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- Title
- CBR-based software quality models and quality of data.
- Creator
- Xiao, Yudong., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
The performance accuracy of software quality estimation models is influenced by several factors, including the following two important factors: performance of the prediction algorithm and the quality of data. This dissertation addresses these two factors, and consists of two components: (1) a proposed genetic algorithm (GA) based optimization of software quality models for accuracy enhancement, and (2) a proposed partitioning- and rule-based filter (PRBF) for noise detection toward...
Show moreThe performance accuracy of software quality estimation models is influenced by several factors, including the following two important factors: performance of the prediction algorithm and the quality of data. This dissertation addresses these two factors, and consists of two components: (1) a proposed genetic algorithm (GA) based optimization of software quality models for accuracy enhancement, and (2) a proposed partitioning- and rule-based filter (PRBF) for noise detection toward improvement of data quality. We construct a generalized framework of our embedded GA-optimizer, and instantiate the GA-optimizer for three optimization problems in software quality engineering: parameter optimization for case-based reasoning (CBR) models; module rank optimization for module-order modeling (MOM); and structural optimization for our multi-strategy classification modeling approach, denoted RB2CBL. Empirical case studies using software measurement data from real-world software systems were performed for the optimization problems. The GA-optimization approaches improved software quality prediction accuracy, highlighting the practical benefits of using GA for solving optimization problems in software engineering. The proposed noise detection approach, PRBF, was empirically evaluated using data categorized into two classes. Empirical studies on artificially corrupted datasets and datasets with known (natural) noise demonstrated that PRBF can effectively detect both artificial and natural noise. The proposed filter is a stable and robust technique, and always provided optimal or near-optimal noise detection results. In addition, it is applicable on datasets with nominal and numerical attributes, as well as those with missing values. The PRBF technique supports two methods of noise detection: class noise detection and cost-sensitive noise detection. The former is an easy-to-use method and does not need parameter settings, while the latter is suited for applications where each class has a specific misclassification cost. PRBF can also be used iteratively to investigate the two general types of data noise: attribute and class noise.
Show less - Date Issued
- 2005
- PURL
- http://purl.flvc.org/fcla/dt/12141
- Subject Headings
- Computer software--Quality control, Genetic programming (Computer science), Software engineering, Case-based reasoning, Combinatorial optimization, Computer network architecture
- Format
- Document (PDF)
- Title
- Classification of software quality using Bayesian belief networks.
- Creator
- Dong, Yuhong., 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 Bayesian Networks modelling technique was used to predict the software quality by classifying program modules either as fault-prone or not fault-prone. A general...
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 Bayesian Networks modelling technique was used to predict the software quality by classifying program modules either as fault-prone or not fault-prone. A general classification rule was applied to yield classification Bayesian Belief Network models. Six classification Bayesian Belief Network models were developed based on quality metrics data records of two very large window application systems. The fit data set was used to build the model and the test data set was used to evaluate the model. The first two models used median based data cluster technique, the second two models used median as critical value to cluster metrics using Generalized Boolean Discriminant Function and the third two models used Kolniogorov-Smirnov test to select the critical value to cluster metrics using Generalized Boolean Discriminant Function; All six models used the product metrics (FAULT or CDCHURN) as predictors.
Show less - Date Issued
- 2002
- PURL
- http://purl.flvc.org/fcla/dt/12918
- Subject Headings
- Computer software--Quality control, Software measurement, Bayesian statistical decision theory
- 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.
Show less - 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
- 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
- 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
- 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
- A comparative study of attribute selection techniques for CBR-based software quality classification models.
- Creator
- Nguyen, Laurent Quoc Viet., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
To achieve high reliability in software-based systems, software metrics-based quality classification models have been explored in the literature. However, the collection of software metrics may be a hard and long process, and some metrics may not be helpful or may be harmful to the classification models, deteriorating the models' accuracies. Hence, methodologies have been developed to select the most significant metrics in order to build accurate and efficient classification models. Case...
Show moreTo achieve high reliability in software-based systems, software metrics-based quality classification models have been explored in the literature. However, the collection of software metrics may be a hard and long process, and some metrics may not be helpful or may be harmful to the classification models, deteriorating the models' accuracies. Hence, methodologies have been developed to select the most significant metrics in order to build accurate and efficient classification models. Case-Based Reasoning is the classification technique used in this thesis. Since it does not provide any metric selection mechanisms, some metric selection techniques were studied. In the context of CBR, this thesis presents a comparative evaluation of metric selection methodologies, for raw and discretized data. Three attribute selection techniques have been studied: Kolmogorov-Smirnov Two-Sample Test, Kruskal-Wallis Test, and Information Gain. These techniques resulted in classification models that are useful for software quality improvement.
