Fault Prediction Modeling For The Prediction Of Number Of Software Faults
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Fault Prediction Modeling For The Prediction Of Number Of Software Faults
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Author : Santosh Singh Rathore
language : en
Publisher: Springer
Release Date : 2019-04-03
Fault Prediction Modeling For The Prediction Of Number Of Software Faults written by Santosh Singh Rathore and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-03 with Computers categories.
This book addresses software faults—a critical issue that not only reduces the quality of software, but also increases their development costs. Various models for predicting the fault-proneness of software systems have been proposed; however, most of them provide inadequate information, limiting their effectiveness. This book focuses on the prediction of number of faults in software modules, and provides readers with essential insights into the generalized architecture, different techniques, and state-of-the art literature. In addition, it covers various software fault datasets and issues that crop up when predicting number of faults. A must-read for readers seeking a “one-stop” source of information on software fault prediction and recent research trends, the book will especially benefit those interested in pursuing research in this area. At the same time, it will provide experienced researchers with a valuable summary of the latest developments.
Software Fault Prediction
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Author : Sandeep Kumar
language : en
Publisher: Springer
Release Date : 2018-06-06
Software Fault Prediction written by Sandeep Kumar and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-06 with Computers categories.
This book focuses on exploring the use of software fault prediction in building reliable and robust software systems. It is divided into the following chapters: Chapter 1 presents an introduction to the study and also introduces basic concepts of software fault prediction. Chapter 2 explains the generalized architecture of the software fault prediction process and discusses its various components. In turn, Chapter 3 provides detailed information on types of fault prediction models and discusses the latest literature on each model. Chapter 4 describes the software fault datasets and diverse issues concerning fault datasets when building fault prediction models. Chapter 5 presents a study evaluating different techniques on the basis of their performance for software fault prediction. Chapter 6 presents another study evaluating techniques for predicting the number of faults in the software modules. In closing, Chapter 7 provides a summary of the topics discussed. The book will be of immense benefit to all readers who are interested in starting research in this area. In addition, it offers experienced researchers a valuable overview of the latest work in this area.
Fault Prediction Approach
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Author : Dhana Laxmi
language : en
Publisher:
Release Date : 2019-03-28
Fault Prediction Approach written by Dhana Laxmi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-28 with categories.
Project Report from the year 2019 in the subject Computer Science - Software, grade: A, course: Doctoral Degree, language: English, abstract: This research works seeks to explore and provide an improved fault detection approach for inspection and fault detection. It systematically investigate and characterize software faults and faults to improve fault detection and prevention mechanisms in the quality software development process. Firstly, it contributes an Adaptive PSO-based association rule mining techniques for software fault classification using ANN. This task categorizes real defects by finding the best support and reliability to have the best policy for software fault classification using ANN. Secondly, it provides a Fault Prediction Approach (FPA) based on probabilistic models to perform software testing in Software Inspection. This describes a cost-effective way to accurately detect the defects by performing software inspection to develop quality software. The proposed FPA probes stochastic methods using the modified Naive Bayes classification to estimate the possible faults in the experimental environment to suggest novel defect control development. Software reliability engineering has become very important as the complexity of the system has increased exponentially with technological advances. The fact that all systems today depend on many other systems and interfaces is not only an application error but also a number of environmental factors that lead to failure. The impact of these failures depends on the nature of the system, but many of them cause customer dissatisfaction and business loss. System testing and fault detection have become the most important processes in the software life cycle. Various failure prediction models can be analyzed and suggested so that failures can be detected at an early stage and many test efforts can be saved. Software development has many defects in the design phase. In the past, many examples of software development
Defect Prediction In Software Development Maintainence
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Author : Rudra Kumar
language : en
Publisher: Partridge Publishing
Release Date : 2018-04-11
Defect Prediction In Software Development Maintainence written by Rudra Kumar and has been published by Partridge Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-11 with Computers categories.
This book is a collection of taxonomy and review of contemporary model in the field of software development and maintenance. This book is basically the result of our passion toward the research of application of software engineering concepts. This work is derived from the need for accurate fault estimation in goals of quality programming and minimal maintenance overheads. State of art technologies have been discussed with respective experimental investigations and analysis. This work started out as a survey and then evolved according to our interest and proclivity into a work that emphasizes the aspects of software development. This book is intended to explain how the defect predictions are used to improve the quality of software development for easy analysis in a very simple way. It contains research that is useful to research scholars, engineers, and computing researchers.
