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Title: Combining decision trees for software quality classification: An empirical study.
Creator: Geleyn, Erik., Florida Atlantic University, Khoshgoftaar, Taghi M.
Abstract/Description: The increased reliance on computer systems in the modern world has created a need for engineering reliability control of computer systems to the highest standards. Software quality classification models are one of the important tools to achieve high reliability. They can be used to calibrate software metrics-based models to predict whether software modules are fault-prone or not. Timely use of such models can aid in detecting faults early in the life cycle. Individual classifiers may be...
Show moreThe increased reliance on computer systems in the modern world has created a need for engineering reliability control of computer systems to the highest standards. Software quality classification models are one of the important tools to achieve high reliability. They can be used to calibrate software metrics-based models to predict whether software modules are fault-prone or not. Timely use of such models can aid in detecting faults early in the life cycle. Individual classifiers may be improved by using the combined decision from multiple classifiers. Several algorithms implement this concept and are investigated in this thesis. These combined learners provide the software quality modeling community with accurate, robust, and goal oriented models. This study presents a comprehensive comparative evaluation of meta learners using a strong and a weak learner, C4.5 and Decision Stump, respectively. Two case studies of industrial software systems are used in our empirical investigations.
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Date Issued: 2002
PURL: http://purl.flvc.org/fcla/dt/12898
Subject Headings: Computer software--Quality control, 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: 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 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: 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: An empirical study of a three-group classification model using case-based reasoning.
Creator: Bhupathiraju, Sajan S., Florida Atlantic University, Khoshgoftaar, Taghi M.
Abstract/Description: Reliability is becoming a very important and competitive factor for software-based products. Software metrics-based quality estimation models provide a systematic and scientific approach to detect software faults early in the life cycle, improving software reliability. Classification models for software quality estimation usually classify observations into two groups. This thesis presents an empirical study of an algorithm for software quality classification using three groups: Three-Group...
Show moreReliability is becoming a very important and competitive factor for software-based products. Software metrics-based quality estimation models provide a systematic and scientific approach to detect software faults early in the life cycle, improving software reliability. Classification models for software quality estimation usually classify observations into two groups. This thesis presents an empirical study of an algorithm for software quality classification using three groups: Three-Group Classification Model using Case-Based Reasoning (CBR). The basic idea behind the algorithm is that it uses the commonly used two-group classification technique three times. It can also be implemented with other quality estimation methods, such as Logistic Regression, Regression Trees, etc. This work evaluates the obtained quality with that from the Discriminant Analysis method. Empirical studies were conducted using an inspection data set, collected from a telecommunications system. It was observed that CBR performs better than Discriminant Analysis.
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Date Issued: 2002
PURL: http://purl.flvc.org/fcla/dt/12903
Subject Headings: Software measurement, Computer software--Quality control
Format: Document (PDF)

