectiveness. This requires validation, verification, and evaluation of web services. Verification and validation should be collaborative when web services from different vendors are integrated together to carry out a coherent task. For this purpose, novel model checking technologies have been devised and applied to web services. "Model...
Show moreRecently most of the research pertaining to Service-Oriented Architecture (SOA) is based on web services and how secure they are in terms of efficiency and effectiveness. This requires validation, verification, and evaluation of web services. Verification and validation should be collaborative when web services from different vendors are integrated together to carry out a coherent task. For this purpose, novel model checking technologies have been devised and applied to web services. "Model Checking" is a promising technique for verification and validation of software systems. WS-BPEL (Business Process Execution Language for Web Services) is an emerging standard language to describe web service composition behavior. The advanced features of BPEL such as concurrency and hierarchy make it challenging to verify BPEL models. Based on all such factors my thesis surveys a few important technologies (tools) for model checking and comparing each of them based on their "functional" and "non-functional" properties. The comparison is based on three case studies (first being the small case, second medium and the third one a large case) where we construct synthetic web service compositions for each case (as there are not many publicly available compositions [1]). The first case study is "Enhanced LoanApproval Process" and is considered a small case. The second is "Enhanced Purchase Order Process" which is of medium size and the third, and largest is based on a scientific workflow pattern, called the "Service Oriented Architecture Implementing BOINC Workflow" based on BOINC (Berkeley Open Infrastructure Network Computing) architecture.
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Date Issued: 2007
PURL: http://purl.flvc.org/fau/fd/FA00012565
Subject Headings: Computer network architectures, Expert systems (Computer science), Software engineering, Web servers--Management
Format: Document (PDF)

Title: Assessing Data Interoperability ofUML Modeling Tools.
Creator: Gohel, Vaishali P., Huang, Shihong, Florida Atlantic University
Abstract/Description: In the globalization software development environments, where the development activities are distributed geographically and temporally, it is increasingly important for the Computer Aided Software Engineering (CASE) tools to maintain the information (both syntactic and semantic) captured in the design models. The Unified Modeling Language (UML) is the de facto standard for modeling software applications and UML diagrams serve as graphical documentations of the software system. The...
Show moreIn the globalization software development environments, where the development activities are distributed geographically and temporally, it is increasingly important for the Computer Aided Software Engineering (CASE) tools to maintain the information (both syntactic and semantic) captured in the design models. The Unified Modeling Language (UML) is the de facto standard for modeling software applications and UML diagrams serve as graphical documentations of the software system. The interoperability of UML modeling tools is important in supporting the models exchange, and further support design reuse. Tool interoperability is often implemented using XML Metadata Interchange (XMI). Unfortunately, there is a loss of fidelity of the design documentation when transforming between UML and XMI due to the compatibility of different versions of UML, XMI and add-on proprietary information, which hinder reuse. This thesis evaluates the interoperability of UML modeling tools by assessing the quality of XMI documents representing the design. Case studies in this thesis demonstrate a framework of preserving the fidelity of UML model 's data when importing and exporting different UML models in a distributed heterogeneous environment.
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Date Issued: 2007
PURL: http://purl.flvc.org/fau/fd/FA00012519
Subject Headings: UML (Computer science), Computer software--Development, Software engineering, Computer-aided design
Format: Document (PDF)

