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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: Classification of software quality with tree modeling using C4.5 algorithm.
Creator: Ponnuswamy, Viswanathan Kolathupalayam., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Developing highly reliable software is a must in today's competitive environment. However quality control is a costly and time consuming process. If the quality of software modules being developed can be predicted early in their life cycle, resources can be effectively allocated improving quality, reducing cost and development time. This study examines the C4.5 algorithm as a tool for building classification trees, classifying software module either as fault-prone or not fault-prone. The...
Show moreDeveloping highly reliable software is a must in today's competitive environment. However quality control is a costly and time consuming process. If the quality of software modules being developed can be predicted early in their life cycle, resources can be effectively allocated improving quality, reducing cost and development time. This study examines the C4.5 algorithm as a tool for building classification trees, classifying software module either as fault-prone or not fault-prone. The classification tree models were developed based on four consecutive releases of a very large legacy telecommunication system. The first two releases were used as training data sets and the subsequent two releases were used as test data sets to evaluate the model. We found out that C4.5 was able to build compact classification trees models with balanced misclassification rates.
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/12855
Subject Headings: Computer software--Quality control, 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: 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: An empirical study of analogy-based software fault prediction.
Creator: Sundaresh, Nandini., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Ensuring quality and reliability in software is important with its growing use in day to day life. Having an estimate of the number of faults in software modules early in their life cycles will enable software project managers to direct testing efforts in those considered risky and reduce the waste of resources in testing the entire software system. Case-based reasoning, abbreviated CBR, is one of the methods which predicts the number of faults in a software. The scope of this thesis is two...
Show moreEnsuring quality and reliability in software is important with its growing use in day to day life. Having an estimate of the number of faults in software modules early in their life cycles will enable software project managers to direct testing efforts in those considered risky and reduce the waste of resources in testing the entire software system. Case-based reasoning, abbreviated CBR, is one of the methods which predicts the number of faults in a software. The scope of this thesis is two-fold. First, it empirically investigates the effects of the different factors on the predictive accuracy of CBR. Experiments were done to compare different similarity functions, solution processes, and maximum number of nearest neighbors. Second, it compares the predictive accuracy of CBR models with multiple linear regression and artificial neural network models. The average absolute error and average relative error are used to determine the model with a high accuracy of prediction.
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/12749
Subject Headings: Computer software--Quality control, Software measurement
Format: Document (PDF)

Title: An empirical study of analogy-based software quality classification models.
Creator: Ross, Fletcher Douglas., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Time and cost are among the most important elements in a software project. By efficiently using time and resources we can reduce costs. Any program can potentially contain faults. If we can identify those program modules that have better quality and are less likely to be fault-prone, then we can reduce the effort and cost required in testing these modules. This thesis presents a series of studies evaluating the use of Case-Based Reasoning (CBR ) as an effective method for classifying program...
Show moreTime and cost are among the most important elements in a software project. By efficiently using time and resources we can reduce costs. Any program can potentially contain faults. If we can identify those program modules that have better quality and are less likely to be fault-prone, then we can reduce the effort and cost required in testing these modules. This thesis presents a series of studies evaluating the use of Case-Based Reasoning (CBR ) as an effective method for classifying program modules based upon their quality. We believe that this is the first time that the mahalanobis distance, a distance measure utilizing the covariance matrix of the independent variables which accounts for the multi-colinearity of the data without the necessity for preprocessing, and data clustering, wherein the data was separated into groups based on a dependent variable have been used as modeling techniques in conjunction with (CBR).
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/12817
Subject Headings: Modular programming, Computer software--Quality control, Software measurement
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: Implementation of a three-group classification model using case-based reasoning.
Creator: Song, Huiming., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Reliability is becoming a very important and competitive factor for software products. Software quality models based on software metrics provide a systematic and scientific way to detect software faults early and to improve software reliability. Classification models for software quality usually classify observations using two groups. This thesis presents a new algorithm for classification using three groups, i.e., Three-Group Classification Model using Case Based Reasoning. The basic idea...
Show moreReliability is becoming a very important and competitive factor for software products. Software quality models based on software metrics provide a systematic and scientific way to detect software faults early and to improve software reliability. Classification models for software quality usually classify observations using two groups. This thesis presents a new algorithm for classification using three groups, i.e., Three-Group Classification Model using Case Based Reasoning. The basic idea behind the algorithm is that it uses the commonly used two-group classification method three times. This algorithm can be implemented with other techniques such as logistic regression, classification tree models, etc. This work compares its quality with the Discriminant Analysis method. We find that our new method performs much better than Discriminant Analysis. We also show that the addition of object-oriented software measures yielded a model that a practitioner may actually prefer over the simpler procedural measures model.
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/12816
Subject Headings: Software measurement, Computer software--Quality control
Format: Document (PDF)