Show less - Date Issued
- 2002
- PURL
- http://purl.flvc.org/fcla/dt/12944
- Subject Headings
- Case-based reasoning, Software engineering, Computer software--Quality control
- Format
- Document (PDF)
- Title
- A comprehensive comparative study of multiple classification techniques for software quality estimation.
- Creator
- Puppala, Kishore., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Reliability and quality are desired features in industrial software applications. In some cases, they are absolutely essential. When faced with limited resources, software project managers will need to allocate such resources to the most fault prone areas. The ability to accurately classify a software module as fault-prone or not fault-prone enables the manager to make an informed resource allocation decision. An accurate quality classification avoids wasting resources on modules that are not...
Show moreReliability and quality are desired features in industrial software applications. In some cases, they are absolutely essential. When faced with limited resources, software project managers will need to allocate such resources to the most fault prone areas. The ability to accurately classify a software module as fault-prone or not fault-prone enables the manager to make an informed resource allocation decision. An accurate quality classification avoids wasting resources on modules that are not fault-prone. It also avoids missing the opportunity to correct faults relatively early in the development cycle, when they are less costly. This thesis seeks to introduce the classification algorithms (classifiers) that are implemented in the WEKA software tool. WEKA (Waikato Environment for Knowledge Analysis) was developed at the University of Waikato in New Zealand. An empirical investigation is performed using a case study at a real-world system.
Show less - Date Issued
- 2003
- PURL
- http://purl.flvc.org/fcla/dt/13039
- Subject Headings
- Software engineering, Computer software--Quality control, Decision trees
- Format
- Document (PDF)
- Title
- 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.
Show less - 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)
- Title
- Count models for software quality estimation.
- Creator
- Gao, Kehan, Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
The primary aim of software engineering is to produce quality software that is delivered on time, within budget, and fulfils all its requirements. A timely estimation of software quality can serve as a prerequisite in achieving high reliability of software-based systems. More specifically, software quality assurance efforts can be prioritized for targeting program modules that are most likely to have a high number of faults. Software quality estimation models are generally of two types: a...
Show moreThe primary aim of software engineering is to produce quality software that is delivered on time, within budget, and fulfils all its requirements. A timely estimation of software quality can serve as a prerequisite in achieving high reliability of software-based systems. More specifically, software quality assurance efforts can be prioritized for targeting program modules that are most likely to have a high number of faults. Software quality estimation models are generally of two types: a classification model that predicts the class membership of modules into two or more quality-based classes, and a quantitative prediction model that estimates the number of faults (or some other software quality factor) that are likely to occur in software modules. In the literature, a variety of techniques have been developed for software quality estimation, most of which are suited for either prediction or classification but not for both, e.g., the multiple linear regression (only for prediction) and logistic regression (only for classification).
Show less - Date Issued
- 2003
- PURL
- http://purl.flvc.org/fcla/dt/12042
- Subject Headings
- Computer software--Quality control, Software engineering, Econometrics, Regression analysis
- Format
- Document (PDF)
- Title
- Developing accurate software quality models using a faster, easier, and cheaper method.
- Creator
- Lim, Linda., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Managers of software development need to know which components of a system are fault-prone. If this can be determined early in the development cycle then resources can be more effectively allocated and significant costs can be reduced. Case-Based Reasoning (CBR) is a simple and efficient methodology for building software quality models that can provide early information to managers. Our research focuses on two case studies. The first study analyzes source files and classifies them as fault...
Show moreManagers of software development need to know which components of a system are fault-prone. If this can be determined early in the development cycle then resources can be more effectively allocated and significant costs can be reduced. Case-Based Reasoning (CBR) is a simple and efficient methodology for building software quality models that can provide early information to managers. Our research focuses on two case studies. The first study analyzes source files and classifies them as fault-prone or not fault-prone. It also predicts the number of faults in each file. The second study analyzes the fault removal process, and creates models that predict the outcome of software inspections.
Show less - Date Issued
- 2001
- PURL
- http://purl.flvc.org/fcla/dt/12746
- Subject Headings
- Computer software--Development, Computer software--Quality control, Software engineering
- Format
- Document (PDF)
- Title
- An empirical study of 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 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
- An empirical study of combining techniques in software quality classification.
- Creator
- Eroglu, Cemal., Florida Atlantic University, Khoshgoftaar, Taghi M.
- Abstract/Description
-
In the literature, there has been limited research that systematically investigates the possibility of exercising a hybrid approach by simply learning from the output of numerous base-level learners. We analyze a hybrid learning approach upon the systems that had previously been worked with twenty-four different classifiers. Instead of relying on only one classifier's judgment, it is expected that taking into account the opinions of several learners is a wise decision. Moreover, by using...