Title: CBR-based software quality models and quality of data.
Creator: Xiao, Yudong., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The performance accuracy of software quality estimation models is influenced by several factors, including the following two important factors: performance of the prediction algorithm and the quality of data. This dissertation addresses these two factors, and consists of two components: (1) a proposed genetic algorithm (GA) based optimization of software quality models for accuracy enhancement, and (2) a proposed partitioning- and rule-based filter (PRBF) for noise detection toward...
Show moreThe performance accuracy of software quality estimation models is influenced by several factors, including the following two important factors: performance of the prediction algorithm and the quality of data. This dissertation addresses these two factors, and consists of two components: (1) a proposed genetic algorithm (GA) based optimization of software quality models for accuracy enhancement, and (2) a proposed partitioning- and rule-based filter (PRBF) for noise detection toward improvement of data quality. We construct a generalized framework of our embedded GA-optimizer, and instantiate the GA-optimizer for three optimization problems in software quality engineering: parameter optimization for case-based reasoning (CBR) models; module rank optimization for module-order modeling (MOM); and structural optimization for our multi-strategy classification modeling approach, denoted RB2CBL. Empirical case studies using software measurement data from real-world software systems were performed for the optimization problems. The GA-optimization approaches improved software quality prediction accuracy, highlighting the practical benefits of using GA for solving optimization problems in software engineering. The proposed noise detection approach, PRBF, was empirically evaluated using data categorized into two classes. Empirical studies on artificially corrupted datasets and datasets with known (natural) noise demonstrated that PRBF can effectively detect both artificial and natural noise. The proposed filter is a stable and robust technique, and always provided optimal or near-optimal noise detection results. In addition, it is applicable on datasets with nominal and numerical attributes, as well as those with missing values. The PRBF technique supports two methods of noise detection: class noise detection and cost-sensitive noise detection. The former is an easy-to-use method and does not need parameter settings, while the latter is suited for applications where each class has a specific misclassification cost. PRBF can also be used iteratively to investigate the two general types of data noise: attribute and class noise.
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Date Issued: 2005
PURL: http://purl.flvc.org/fcla/dt/12141
Subject Headings: Computer software--Quality control, Genetic programming (Computer science), Software engineering, Case-based reasoning, Combinatorial optimization, Computer network architecture
Format: Document (PDF)

Title: Classification of software quality using tree modeling with the SPRINT/SLIQ algorithm.
Creator: Mao, Wenlei., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Providing high quality software products is the common goal of all software engineers. Finding faults early can produce large savings over the software life cycle. Therefore, software quality has become the main subject in our research field. This thesis presents a series of studies on a very large legacy telecommunication system. The system has significantly more than ten million lines of code written in a high-level language similar to Pascal. Software quality models were developed to...
Show moreProviding high quality software products is the common goal of all software engineers. Finding faults early can produce large savings over the software life cycle. Therefore, software quality has become the main subject in our research field. This thesis presents a series of studies on a very large legacy telecommunication system. The system has significantly more than ten million lines of code written in a high-level language similar to Pascal. Software quality models were developed to predict the class of each module either as fault-prone or as not fault-prone. We used the SPRINT/SLIQ algorithm to build the classification tree models. We found out that SPRINT/ SLIQ as an improved CART algorithm can give us tree models with more accuracy, more balance, and less overfitting. We also found that software process metrics can significantly improve the predictive accuracy of software quality models.
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Date Issued: 2000
PURL: http://purl.flvc.org/fcla/dt/15767
Subject Headings: Computer software--Quality control, Software engineering, Software measurement
Format: Document (PDF)

Title: A comparative study of attribute selection techniques for CBR-based software quality classification models.
Creator: Nguyen, Laurent Quoc Viet., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: To achieve high reliability in software-based systems, software metrics-based quality classification models have been explored in the literature. However, the collection of software metrics may be a hard and long process, and some metrics may not be helpful or may be harmful to the classification models, deteriorating the models' accuracies. Hence, methodologies have been developed to select the most significant metrics in order to build accurate and efficient classification models. Case...
Show moreTo achieve high reliability in software-based systems, software metrics-based quality classification models have been explored in the literature. However, the collection of software metrics may be a hard and long process, and some metrics may not be helpful or may be harmful to the classification models, deteriorating the models' accuracies. Hence, methodologies have been developed to select the most significant metrics in order to build accurate and efficient classification models. Case-Based Reasoning is the classification technique used in this thesis. Since it does not provide any metric selection mechanisms, some metric selection techniques were studied. In the context of CBR, this thesis presents a comparative evaluation of metric selection methodologies, for raw and discretized data. Three attribute selection techniques have been studied: Kolmogorov-Smirnov Two-Sample Test, Kruskal-Wallis Test, and Information Gain. These techniques resulted in classification models that are useful for software quality improvement.
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Date Issued: 2002
PURL: http://purl.flvc.org/fcla/dt/12944
Subject Headings: Case-based reasoning, Software engineering, Computer software--Quality control
Format: Document (PDF)