Title: Improved models of software quality.
Creator: Szabo, Robert Michael., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Though software development has been evolving for over 50 years, the development of computer software systems has largely remained an art. Through the application of measurable and repeatable processes, efforts have been made to slowly transform the software development art into a rigorous engineering discipline. The potential gains are tremendous. Computer software pervades modern society in many forms. For example, the automobile, radio, television, telephone, refrigerator, and still-camera...
Show moreThough software development has been evolving for over 50 years, the development of computer software systems has largely remained an art. Through the application of measurable and repeatable processes, efforts have been made to slowly transform the software development art into a rigorous engineering discipline. The potential gains are tremendous. Computer software pervades modern society in many forms. For example, the automobile, radio, television, telephone, refrigerator, and still-camera have all been transformed by the introduction of computer based controls. The quality of these everyday products is in part determined by the quality of the computer software running inside them. Therefore, the timely delivery of low-cost and high-quality software to enable these mass market products becomes very important to the long term success of the companies building them. It is not surprising that managing the number of faults in computer software to competitive levels is a prime focus of the software engineering activity. In support of this activity, many models of software quality have been developed to help control the software development process and ensure that our goals of cost and quality are met on time. In this study, we focus on the software quality modeling activity. We improve existing static and dynamic methodologies and demonstrate new ones in a coordinated attempt to provide engineering methods applicable to the development of computer software. We will show how the power of separate predictive and classification models of software quality may be combined into one model; introduce a three group fault classification model in the object-oriented paradigm; demonstrate a dynamic modeling methodology of the testing process and show how software product measures and software process measures may be incorporated as input to such a model; demonstrate a relationship between software product measures and the testability of software. The following methodologies were considered: principal components analysis, multiple regression analysis, Poisson regression analysis, discriminant analysis, time series analysis, and neural networks. Commercial grade software systems are used throughout this dissertation to demonstrate concepts and validate new ideas. As a result, we hope to incrementally advance the state of the software engineering "art".
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Date Issued: 1995
PURL: http://purl.flvc.org/fcla/dt/12409
Subject Headings: Software engineering--Standards, Software engineering--Management, Computer software--Development, Computer software--Quality control
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: A metrics-based software quality modeling tool.
Creator: Rajeevalochanam, Jayanth Munikote., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In today's world, high reliability has become an essential component of almost every software system. However, since the reliability-enhancement activities entail enormous costs, software quality models, based on the metrics collected early in the development life cycle, serve as handy tools for cost-effectively guiding such activities to the software modules that are likely to be faulty. Case-Based Reasoning (CBR) is an attractive technique for software quality modeling. Software Measurement...
Show moreIn today's world, high reliability has become an essential component of almost every software system. However, since the reliability-enhancement activities entail enormous costs, software quality models, based on the metrics collected early in the development life cycle, serve as handy tools for cost-effectively guiding such activities to the software modules that are likely to be faulty. Case-Based Reasoning (CBR) is an attractive technique for software quality modeling. Software Measurement Analysis and Reliability Toolkit (SMART) is a CBR tool customized for metrics-based software quality modeling. Developed for the NASA IV&V Facility, SMART supports three types of software quality models: quantitative quality prediction, classification, and module-order models. It also supports a goal-oriented selection of classification models. An empirical case study of a military command, control, and communication system demonstrates the accuracy and usefulness of SMART, and also serves as a user-guide for the tool.
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Date Issued: 2002
PURL: http://purl.flvc.org/fcla/dt/12967
Subject Headings: Software measurement, Computer software--Quality control, Case-based reasoning
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 at system and subsystem level with TREEDISC classification algorithm.
Creator: Liu, Jinxia., 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 are tools for detecting faults early in the software development process. In this research, the TREEDISC algorithm and a general classification rule were used to create classification tree models and predict software quality by classifying software modules as fault-prone or not fault-prone. Software metrics were collected from four consecutive releases of a very large legacy telecommunications system with six subsystems. Using release 1, four classification tree models...
Show moreSoftware quality models are tools for detecting faults early in the software development process. In this research, the TREEDISC algorithm and a general classification rule were used to create classification tree models and predict software quality by classifying software modules as fault-prone or not fault-prone. Software metrics were collected from four consecutive releases of a very large legacy telecommunications system with six subsystems. Using release 1, four classification tree models were built using raw metrics, and another four tree models were built using PCA metrics. Models were then selected based on release 2. Releases 3 and 4 were used to validate the selected model. Models that used PCA metrics were as good as or better than models that used raw metrics. This study also investigated the performance of classification tree models, when the subsystem identifier was included as a predictor.
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/12747
Subject Headings: Computer software--Quality control, Software measurement
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)