Show moreIn the literature, there has been limited research that systematically investigates the possibility of exercising a hybrid approach by simply learning from the output of numerous base-level learners. We analyze a hybrid learning approach upon the systems that had previously been worked with twenty-four different classifiers. Instead of relying on only one classifier's judgment, it is expected that taking into account the opinions of several learners is a wise decision. Moreover, by using clustering techniques some base-level classifiers were eliminated from the hybrid learner input. We had three different experiments each with a different number of base-level classifiers. We empirically show that the hybrid learning approach generally yields better performance than the best selected base-level learners and majority voting under some conditions.
Show less - Date Issued
- 2004
- PURL
- http://purl.flvc.org/fcla/dt/13162
- Subject Headings
- Computer software--Testing, Computer software--Quality control, Computational learning theory, Machine learning, Digital computer simulation
- 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
- 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
- Fuzzy logic techniques for software reliability engineering.
- Creator
- Xu, Zhiwei., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Modern people are becoming more and more dependent on computers in their daily lives. Most industries, from automobile, avionics, oil, and telecommunications to banking, stocks, and pharmaceuticals, require computers to function. As the tasks required become more complex, the complexity of computer software and hardware has increased dramatically. As a consequence, the possibility of failure increases. As the requirements for and dependence on computers increases, the possibility of crises...
Show moreModern people are becoming more and more dependent on computers in their daily lives. Most industries, from automobile, avionics, oil, and telecommunications to banking, stocks, and pharmaceuticals, require computers to function. As the tasks required become more complex, the complexity of computer software and hardware has increased dramatically. As a consequence, the possibility of failure increases. As the requirements for and dependence on computers increases, the possibility of crises caused by computer failures also increases. High reliability is an important attribute for almost any software system. Consequently, software developers are seeking ways to forecast and improve quality before release. Since many quality factors cannot be measured until after the software becomes operational, software quality models are developed to predict quality factors based on measurements collected earlier in the life cycle. Due to incomplete information in the early life cycle of software development, software quality models with fuzzy characteristics usually perform better because fuzzy concepts deal with phenomenon that is vague in nature. This study focuses on the usage of fuzzy logic in software reliability engineering. Discussing will include the fuzzy expert systems and the application of fuzzy expert systems in early risk assessment; introducing the interval prediction using fuzzy regression modeling; demonstrating fuzzy rule extraction for fuzzy classification and its usage in software quality models; demonstrating the fuzzy identification, including extraction of both rules and membership functions from fuzzy data and applying the technique to software project cost estimations. The following methodologies were considered: nonparametric discriminant analysis, Z-test and paired t-test, neural networks, fuzzy linear regression, fuzzy nonlinear regression, fuzzy classification with maximum matched method, fuzzy identification with fuzzy clustering, and fuzzy projection. Commercial software systems and the COCOMO database are used throughout this dissertation to demonstrate the usefulness of concepts and to validate new ideas.
Show less - Date Issued
- 2001
- PURL
- http://purl.flvc.org/fcla/dt/11948
- Subject Headings
- Software engineering, Fuzzy logic, Computer software--Quality control, Fuzzy systems
- Format
- Document (PDF)
- Title
- Implementation of a three-group classification model using case-based reasoning.
- Creator
- Song, Huiming., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Reliability is becoming a very important and competitive factor for software products. Software quality models based on software metrics provide a systematic and scientific way to detect software faults early and to improve software reliability. Classification models for software quality usually classify observations using two groups. This thesis presents a new algorithm for classification using three groups, i.e., Three-Group Classification Model using Case Based Reasoning. The basic idea...
Show moreReliability is becoming a very important and competitive factor for software products. Software quality models based on software metrics provide a systematic and scientific way to detect software faults early and to improve software reliability. Classification models for software quality usually classify observations using two groups. This thesis presents a new algorithm for classification using three groups, i.e., Three-Group Classification Model using Case Based Reasoning. The basic idea behind the algorithm is that it uses the commonly used two-group classification method three times. This algorithm can be implemented with other techniques such as logistic regression, classification tree models, etc. This work compares its quality with the Discriminant Analysis method. We find that our new method performs much better than Discriminant Analysis. We also show that the addition of object-oriented software measures yielded a model that a practitioner may actually prefer over the simpler procedural measures model.
Show less - Date Issued
- 2001
- PURL
- http://purl.flvc.org/fcla/dt/12816
- Subject Headings
- Software measurement, Computer software--Quality control
- Format
- Document (PDF)