Title: A comprehensive comparative study of multiple classification techniques for software quality estimation.
Creator: Puppala, Kishore., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Reliability and quality are desired features in industrial software applications. In some cases, they are absolutely essential. When faced with limited resources, software project managers will need to allocate such resources to the most fault prone areas. The ability to accurately classify a software module as fault-prone or not fault-prone enables the manager to make an informed resource allocation decision. An accurate quality classification avoids wasting resources on modules that are not...
Show moreReliability and quality are desired features in industrial software applications. In some cases, they are absolutely essential. When faced with limited resources, software project managers will need to allocate such resources to the most fault prone areas. The ability to accurately classify a software module as fault-prone or not fault-prone enables the manager to make an informed resource allocation decision. An accurate quality classification avoids wasting resources on modules that are not fault-prone. It also avoids missing the opportunity to correct faults relatively early in the development cycle, when they are less costly. This thesis seeks to introduce the classification algorithms (classifiers) that are implemented in the WEKA software tool. WEKA (Waikato Environment for Knowledge Analysis) was developed at the University of Waikato in New Zealand. An empirical investigation is performed using a case study at a real-world system.
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Date Issued: 2003
PURL: http://purl.flvc.org/fcla/dt/13039
Subject Headings: Software engineering, Computer software--Quality control, Decision trees
Format: Document (PDF)

Title: Count models for software quality estimation.
Creator: Gao, Kehan, Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The primary aim of software engineering is to produce quality software that is delivered on time, within budget, and fulfils all its requirements. A timely estimation of software quality can serve as a prerequisite in achieving high reliability of software-based systems. More specifically, software quality assurance efforts can be prioritized for targeting program modules that are most likely to have a high number of faults. Software quality estimation models are generally of two types: a...
Show moreThe primary aim of software engineering is to produce quality software that is delivered on time, within budget, and fulfils all its requirements. A timely estimation of software quality can serve as a prerequisite in achieving high reliability of software-based systems. More specifically, software quality assurance efforts can be prioritized for targeting program modules that are most likely to have a high number of faults. Software quality estimation models are generally of two types: a classification model that predicts the class membership of modules into two or more quality-based classes, and a quantitative prediction model that estimates the number of faults (or some other software quality factor) that are likely to occur in software modules. In the literature, a variety of techniques have been developed for software quality estimation, most of which are suited for either prediction or classification but not for both, e.g., the multiple linear regression (only for prediction) and logistic regression (only for classification).
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Date Issued: 2003
PURL: http://purl.flvc.org/fcla/dt/12042
Subject Headings: Computer software--Quality control, Software engineering, Econometrics, Regression analysis
Format: Document (PDF)

Title: Detection of change-prone telecommunications software modules.
Creator: Weir, Ronald Eugene., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Accurately classifying the quality of software is a major problem in any software development project. Software engineers develop models that provide early estimates of quality metrics which allow them to take actions against emerging quality problems. The use of a neural network as a tool to classify programs as a low, medium, or high risk for errors or change is explored using multiple software metrics as input. It is demonstrated that a neural network, trained using the back-propagation...
Show moreAccurately classifying the quality of software is a major problem in any software development project. Software engineers develop models that provide early estimates of quality metrics which allow them to take actions against emerging quality problems. The use of a neural network as a tool to classify programs as a low, medium, or high risk for errors or change is explored using multiple software metrics as input. It is demonstrated that a neural network, trained using the back-propagation supervised learning strategy, produced the desired mapping between the static software metrics and the software quality classes. The neural network classification methodology is compared to the discriminant analysis classification methodology in this experiment. The comparison is based on two and three class predictive models developed using variables resulting from principal component analysis of software metrics.
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Date Issued: 1995
PURL: http://purl.flvc.org/fcla/dt/15183
Subject Headings: Computer software--Evaluation, Software engineering, Neural networks (Computer science)
Format: Document (PDF)

