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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: 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: 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.
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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: Predicting decay in program modules of legacy software systems.
Creator: Joshi, Dhaval Kunvarabhai., Florida Atlantic University, Khoshgoftaar, Taghi M.
Abstract/Description: Legacy software systems may go through many releases. It is important to ensure that the reliability of a system improves with subsequent releases. Methods are needed to identify decaying software modules, i.e., modules for which quality decreases with each system release. Early identification of such modules during the software life cycle allows us to focus quality improvement efforts in a more productive manner, by reducing resources wasted for testing and improving the entire system. We...
Show moreLegacy software systems may go through many releases. It is important to ensure that the reliability of a system improves with subsequent releases. Methods are needed to identify decaying software modules, i.e., modules for which quality decreases with each system release. Early identification of such modules during the software life cycle allows us to focus quality improvement efforts in a more productive manner, by reducing resources wasted for testing and improving the entire system. We present a scheme to classify modules in three groups---Decayed, Improved, and Unchanged---based on a three-group software quality classification method. This scheme is applied to three different case studies, using a case-based reasoning three-group classification model. The model identifies decayed modules, and is validated over different releases. The main goal of this work is to focus on the evolution of program modules of a legacy software system to identify modules that are difficult to maintain and may need to be reengineered.
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Date Issued: 2002
PURL: http://purl.flvc.org/fcla/dt/12899
Subject Headings: Software reengineering, Computer software--Quality control, Software measurement, Software maintenance
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: Measurement of coupling and cohesion of software.
Creator: Chen, Ye., Florida Atlantic University, Khoshgoftaar, Taghi M.
Abstract/Description: Graphs are often used to depict an abstraction of software. A graph may be an abstraction of a software system and a subgraph may represent a software module. Coupling and cohesion are attributes that summarize the degree of interdependence or connectivity among subsystems or within subsystems, respectively. When used in conjunction with measures of other attributes, coupling and cohesion can contribute to an assessment or prediction of software quality. Information theory is attractive to us...
Show moreGraphs are often used to depict an abstraction of software. A graph may be an abstraction of a software system and a subgraph may represent a software module. Coupling and cohesion are attributes that summarize the degree of interdependence or connectivity among subsystems or within subsystems, respectively. When used in conjunction with measures of other attributes, coupling and cohesion can contribute to an assessment or prediction of software quality. Information theory is attractive to us because the design decisions embodied by the graph are information. Using information theory, we propose measures of the cohesion and coupling of a modular system and cohesion and coupling of each constituent module. These measures conform to the properties of cohesion and coupling defined by Briand, Morasca and Basili, applied to undirected graphs and therefore, are in the families of measures called cohesion and coupling.
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Date Issued: 2000
PURL: http://purl.flvc.org/fcla/dt/15760
Subject Headings: Information theory, Computer software--Evaluation, Software measurement
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)

Title: Classification of software quality using tree modeling with the S-Plus algorithm.
Creator: Deng, Jianyu., Florida Atlantic University, Khoshgoftaar, Taghi M.
Abstract/Description: In today's competitive environment for software products, quality has become an increasingly important asset to software development organizations. Software quality models are tools for focusing efforts to find faults early in the development. Delaying corrections can lead to higher costs. In this research, the classification tree modeling technique was used to predict the software quality by classifying program modules either as fault-prone or not fault-prone. The S-Plus regression tree...
Show moreIn today's competitive environment for software products, quality has become an increasingly important asset to software development organizations. Software quality models are tools for focusing efforts to find faults early in the development. Delaying corrections can lead to higher costs. In this research, the classification tree modeling technique was used to predict the software quality by classifying program modules either as fault-prone or not fault-prone. The S-Plus regression tree algorithm and a general classification rule were applied to yield classification tree models. Two classification tree models were developed based on four consecutive releases of a very large legacy telecommunications system. The first release was used as the training data set and the subsequent three releases were used as evaluation data sets. The first model used twenty-four product metrics and four execution metrics as candidate predictors. The second model added fourteen process metrics as candidate predictors.
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Date Issued: 1999
PURL: http://purl.flvc.org/fcla/dt/15707
Subject Headings: Computer software--Quality control, Software measurement, Computer software--Evaluation
Format: Document (PDF)

