View All Items

Title: Software metrics collection: Two new research tools.
Creator: Jordan, Sylviane G., Florida Atlantic University, Khoshgoftaar, Taghi M.
Abstract/Description: Collecting software metrics manually could be a tedious, inaccurate, and subjective task. Two new tools were developed to automate this process in a rapid, accurate, and objective way. The first tool, the Metrics Analyzer, evaluates 19 metrics at the function level, from complete or partial systems written in C. The second tool, the Call Graph Generator, does not assess a metric directly, but generates a call graph based on a complete or partial system written in C. The call graph is used as...
Show moreCollecting software metrics manually could be a tedious, inaccurate, and subjective task. Two new tools were developed to automate this process in a rapid, accurate, and objective way. The first tool, the Metrics Analyzer, evaluates 19 metrics at the function level, from complete or partial systems written in C. The second tool, the Call Graph Generator, does not assess a metric directly, but generates a call graph based on a complete or partial system written in C. The call graph is used as an input to another tool (not considered here) that measures the coupling of a module, such as a function or a file. A case study analyzed the relationships among the metrics, including the coupling metric, using principal component analysis, which transformed the 19 metrics into eight principal components.
Show less
Date Issued: 1997
PURL: http://purl.flvc.org/fcla/dt/15483
Subject Headings: Software measurement, Computer software--Development, Computer software--Evaluation
Format: Document (PDF)

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.
Show less
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.
Show less
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.
Show less
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.
Show less
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.
Show less
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.
Show less
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.
Show less
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.
Show less
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.
Show less
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.
Show less
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.
Show less
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.
Show less
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.
Show less
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: Choosing software reliability models.
Creator: Woodcock, Timothy G., Florida Atlantic University, Khoshgoftaar, Taghi M.
Abstract/Description: One of the important problems which software engineers face is how to determine which software reliability model should be used for a particular system. Some recent attempts to compare different models used complementary graphical and analytical techniques. These techniques require an excessive amount of time for plotting the data and running the analyses, and they are still rather subjective as to which model is best. So another technique needs to be found that is simpler and yet yields a...
Show moreOne of the important problems which software engineers face is how to determine which software reliability model should be used for a particular system. Some recent attempts to compare different models used complementary graphical and analytical techniques. These techniques require an excessive amount of time for plotting the data and running the analyses, and they are still rather subjective as to which model is best. So another technique needs to be found that is simpler and yet yields a less subjective measure of goodness of fit. The Akaike Information Criterion (AIC) is proposed as a new approach for selecting the best model. The performance of AIC is measured by Monte-Carlo simulation and by comparison to published data sets. The AIC chooses the correct model 95% of the time.
Show less
Date Issued: 1989
PURL: http://purl.flvc.org/fcla/dt/14561
Subject Headings: Computer software--Testing, Computer software--Reliability
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.
Show less
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: 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.
Show less
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: An empirical study of source code complexity and source code modifications during testing and maintenance.
Creator: De Gramont, Anne H., Florida Atlantic University, Khoshgoftaar, Taghi M.
Abstract/Description: Since maintenance is the most expensive phase of the software life cycle, detecting most of the errors as early as possible in the software development effort can provide substantial savings. This study investigates the behavior of complexity metrics during testing and maintenance, and their relationship to modifications made to the software. Interface complexity causes most of the change activities during integration testing and maintenance, while size causes most of the changes during unit...
Show moreSince maintenance is the most expensive phase of the software life cycle, detecting most of the errors as early as possible in the software development effort can provide substantial savings. This study investigates the behavior of complexity metrics during testing and maintenance, and their relationship to modifications made to the software. Interface complexity causes most of the change activities during integration testing and maintenance, while size causes most of the changes during unit testing. Principal component analysis groups 16 complexity metrics into four domains. Changes in domain pattern are observed throughout the software life cycle. Using those domains as input, regression analysis shows that software complexity measures collected as early as the unit testing phase can identify and predict change prone modules. With a low rate of misclassification, discriminant analysis further confirms that complexity metrics provide a strong indication of the changes made to a module during testing and maintenance.
Show less
Date Issued: 1994
PURL: http://purl.flvc.org/fcla/dt/15089
Subject Headings: Computer software--Development, Software maintenance, Source code (Computer science)
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.
Show less
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: Rough Set-Based Software Quality Models and Quality of Data.
Creator: Bullard, Lofton A., Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In this dissertation we address two significant issues of concern. These are software quality modeling and data quality assessment. Software quality can be measured by software reliability. Reliability is often measured in terms of the time between system failures. A failure is caused by a fault which is a defect in the executable software product. The time between system failures depends both on the presence and the usage pattern of the software. Finding faulty components in the development...
Show moreIn this dissertation we address two significant issues of concern. These are software quality modeling and data quality assessment. Software quality can be measured by software reliability. Reliability is often measured in terms of the time between system failures. A failure is caused by a fault which is a defect in the executable software product. The time between system failures depends both on the presence and the usage pattern of the software. Finding faulty components in the development cycle of a software system can lead to a more reliable final system and will reduce development and maintenance costs. The issue of software quality is investigated by proposing a new approach, rule-based classification model (RBCM) that uses rough set theory to generate decision rules to predict software quality. The new model minimizes over-fitting by balancing the Type I and Type II niisclassiflcation error rates. We also propose a model selection technique for rule-based models called rulebased model selection (RBMS). The proposed rule-based model selection technique utilizes the complete and partial matching rule sets of candidate RBCMs to determine the model with the least amount of over-fitting. In the experiments that were performed, the RBCMs were effective at identifying faulty software modules, and the RBMS technique was able to identify RBCMs that minimized over-fitting. Good data quality is a critical component for building effective software quality models. We address the significance of the quality of data on the classification performance of learners by conducting a comprehensive comparative study. Several trends were observed in the experiments. Class and attribute had the greatest impact on the performance of learners when it occurred simultaneously in the data. Class noise had a significant impact on the performance of learners, while attribute noise had no impact when it occurred in less than 40% of the most significant independent attributes. Random Forest (RF100), a group of 100 decision trees, was the most, accurate and robust learner in all the experiments with noisy data.
Show less
Date Issued: 2008
PURL: http://purl.flvc.org/fau/fd/FA00012567
Subject Headings: Computer software--Quality control, Computer software--Reliability, Software engineering, Computer arithmetic
Format: Document (PDF)