Title: Developing accurate software quality models using a faster, easier, and cheaper method.
Creator: Lim, Linda., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Managers of software development need to know which components of a system are fault-prone. If this can be determined early in the development cycle then resources can be more effectively allocated and significant costs can be reduced. Case-Based Reasoning (CBR) is a simple and efficient methodology for building software quality models that can provide early information to managers. Our research focuses on two case studies. The first study analyzes source files and classifies them as fault...
Show moreManagers of software development need to know which components of a system are fault-prone. If this can be determined early in the development cycle then resources can be more effectively allocated and significant costs can be reduced. Case-Based Reasoning (CBR) is a simple and efficient methodology for building software quality models that can provide early information to managers. Our research focuses on two case studies. The first study analyzes source files and classifies them as fault-prone or not fault-prone. It also predicts the number of faults in each file. The second study analyzes the fault removal process, and creates models that predict the outcome of software inspections.
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/12746
Subject Headings: Computer software--Development, Computer software--Quality control, Software engineering
Format: Document (PDF)

Title: A framework for an icon-based software engineering environment.
Creator: Huang, Qinxi., Florida Atlantic University, Larrondo-Petrie, Maria M.
Abstract/Description: Current computer technologies and demands bring new challenges to the software engineering tools. This thesis includes a survey of software engineering environments, standards and technologies. It also examines the features needed to support rigorous object-oriented software development. The main contributions of the thesis are descriptions of innovative concepts and a high-level framework for a next-generation object-oriented software system development, management and maintenance...
Show moreCurrent computer technologies and demands bring new challenges to the software engineering tools. This thesis includes a survey of software engineering environments, standards and technologies. It also examines the features needed to support rigorous object-oriented software development. The main contributions of the thesis are descriptions of innovative concepts and a high-level framework for a next-generation object-oriented software system development, management and maintenance environment, called IconSEE++, an Icon-based Software Engineering Environment.
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Date Issued: 1998
PURL: http://purl.flvc.org/fcla/dt/15576
Subject Headings: Software engineering, Object-oriented methods (Computer science), Icon (Computer program language)
Format: Document (PDF)

Title: Fuzzy logic techniques for software reliability engineering.
Creator: Xu, Zhiwei., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Modern people are becoming more and more dependent on computers in their daily lives. Most industries, from automobile, avionics, oil, and telecommunications to banking, stocks, and pharmaceuticals, require computers to function. As the tasks required become more complex, the complexity of computer software and hardware has increased dramatically. As a consequence, the possibility of failure increases. As the requirements for and dependence on computers increases, the possibility of crises...
Show moreModern people are becoming more and more dependent on computers in their daily lives. Most industries, from automobile, avionics, oil, and telecommunications to banking, stocks, and pharmaceuticals, require computers to function. As the tasks required become more complex, the complexity of computer software and hardware has increased dramatically. As a consequence, the possibility of failure increases. As the requirements for and dependence on computers increases, the possibility of crises caused by computer failures also increases. High reliability is an important attribute for almost any software system. Consequently, software developers are seeking ways to forecast and improve quality before release. Since many quality factors cannot be measured until after the software becomes operational, software quality models are developed to predict quality factors based on measurements collected earlier in the life cycle. Due to incomplete information in the early life cycle of software development, software quality models with fuzzy characteristics usually perform better because fuzzy concepts deal with phenomenon that is vague in nature. This study focuses on the usage of fuzzy logic in software reliability engineering. Discussing will include the fuzzy expert systems and the application of fuzzy expert systems in early risk assessment; introducing the interval prediction using fuzzy regression modeling; demonstrating fuzzy rule extraction for fuzzy classification and its usage in software quality models; demonstrating the fuzzy identification, including extraction of both rules and membership functions from fuzzy data and applying the technique to software project cost estimations. The following methodologies were considered: nonparametric discriminant analysis, Z-test and paired t-test, neural networks, fuzzy linear regression, fuzzy nonlinear regression, fuzzy classification with maximum matched method, fuzzy identification with fuzzy clustering, and fuzzy projection. Commercial software systems and the COCOMO database are used throughout this dissertation to demonstrate the usefulness of concepts and to validate new ideas.
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/11948
Subject Headings: Software engineering, Fuzzy logic, Computer software--Quality control, Fuzzy systems
Format: Document (PDF)