Title: Prediction of software quality using classification tree modeling.
Creator: Naik, Archana B., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Reliability of software systems is one of the major concerns in today's world as computers have really become an integral part of our lives. Society has become so dependent on reliable software systems that failures can be dangerous in terms of worsening a company's business, human relationships or affecting human lives. Software quality models are tools for focusing efforts to find faults early in the development. In this experiment, we used classification tree modeling techniques to predict...
Show moreReliability of software systems is one of the major concerns in today's world as computers have really become an integral part of our lives. Society has become so dependent on reliable software systems that failures can be dangerous in terms of worsening a company's business, human relationships or affecting human lives. Software quality models are tools for focusing efforts to find faults early in the development. In this experiment, we used classification tree modeling techniques to predict the software quality by classifying program modules either as fault-prone or not fault-prone. We introduced the Classification And Regression Trees (scCART) algorithm as a tool to generate classification trees. We focused our experiments on very large telecommunications system to build quality models using set of product and process metrics as independent variables.
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Date Issued: 1998
PURL: http://purl.flvc.org/fcla/dt/15600
Subject Headings: Computer software--Quality control, Computer software--Evaluation, Software measurement
Format: Document (PDF)

Title: Cost of misclassification in software quality models.
Creator: Guan, Xin., Florida Atlantic University, Khoshgoftaar, Taghi M.
Abstract/Description: Reliability has become a very important and competitive factor for software products. Using software quality models based on software measurements provides a systematic and scientific way to detect software faults early and to improve software reliability. This thesis considers several classification techniques including Generalized Classification Rule, MetaCost algorithm, Cost-Boosting algorithm and AdaCost algorithm. We also introduce the weighted logistic regression algorithm, and a new...
Show moreReliability has become a very important and competitive factor for software products. Using software quality models based on software measurements provides a systematic and scientific way to detect software faults early and to improve software reliability. This thesis considers several classification techniques including Generalized Classification Rule, MetaCost algorithm, Cost-Boosting algorithm and AdaCost algorithm. We also introduce the weighted logistic regression algorithm, and a new method to evaluate the performance of classification models---ROC Analysis. We focus our experiments on a very large legacy telecommunications system (LLTS) to build software quality models with principal components analysis. Two other data sets, CCCS and LTS are also used in our experiments.
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Date Issued: 2000
PURL: http://purl.flvc.org/fcla/dt/15762
Subject Headings: Computer software--Quality control, Software measurement, Computer software--Testing
Format: Document (PDF)

Software measurement (26)	+ -
Computer software--Quality control (23)	+ -
Computer software--Evaluation (5)	+ -
Software engineering (3)	+ -
Computer networks--Security measures (2)	+ -

Computer security (2)	+ -
Decision trees (2)	+ -
Software architecture (2)	+ -
Access control (1)	+ -
Android (Electronic resource)--Security measures (1)	+ -
Application software -- Security measures (1)	+ -
Bayesian statistical decision theory (1)	+ -
Biometric identification (1)	+ -
Case-based reasoning (1)	+ -
Client/server computing -- Security measures (1)	+ -
Cloud computing -- Security measures (1)	+ -
Cloud computing--Security measures (1)	+ -
Computational intelligence (1)	+ -
Computer architecture (1)	+ -
Computer architecture. (1)	+ -
Computer modeling (1)	+ -
Computer network protocols (1)	+ -
Computer network protocols. (1)	+ -
Computer networks (Security measures) (1)	+ -
Computer networks--Access control (1)	+ -
Computer networks--Security measures. (1)	+ -
Computer software -- Quality control (1)	+ -
Computer software--Development (1)	+ -
Computer software--Development. (1)	+ -
Computer software--Reliability--Mathematical models (1)	+ -

Florida Atlantic University (34)	+ -
Khoshgoftaar, Taghi M. (26)	+ -
College of Engineering and Computer Science (19)	+ -
Department of Computer and Electrical Engineering and Computer Science (19)	+ -
Fernandez, Eduardo B. (5)	+ -

Seliya, Naeem A. (2)	+ -
Adipat, Boonlit. (1)	+ -
Alkazimi, Ali (1)	+ -
Berkovich, Yevgeniy. (1)	+ -
Bhupathiraju, Sajan S. (1)	+ -
Charles E. Schmidt College of Science (1)	+ -
Chen, Ye. (1)	+ -
Cherukuri, Reena. (1)	+ -
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