Title: Partitioning filter approach to noise elimination: An empirical study in software quality classification.
Creator: Rebours, Pierre., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This thesis presents two new noise filtering techniques which improve the quality of training datasets by removing noisy data. The training dataset is first split into subsets, and base learners are induced on each of these splits. The predictions are combined in such a way that an instance is identified as noisy if it is misclassified by a certain number of base learners. The Multiple-Partitioning Filter combines several classifiers on each split. The Iterative-Partitioning Filter only uses...
Show moreThis thesis presents two new noise filtering techniques which improve the quality of training datasets by removing noisy data. The training dataset is first split into subsets, and base learners are induced on each of these splits. The predictions are combined in such a way that an instance is identified as noisy if it is misclassified by a certain number of base learners. The Multiple-Partitioning Filter combines several classifiers on each split. The Iterative-Partitioning Filter only uses one base learner, but goes through multiple iterations. The amount of noise removed is varied by tuning the filtering level or the number of iterations. Empirical studies on a high assurance software project compare the effectiveness of our noise removal approaches with two other filters, the Cross-Validation Filter and the Ensemble Filter. Our studies suggest that using several base classifiers as well as performing several iterations with a conservative scheme may improve the efficiency of the filter.
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Date Issued: 2004
PURL: http://purl.flvc.org/fcla/dt/13110
Subject Headings: Software measurement, Computer software--Quality control, Decision trees, Recursive partitioning
Format: Document (PDF)

Software engineering (14)	+ -
Software measurement (13)	+ -
Case-based reasoning (3)	+ -
Decision trees (3)	+ -
Computer software--Development (2)	+ -

Genetic programming (Computer science) (2)	+ -
Combinatorial optimization (1)	+ -
Computer arithmetic (1)	+ -
Computer network architecture (1)	+ -
Computer simulation (1)	+ -
Computer software--Evaluation (1)	+ -
Computer software--Reliability (1)	+ -
Computer software--Reliability--Mathematical models (1)	+ -
Computer software--Testing (1)	+ -
Econometrics (1)	+ -
Fuzzy logic (1)	+ -
Fuzzy systems (1)	+ -
Information theory (1)	+ -
Modular programming (1)	+ -
Principal components analysis (1)	+ -
Recursive partitioning (1)	+ -
Regression analysis (1)	+ -
Software engineering--Management (1)	+ -
Software engineering--Quality control (1)	+ -
Software engineering--Standards (1)	+ -

College of Engineering and Computer Science (27)	+ -
Florida Atlantic University (27)	+ -
Khoshgoftaar, Taghi M. (27)	+ -
Seliya, Naeem A. (2)	+ -
Allen, Edward B. (1)	+ -

Bullard, Lofton A. (1)	+ -
Gao, Kehan (1)	+ -
Lanning, David Lee. (1)	+ -
Lim, Linda. (1)	+ -
Liu, Jinxia. (1)	+ -
Liu, Yi. (1)	+ -
Liu, Yongbin. (1)	+ -
Mao, Meihui. (1)	+ -
Mao, Wenlei. (1)	+ -
Naik, Archana B. (1)	+ -
Nguyen, Laurent Quoc Viet. (1)	+ -
Ponnuswamy, Viswanathan Kolathupalayam. (1)	+ -
Puppala, Kishore. (1)	+ -
Rajeevalochanam, Jayanth Munikote. (1)	+ -
Rebours, Pierre. (1)	+ -
Ross, Fletcher Douglas. (1)	+ -
Shan, Ruqun. (1)	+ -
Song, Huiming. (1)	+ -
Sundaresh, Nandini. (1)	+ -
Szabo, Robert Michael. (1)	+ -
Thaker, Vishal Kirit. (1)	+ -
Xiao, Yudong. (1)	+ -
Xu, Zhiwei. (1)	+ -
Yuan, Xiaojing (1)	+ -

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