Title: Impact of best manufacturing engineering practices on software engineering practices.
Creator: Akhter, Shahina., Florida Atlantic University, Coulter, Neal S.
Abstract/Description: This thesis involves original research in the area of semantic analysis of textual databases (content analysis). The main intention of this study is to examine how software engineering practices can benefit from the best manufacturing practices. There is a deliberate focus and emphasis on competitive effectiveness worldwide. The ultimate goal of the U.S. NAVY's Best Manufacturing Practices Program is to strengthen the U.S. industrial base and reduce the cost of defense systems by solving...
Show moreThis thesis involves original research in the area of semantic analysis of textual databases (content analysis). The main intention of this study is to examine how software engineering practices can benefit from the best manufacturing practices. There is a deliberate focus and emphasis on competitive effectiveness worldwide. The ultimate goal of the U.S. NAVY's Best Manufacturing Practices Program is to strengthen the U.S. industrial base and reduce the cost of defense systems by solving manufacturing problems and improving quality and reliability. Best manufacturing practices can assist software engineering practices in a way that when software companies use these practices they can: (1) Improve both software quality and staff productivity; (2) Determine the current status of the organization's software process; (3) Set goals for process improvement; (4) Create effective plans for reaching those goals; (5) Implement the major elements of the plans.
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Date Issued: 1997
PURL: http://purl.flvc.org/fcla/dt/15445
Subject Headings: Software engineering, Production engineering, Production management--Computer softwares
Format: Document (PDF)

Title: An improved neural net-based approach for predicting software quality.
Creator: Guasti, Peter John., Florida Atlantic University, Khoshgoftaar, Taghi M., Pandya, Abhijit S.
Abstract/Description: Accurately predicting the quality of software is a major problem in any software development project. Software engineers develop models that provide early estimates of quality metrics which allow them to take action against emerging quality problems. Most often the predictive models are based upon multiple regression analysis which become unstable when certain data assumptions are not met. Since neural networks require no data assumptions, they are more appropriate for predicting software...
Show moreAccurately predicting the quality of software is a major problem in any software development project. Software engineers develop models that provide early estimates of quality metrics which allow them to take action against emerging quality problems. Most often the predictive models are based upon multiple regression analysis which become unstable when certain data assumptions are not met. Since neural networks require no data assumptions, they are more appropriate for predicting software quality. This study proposes an improved neural network architecture that significantly outperforms multiple regression and other neural network attempts at modeling software quality. This is demonstrated by applying this approach to several large commercial software systems. After developing neural network models, we develop regression models on the same data. We find that the neural network models surpass the regression models in terms of predictive quality on the data sets considered.
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Date Issued: 1995
PURL: http://purl.flvc.org/fcla/dt/15134
Subject Headings: Neural networks (Computer science), Computer software--Development, Computer software--Quality control, Software engineering
Format: Document (PDF)

Title: Information theory and software measurement.
Creator: Allen, Edward B., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Development of reliable, high quality, software requires study and understanding at each step of the development process. A basic assumption in the field of software measurement is that metrics of internal software attributes somehow relate to the intrinsic difficulty in understanding a program. Measuring the information content of a program attempts to indirectly quantify the comprehension task. Information theory based software metrics are attractive because they quantify the amount of...
Show moreDevelopment of reliable, high quality, software requires study and understanding at each step of the development process. A basic assumption in the field of software measurement is that metrics of internal software attributes somehow relate to the intrinsic difficulty in understanding a program. Measuring the information content of a program attempts to indirectly quantify the comprehension task. Information theory based software metrics are attractive because they quantify the amount of information in a well defined framework. However, most information theory based metrics have been proposed with little reference to measurement theory fundamentals, and empirical validation of predictive quality models has been lacking. This dissertation proves that representative information theory based software metrics can be "meaningful" components of software quality models in the context of measurement theory. To this end, members of a major class of metrics are shown to be regular representations of Minimum Description Length or Variety of software attributes, and are interval scale. An empirical validation case study is presented that predicted faults in modules based on Operator Information. This metric is closely related to Harrison's Average Information Content Classification, which is the entropy of the operators. New general methods for calculating synthetic complexity at the system level and module level are presented, quantifying the joint information of an arbitrary set of primitive software measures. Since all kinds of information are not equally relevant to software quality factors, components of synthetic module complexity are also defined. Empirical case studies illustrate the potential usefulness of the proposed synthetic metrics. A metrics data base is often the key to a successful ongoing software metrics program. The contribution of any proposed metric is defined in terms of measured variation using information theory, irrespective of the metric's usefulness in quality models. This is of interest when full validation is not practical. Case studies illustrate the method.
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Date Issued: 1995
PURL: http://purl.flvc.org/fcla/dt/12412
Subject Headings: Software engineering, Computer software--Quality control, Information theory
Format: Document (PDF)

