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- 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.
Show less - 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
- 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
- Partitioning filter approach to noise elimination: An empirical study in software quality classification.
- Creator
- Rebours, Pierre., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
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This thesis presents two new noise filtering techniques which improve the quality of training datasets by removing noisy data. The training dataset is first split into subsets, and base learners are induced on each of these splits. The predictions are combined in such a way that an instance is identified as noisy if it is misclassified by a certain number of base learners. The Multiple-Partitioning Filter combines several classifiers on each split. The Iterative-Partitioning Filter only uses...
Show moreThis thesis presents two new noise filtering techniques which improve the quality of training datasets by removing noisy data. The training dataset is first split into subsets, and base learners are induced on each of these splits. The predictions are combined in such a way that an instance is identified as noisy if it is misclassified by a certain number of base learners. The Multiple-Partitioning Filter combines several classifiers on each split. The Iterative-Partitioning Filter only uses one base learner, but goes through multiple iterations. The amount of noise removed is varied by tuning the filtering level or the number of iterations. Empirical studies on a high assurance software project compare the effectiveness of our noise removal approaches with two other filters, the Cross-Validation Filter and the Ensemble Filter. Our studies suggest that using several base classifiers as well as performing several iterations with a conservative scheme may improve the efficiency of the filter.
Show less - Date Issued
- 2004
- PURL
- http://purl.flvc.org/fcla/dt/13110
- Subject Headings
- Software measurement, Computer software--Quality control, Decision trees, Recursive partitioning
- Format
- Document (PDF)
- 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.
Show less - 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
- A metrics-based software quality modeling tool.
- Creator
- Rajeevalochanam, Jayanth Munikote., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
In today's world, high reliability has become an essential component of almost every software system. However, since the reliability-enhancement activities entail enormous costs, software quality models, based on the metrics collected early in the development life cycle, serve as handy tools for cost-effectively guiding such activities to the software modules that are likely to be faulty. Case-Based Reasoning (CBR) is an attractive technique for software quality modeling. Software Measurement...
Show moreIn today's world, high reliability has become an essential component of almost every software system. However, since the reliability-enhancement activities entail enormous costs, software quality models, based on the metrics collected early in the development life cycle, serve as handy tools for cost-effectively guiding such activities to the software modules that are likely to be faulty. Case-Based Reasoning (CBR) is an attractive technique for software quality modeling. Software Measurement Analysis and Reliability Toolkit (SMART) is a CBR tool customized for metrics-based software quality modeling. Developed for the NASA IV&V Facility, SMART supports three types of software quality models: quantitative quality prediction, classification, and module-order models. It also supports a goal-oriented selection of classification models. An empirical case study of a military command, control, and communication system demonstrates the accuracy and usefulness of SMART, and also serves as a user-guide for the tool.
Show less - Date Issued
- 2002
- PURL
- http://purl.flvc.org/fcla/dt/12967
- Subject Headings
- Software measurement, Computer software--Quality control, Case-based reasoning
- Format
- Document (PDF)
- Title
- 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 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
- 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).
Show less - 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
- 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
- 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
- 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
- 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
- Software quality modeling and analysis with limited or without defect data.
- 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
-
The key to developing high-quality software is the measurement and modeling of software quality. In practice, software measurements are often used as a resource to model and comprehend the quality of software. The use of software measurements to understand quality is accomplished by a software quality model that is trained using software metrics and defect data of similar, previously developed, systems. The model is then applied to estimate quality of the target software project. Such an...
Show moreThe key to developing high-quality software is the measurement and modeling of software quality. In practice, software measurements are often used as a resource to model and comprehend the quality of software. The use of software measurements to understand quality is accomplished by a software quality model that is trained using software metrics and defect data of similar, previously developed, systems. The model is then applied to estimate quality of the target software project. Such an approach assumes that defect data is available for all program modules in the training data. Various practical issues can cause an unavailability or limited availability of defect data from the previously developed systems. This dissertation presents innovative and practical techniques for addressing the problem of software quality analysis when there is limited or completely absent defect data. The proposed techniques for software quality analysis without defect data include an expert-based approach with unsupervised clustering and an expert-based approach with semi-supervised clustering. The proposed techniques for software quality analysis with limited defect data includes a semi-supervised classification approach with the Expectation-Maximization algorithm and an expert-based approach with semi-supervised clustering. Empirical case studies of software measurement datasets obtained from multiple NASA software projects are used to present and evaluate the different techniques. The empirical results demonstrate the attractiveness, benefit, and definite promise of the proposed techniques. The newly developed techniques presented in this dissertation is invaluable to the software quality practitioner challenged by the absence or limited availability of defect data from previous software development experiences.
Show less - Date Issued
- 2005
- PURL
- http://purl.flvc.org/fcla/dt/12151
- Subject Headings
- Software measurement, Computer software--Quality control, Computer software--Reliability--Mathematical models, Software engineering--Quality control
- Format
- Document (PDF)