Title: An empirical study of a three-group software quality classification model.
Creator: Cherukuri, Reena., Florida Atlantic University, Khoshgoftaar, Taghi M.
Abstract/Description: Maintaining superior quality and reliability of software systems is an important issue in software reliability engineering. Software quality estimation models based on software metrics provide a systematic and scientific way to detect fault-prone modules and enable us to achieve high quality in software systems by focusing on high-risk modules within limited resources and budget. In previous works, classification models for software quality usually classified modules into two groups, fault...
Show moreMaintaining superior quality and reliability of software systems is an important issue in software reliability engineering. Software quality estimation models based on software metrics provide a systematic and scientific way to detect fault-prone modules and enable us to achieve high quality in software systems by focusing on high-risk modules within limited resources and budget. In previous works, classification models for software quality usually classified modules into two groups, fault-prone or not fault-prone. This thesis presents a new technique for classifying modules into three groups, i.e., high-risk, medium-risk, and low-risk groups. This new technique calibrates three-group models according to the resources available, which makes it different from other classification techniques. The proposed three-group classification method proved to be efficient and useful for resource utilization in software quality control.
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Date Issued: 2003
PURL: http://purl.flvc.org/fcla/dt/13004
Subject Headings: Software measurement, Computer software--Quality control
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 resource-based selection of rule-based software quality classification models.
Creator: Herzberg, Angela., Florida Atlantic University, Khoshgoftaar, Taghi M.
Abstract/Description: Software managers are under pressure to deliver reliable and high quality software, within a limited time and budget. To achieve this goal, they can be aided by different modeling techniques that allow them to predict the quality of software, so that the improvement efforts can be directed to software modules that are more likely to be fault-prone. Also, different projects have different resource availability constraints, and being able to select a model that is suitable for a specific...
Show moreSoftware managers are under pressure to deliver reliable and high quality software, within a limited time and budget. To achieve this goal, they can be aided by different modeling techniques that allow them to predict the quality of software, so that the improvement efforts can be directed to software modules that are more likely to be fault-prone. Also, different projects have different resource availability constraints, and being able to select a model that is suitable for a specific resource constraint allows software managers to direct enhancement techniques more effectively and efficiently. In our study, we use Rule-Based Modeling ( RBM) to predict the likelihood of a module being fault-prone and the Modified Expected Cost of Misclassification (MECM ) measure to select the models that are suitable, in the context of the given resource constraints. This empirical study validates MECM as a measure to select an appropriate RBM model.
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Date Issued: 2002
PURL: http://purl.flvc.org/fcla/dt/12968
Subject Headings: Software measurement, Computer software--Quality control
Format: Document (PDF)

Title: An empirical study of module order models.
Creator: Adipat, Boonlit., Florida Atlantic University, Khoshgoftaar, Taghi M.
Abstract/Description: Most software reliability approaches classify modules as fault-prone or not fault-prone by way of a predetermined threshold. However, it may not be practical to predefine a threshold because the amount of resources for reliability enhancement may be unknown. Therefore, a module-order model (MOM) predicting the rank order of modules can be used to solve this problem. The objective of this research is to make an empirical study of MOMs based on five different underlying quantitative software...
Show moreMost software reliability approaches classify modules as fault-prone or not fault-prone by way of a predetermined threshold. However, it may not be practical to predefine a threshold because the amount of resources for reliability enhancement may be unknown. Therefore, a module-order model (MOM) predicting the rank order of modules can be used to solve this problem. The objective of this research is to make an empirical study of MOMs based on five different underlying quantitative software quality models. We examine the benefits of principal components analysis with MOM and demonstrate that better accuracy of underlying techniques does not always yield better performance with MOM. Three case studies of large industrial software systems were conducted. The results confirm that MOM can create efficient models using different underlying techniques that provide various accuracy when predicting a quantitative software quality factor over the data sets.
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/12783
Subject Headings: Computer software--Quality control, Software measurement
Format: Document (PDF)

Title: Software fault prediction using tree-based models.
Creator: Seliya, Naeem A., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Maintaining superior quality and reliability in software systems is of utmost importance in today's world. Early fault prediction is a proven method for achieving this. Tree based modelling is a simple and effective method that can be used to predict the number of faults in a software system. In this thesis, we use regression tree based modelling to predict the number of faults in a software module. The goal of this study is four-fold. First, a comparative study of the tree based modelling...
Show moreMaintaining superior quality and reliability in software systems is of utmost importance in today's world. Early fault prediction is a proven method for achieving this. Tree based modelling is a simple and effective method that can be used to predict the number of faults in a software system. In this thesis, we use regression tree based modelling to predict the number of faults in a software module. The goal of this study is four-fold. First, a comparative study of the tree based modelling tools CART and S-PLUS. CART yielded simpler regression trees than those built by S-PLUS. Second, a comparative study of the least squares and the least absolute deviation methods of CART. It is shown that the latter yielded better results than the former. Third, a study of the possible benefits of using principal components analysis when performing regression tree modelling. The fourth and final study is a comparison of tree based modelling with other prediction techniques namely, Case Based Reasoning, Artificial Neural Networks and Multiple Linear Regression.
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/12782
Subject Headings: Software measurement, Computer software--Quality control
Format: Document (PDF)