Title: INTEGRATING DESIGN THINKING MODEL AND ITEMS PRIORITIZATION DECISION SUPPORT SYSTEMS INTO REQUIREMENTS MANAGEMENT IN SCRUM.
Creator: Alhazmi, Alhejab Shawqi, Huang, Shihong, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The Agile methodologies have attracted the software development industry's attention due to their capability to overcome the limitations of the traditional software development approaches and to cope with increasing complexity in system development. Scrum is one of the Agile software development processes broadly adopted by industry. Scrum promotes frequent customer involvement and incremental short releases. Despite its popular use, Scrum’s requirements engineering stage is inadequately...
Show moreThe Agile methodologies have attracted the software development industry's attention due to their capability to overcome the limitations of the traditional software development approaches and to cope with increasing complexity in system development. Scrum is one of the Agile software development processes broadly adopted by industry. Scrum promotes frequent customer involvement and incremental short releases. Despite its popular use, Scrum’s requirements engineering stage is inadequately defined which can lead to increase development time and cost, along with low quality or failure for the end products. This research shows the importance of activity planning of requirements engineering in improving the product quality, cost, and scheduling as well as it points out some drawbacks of Agile practices and available solutions. To improve the Scrum requirements engineering by overcoming its challenges in cases, such as providing a comprehensive understanding of the customer’s needs and addressing the effects of the challenges in other cases, such as frequent changes of requirements, the Design Thinking model is integrated into the Scrum framework in the context of requirements engineering management. The use of the Design Thinking model, in the context of requirements engineering management, is validated through an in-depth scientific study of the IBM Design Thinking framework. In addition, this research presents an Items Prioritization dEcision Support System (IPESS) which is a tool to assist the Product Owners for requirements prioritization. IPESS is built on information collected in the Design Thinking model. The IPESS tool adopts Analytic Hierarchy Process (AHP) technique and PageRank algorithm to deal with the specified factors and to achieve the optimal order for requirements items based on the prioritization score. IPESS is a flexible and comprehensive tool that focuses on different important aspects including customer satisfaction and product quality. The IPESS tool is validated through an experiment that was conducted in a real-world project
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Date Issued: 2021
PURL: http://purl.flvc.org/fau/fd/FA00013699
Subject Headings: Scrum (Computer software development), Computer software--Development--Management, Software engineering
Format: Document (PDF)

Title: Model-Driven Architecture and the Secure Systems Methodology.
Creator: Morrison, Patrick, Fernandez, Eduardo B., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: As a compamon and complement to the work being done to build a secure systems methodology, this thesis evaluates the use of Model-Driven Architecture (MDA) in support of the methodology's lifecycle. The development lifecycle illustrated follows the recommendations of this secure systems methodology, while using MDA models to represent requirements, analysis, design, and implementation information. In order to evaluate MDA, we analyze a well-understood distributed systems security problem,...
Show moreAs a compamon and complement to the work being done to build a secure systems methodology, this thesis evaluates the use of Model-Driven Architecture (MDA) in support of the methodology's lifecycle. The development lifecycle illustrated follows the recommendations of this secure systems methodology, while using MDA models to represent requirements, analysis, design, and implementation information. In order to evaluate MDA, we analyze a well-understood distributed systems security problem, remote access, as illustrated by the internet "secure shell" protocol, ssh. By observing the ability of MDA models and transformations to specify remote access in each lifecycle phase, MDA's strengths and weaknesses can be evaluated in this context. A further aim of this work is to extract concepts that can be contained in an MDA security metamodel for use in future projects.
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Date Issued: 2007
PURL: http://purl.flvc.org/fau/fd/FA00012537
Subject Headings: Expert systems (Computer science), Software engineering, Computer-aided design, Computer network architectures
Format: Document (PDF)