Computer software--Quality control (40)	+ -
Software engineering (29)	+ -
Software measurement (26)	+ -
Computer software--Development (17)	+ -
Development (9)	+ -

Application software (8)	+ -
Computer software--Evaluation (8)	+ -
Computer software (6)	+ -
Computer software--Reliability (6)	+ -
Expert systems (Computer science) (6)	+ -
Object-oriented programming (Computer science) (6)	+ -
Fault-tolerant computing (5)	+ -
Case-based reasoning (4)	+ -
Computer software--Testing (4)	+ -
Neural networks (Computer science) (4)	+ -
Software architecture (4)	+ -
System design (4)	+ -
Application software--Development (3)	+ -
Computer architecture (3)	+ -
Computer network architectures (3)	+ -
Computer security (3)	+ -
Computer-aided design (3)	+ -
Data encryption (Computer science) (3)	+ -
Data mining (3)	+ -
Decision trees (3)	+ -
Mobile communication systems (3)	+ -
Mobile computing (3)	+ -
Software engineering--Management (3)	+ -
Software maintenance (3)	+ -
Acoustical engineering (2)	+ -

Android (Electronic resource). (2)	+ -
Android. (1)	+ -

Florida Atlantic University (102)	+ -
Department of Computer and Electrical Engineering and Computer Science (75)	+ -
College of Engineering and Computer Science (74)	+ -
Khoshgoftaar, Taghi M. (50)	+ -
Fernandez, Eduardo B. (12)	+ -

Charles E. Schmidt College of Science (4)	+ -
Coulter, Neal S. (4)	+ -
Huang, Shihong (4)	+ -
Larrondo-Petrie, Maria M. (3)	+ -
Pandya, Abhijit S. (3)	+ -
Wu, Jie (3)	+ -
Alhazmi, Alhejab Shawqi (2)	+ -
An, Edgar (2)	+ -
Buckley, Ingrid A. (2)	+ -
Bullard, Lofton A. (2)	+ -
Department of Mathematical Sciences (2)	+ -
Furht, Borko (2)	+ -
Levow, Roy B. (2)	+ -
Mulcahy, James J. (2)	+ -
Seliya, Naeem A. (2)	+ -
Shankar, Ravi (2)	+ -
Solomon, Martin K. (2)	+ -
Woodcock, Timothy G. (2)	+ -
Zhu, Xingquan (2)	+ -
Adipat, Boonlit. (1)	+ -
Akhter, Shahina. (1)	+ -
Alkazimi, Ali (1)	+ -
Allen, Edward B. (1)	+ -
Bayard, Gildas Yann. (1)	+ -
Berkovich, Yevgeniy. (1)	+ -

Search In

Pages

Pages

Sort Results By:

Narrow results by:

Collection

Topical Subject

Title as Subject

Genre

Type of Resource

Language

Creator

Format

Owner