Title: Software quality classification using rule-based modeling.
Creator: Mao, Meihui., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Software-based products are part of our daily life. They can be encountered in most of the systems we interact with. This reliance on software products generates a strong need for better software reliability, reducing the cost associated with potential failures. Reliability in software systems may be achieved by using additional testing. However, extensive software testing is expensive and time consuming. Software quality classification models provide an early prediction of a module's quality...
Show moreSoftware-based products are part of our daily life. They can be encountered in most of the systems we interact with. This reliance on software products generates a strong need for better software reliability, reducing the cost associated with potential failures. Reliability in software systems may be achieved by using additional testing. However, extensive software testing is expensive and time consuming. Software quality classification models provide an early prediction of a module's quality. Boolean Discriminant Function (BDF), Generalized Boolean Discriminant Function (GBDF), and Rule-Based Modeling (RBM) can be used as classification models. This thesis demonstrates the ability of GBDF and RBM to correctly classify modules. The introduction of the AND operator in the GBDF model and the customizable outcomes for the rules in RBM, enhanced the discriminating quality of GBDF and RBM as compared to BDF. Furthermore, they also yielded better balances for the misclassification rates.
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Date Issued: 2002
PURL: http://purl.flvc.org/fcla/dt/12886
Subject Headings: Computer software--Quality control, Software measurement
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: 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: 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: 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: 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: 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: 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: Three-group software quality classification modeling with TREEDISC algorithm.
Creator: Liu, Yongbin., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Maintaining superior quality and reliability of software systems is important nowadays. Software quality modeling detects fault-prone modules and enables us to achieve high quality in software system by focusing on fewer modules, because of limited resources and budget. Tree-based modeling is a simple and effective method that predicts the fault proneness in software systems. In this thesis, we introduce TREEDISC modeling technique with a three-group classification rule to predict the quality...
Show moreMaintaining superior quality and reliability of software systems is important nowadays. Software quality modeling detects fault-prone modules and enables us to achieve high quality in software system by focusing on fewer modules, because of limited resources and budget. Tree-based modeling is a simple and effective method that predicts the fault proneness in software systems. In this thesis, we introduce TREEDISC modeling technique with a three-group classification rule to predict the quality of software modules. A general classification rule is applied and validated. The three impact parameters, group number, minimum leaf size and significant level, are thoroughly evaluated. An optimization procedure is conducted and empirical results are presented. Conclusions about the impact factors as well as the robustness of our research are performed. TREEDISC modeling technique with three-group classification has proved to be an efficient and convincing method in software quality control.
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Date Issued: 2003
PURL: http://purl.flvc.org/fcla/dt/13008
Subject Headings: Computer software--Quality control, Software measurement, Decision trees
Format: Document (PDF)

Computer software--Quality control (40)	+ -
Software measurement (23)	+ -
Software engineering (16)	+ -
Case-based reasoning (3)	+ -
Computer software--Development (3)	+ -

Computer software--Evaluation (3)	+ -
Computer software--Testing (3)	+ -
Decision trees (3)	+ -
Acoustical engineering (2)	+ -
Computer interfaces--Software--Quality control (2)	+ -
Computer software--Reliability (2)	+ -
Expert systems (Computer science) (2)	+ -
Genetic programming (Computer science) (2)	+ -
Software documentation (2)	+ -
Bayesian statistical decision theory (1)	+ -
Biometric identification (1)	+ -
Client/server computing -- Security measures (1)	+ -
Cloud computing -- Security measures (1)	+ -
Combinatorial optimization (1)	+ -
Computational intelligence (1)	+ -
Computational learning theory (1)	+ -
Computer arithmetic (1)	+ -
Computer network architecture (1)	+ -
Computer simulation (1)	+ -
Computer software -- Quality control (1)	+ -
Computer software--Reliability--Mathematical models (1)	+ -
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