Title: Modeling fault-prone modules of subsystems.
Creator: Thaker, Vishal Kirit., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In software engineering software quality has become a topic of major concern. It has also been recognized that the role of maintenance organization is to understand and estimate the cost of maintenance releases of software systems. Planning the next release so as to maximize the increase in functionality and the improvement in quality are essential to successful maintenance management. With the growing collection of software in organizations this cost is becoming substantial. In this research...
Show moreIn software engineering software quality has become a topic of major concern. It has also been recognized that the role of maintenance organization is to understand and estimate the cost of maintenance releases of software systems. Planning the next release so as to maximize the increase in functionality and the improvement in quality are essential to successful maintenance management. With the growing collection of software in organizations this cost is becoming substantial. In this research we have compared two software quality models. We tried to see whether a model built on entire system which predicts subsystem and a model built on subsystem which predicts the same subsystem has similar, better or worst classification results. We used Classification And Regression Tree algorithm (CART) to build classification models. A case study is based on a very large telecommunication system.
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Date Issued: 2000
PURL: http://purl.flvc.org/fcla/dt/12700
Subject Headings: Computer software--Quality control, Software engineering
Format: Document (PDF)

Title: Modeling software quality with classification trees using principal components analysis.
Creator: Shan, Ruqun., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Software quality models often have raw software metrics as the input data for predicting quality. Raw metrics are usually highly correlated with one another and thus may result in unstable models. Principal components analysis is a statistical method to improve model stability. This thesis presents a series of studies on a very large legacy telecommunication system. The system has significantly more than ten million lines of code written in a high level language similar to Pascal. Software...
Show moreSoftware quality models often have raw software metrics as the input data for predicting quality. Raw metrics are usually highly correlated with one another and thus may result in unstable models. Principal components analysis is a statistical method to improve model stability. This thesis presents a series of studies on a very large legacy telecommunication system. The system has significantly more than ten million lines of code written in a high level language similar to Pascal. Software quality models were developed to predict the class of each module either as fault-prone or as not fault-prone. We found out that the models based on principal components analysis were more robust than those based on raw metrics. We also found out that software process metrics can significantly improve the predictive accuracy of software quality models.
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Date Issued: 1999
PURL: http://purl.flvc.org/fcla/dt/15714
Subject Headings: Principal components analysis, Computer software--Quality control, Software engineering
Format: Document (PDF)

Title: Modeling software quality with TREEDISC algorithm.
Creator: Yuan, Xiaojing, Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Software quality is crucial both to software makers and customers. However, in reality, improvement of quality and reduction of costs are often at odds. Software modeling can help us to detect fault-prone software modules based on software metrics, so that we can focus our limited resources on fewer modules and lower the cost but still achieve high quality. In the present study, a tree classification modeling technique---TREEDISC was applied to three case studies. Several major contributions...
Show moreSoftware quality is crucial both to software makers and customers. However, in reality, improvement of quality and reduction of costs are often at odds. Software modeling can help us to detect fault-prone software modules based on software metrics, so that we can focus our limited resources on fewer modules and lower the cost but still achieve high quality. In the present study, a tree classification modeling technique---TREEDISC was applied to three case studies. Several major contributions have been made. First, preprocessing of raw data was adopted to solve the computer memory problem and improve the models. Secondly, TREEDISC was thoroughly explored by examining the roles of important parameters in modeling. Thirdly, a generalized classification rule was introduced to balance misclassification rates and decrease type II error, which is considered more costly than type I error. Fourthly, certainty of classification was addressed. Fifthly, TREEDISC modeling was validated over multiple releases of software product.
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Date Issued: 1999
PURL: http://purl.flvc.org/fcla/dt/15718
Subject Headings: Computer software--Quality control, Computer simulation, Software engineering
Format: Document (PDF)

Title: Multivariate modeling of software engineering measures.
Creator: Lanning, David Lee., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: One goal of software engineers is to produce software products. An additional goal, that the software production must lead to profit, releases the power of the software product market. This market demands high quality products and tight cycles in the delivery of new and enhanced products. These market conditions motivate the search for engineering methods that help software producers ship products quicker, at lower cost, and with fewer defects. The control of software defects is key to...
Show moreOne goal of software engineers is to produce software products. An additional goal, that the software production must lead to profit, releases the power of the software product market. This market demands high quality products and tight cycles in the delivery of new and enhanced products. These market conditions motivate the search for engineering methods that help software producers ship products quicker, at lower cost, and with fewer defects. The control of software defects is key to meeting these market conditions. Thus, many software engineering tasks are concerned with software defects. This study considers two sources of variation in the distribution of software defects: software complexity and enhancement activity. Multivariate techniques treat defect activity, software complexity, and enhancement activity as related multivariate concepts. Applied techniques include principal components analysis, canonical correlation analysis, discriminant analysis, and multiple regression analysis. The objective of this study is to improve our understanding of software complexity and software enhancement activity as sources of variation in defect activity, and to apply this understanding to produce predictive and discriminant models useful during testing and maintenance tasks. These models serve to support critical software engineering decisions.
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Date Issued: 1994
PURL: http://purl.flvc.org/fcla/dt/12383
Subject Headings: Software engineering, Computer software--Testing, Computer software--Quality control
Format: Document (PDF)

Computer software--Quality control (16)	+ -
Computer network architectures (3)	+ -
Computer software--Development (3)	+ -
Expert systems (Computer science) (3)	+ -
Neural networks (Computer science) (3)	+ -

Case-based reasoning (2)	+ -
Computer software--Reliability (2)	+ -
Computer-aided design (2)	+ -
Genetic programming (Computer science) (2)	+ -
Combinatorial optimization (1)	+ -
Computer architecture (1)	+ -
Computer arithmetic (1)	+ -
Computer network architecture (1)	+ -
Computer network protocols (1)	+ -
Computer networks--Security measures (1)	+ -
Computer simulation (1)	+ -
Computer software--Development--Management (1)	+ -
Computer software--Evaluation (1)	+ -
Computer software--Testing (1)	+ -
Decision trees (1)	+ -
Econometrics (1)	+ -
Fuzzy logic (1)	+ -
Fuzzy systems (1)	+ -
Genetic algorithms (1)	+ -
Global system for mobile communications (1)	+ -
Icon (Computer program language) (1)	+ -
Information theory (1)	+ -
Internet telephony--Security measures (1)	+ -
Management (1)	+ -

College of Engineering and Computer Science (20)	+ -
Department of Computer and Electrical Engineering and Computer Science (20)	+ -
Khoshgoftaar, Taghi M. (19)	+ -
Bullard, Lofton A. (2)	+ -
Fernandez, Eduardo B. (2)	+ -

Huang, Shihong (2)	+ -
Akhter, Shahina. (1)	+ -
Alhazmi, Alhejab Shawqi (1)	+ -
Allen, Edward B. (1)	+ -
Coulter, Neal S. (1)	+ -
Delessy, Nelly A. (1)	+ -
Gao, Kehan (1)	+ -
Gohel, Vaishali P. (1)	+ -
Guasti, Peter John. (1)	+ -
Hochman, Robert. (1)	+ -
Huang, Qinxi. (1)	+ -
Jain, Ankit. (1)	+ -
Lanning, David Lee. (1)	+ -
Larrondo-Petrie, Maria M. (1)	+ -
Lim, Linda. (1)	+ -
Liu, Yi. (1)	+ -
Mao, Wenlei. (1)	+ -
Morrison, Patrick (1)	+ -
Nguyen, Laurent Quoc Viet. (1)	+ -
Pandya, Abhijit S. (1)	+ -
Pelaez, Juan C. (1)	+ -
Puppala, Kishore. (1)	+ -
Shan, Ruqun. (1)	+ -
Shankar, Ravi (1)	+ -

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