The Cold Start Problem In Software Fault Prediction
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Author : Inbal Roshanski
language : en
Publisher:
Release Date : 2020
The Cold Start Problem In Software Fault Prediction written by Inbal Roshanski and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Software is an integral part of our lives today. Unfortunately, the more sophisticated and complicated software becomes, the greater the chance of failures. Predicting the probability of software components being faulty can help maintaining the software effectiveness. A key factor to the success of prediction algorithms is the amount and quality of historical data of the project collected by the version control and issue tracker tools. However, for new projects, for example, there is no historical data to learn from. This is known as the cold-start problem. Previous work proposed cross-project software fault prediction models, where fault prediction models of other projects are used to determine whether new project's components are faulty or not. In this paper we suggest a novel component-sensitive cross-project software fault prediction approach (OSCAR). OSCAR proceeds in two steps. First, it separately classifies each component in the new project to its most similar project among a set of other projects. Then, OSCAR uses the fault prediction model of that project to predict whether the component in the new project is faulty. This approach is in contrast to previous work that try to find one suitable model for all the components in the new project. Furthermore, we suggest an improvement to OSCAR, by using clustering algorithm combined with it. Evaluation, conducted on three datasets which includes 43 software projects, shows that the prediction of OSCAR is more accurate than state-of-the-art competitive algorithms.
Software Fault Detection And Correction Modeling And Applications
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Author : Rui Peng
language : en
Publisher: Springer
Release Date : 2018-11-01
Software Fault Detection And Correction Modeling And Applications written by Rui Peng and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-01 with Computers categories.
This book focuses on software fault detection and correction processes, presenting 5 different paired models introduced over the last decade and discussing their applications, in particular to determining software release time. The first work incorporates the testing effort function and the fault introduction process into the paired fault detection and fault correction models. The second work incorporates fault dependency, while the third adopts a Markov approach for studying fault detection and correction processes. The fourth work considers the multi-release property of various software, and models fault detection and correction processes. The last work classifies faults into four types and models the fault-detection and correction processes. Enabling readers to familiarize themselves with how software reliability can be modeled when different factors need to be considered, and how the approaches can be used to analyze other systems, the book is important reference guide for researchers in the field of software reliability engineering and practitioners working on software projects. To gain the most from the book, readers should have a firm grasp of the fundamentals of the stochastic process.
Enhancing Software Fault Prediction With Machine Learning Emerging Research And Opportunities
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Author : Rashid, Ekbal
language : en
Publisher: IGI Global
Release Date : 2017-09-13
Enhancing Software Fault Prediction With Machine Learning Emerging Research And Opportunities written by Rashid, Ekbal and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-13 with Computers categories.
Software development and design is an intricate and complex process that requires a multitude of steps to ultimately create a quality product. One crucial aspect of this process is minimizing potential errors through software fault prediction. Enhancing Software Fault Prediction With Machine Learning: Emerging Research and Opportunities is an innovative source of material on the latest advances and strategies for software quality prediction. Including a range of pivotal topics such as case-based reasoning, rate of improvement, and expert systems, this book is an ideal reference source for engineers, researchers, academics, students, professionals, and practitioners interested in novel developments in software design and analysis.
Early Software Reliability Prediction
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Author : Ajeet Kumar Pandey
language : en
Publisher: Springer
Release Date : 2013-07-12
Early Software Reliability Prediction written by Ajeet Kumar Pandey and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-12 with Technology & Engineering categories.
The development of software system with acceptable level of reliability and quality within available time frame and budget becomes a challenging objective. This objective could be achieved to some extent through early prediction of number of faults present in the software, which reduces the cost of development as it provides an opportunity to make early corrections during development process. The book presents an early software reliability prediction model that will help to grow the reliability of the software systems by monitoring it in each development phase, i.e. from requirement phase to testing phase. Different approaches are discussed in this book to tackle this challenging issue. An important approach presented in this book is a model to classify the modules into two categories (a) fault-prone and (b) not fault-prone. The methods presented in this book for assessing expected number of faults present in the software, assessing expected number of faults present at the end of each phase and classification of software modules in fault-prone or no fault-prone category are easy to understand, develop and use for any practitioner. The practitioners are expected to gain more information about their development process and product reliability, which can help to optimize the resources used.
Application Of Multivariate Analysis For Software Fault Prediction
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Author : Niclas Ohlsson
language : en
Publisher:
Release Date : 1996
Application Of Multivariate Analysis For Software Fault Prediction written by Niclas Ohlsson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Computer software categories.
Abstract: "The need for quantitative methods to support project control has been expressed in a number of recent papers. A number of multivariate analysis techniques are available for analysing high- dimensional observations of software design metrics. This paper presents a successful study in which principal component analysis (PCA) and discriminant coordinates (DC) were used to develop prediction models for data from Ericsson Telecom AB. Instead of dividing modules into fault- prone and non-fault-prone, which has been common in previous studies, observations were categorized into several groups according to the ordered number of faults. The DC analysis revealed that the first discriminant coordinates statistically increase with the ordering of modules. This empirical result suggests an approach for ordering as a first step toward prediction of fault-prone modules that incorporates attributes of process and resources. The result of applying DC was compared with discriminant analysis (DA), which has been reported useful for building prediction models of fault-prone modules. The later models were found to be inadequate for predicting the most fault-prone modules for the considered data set. The authors experienced a number of problems while applying the earlier reported prediction models. These are illustrated in this paper, and improvements are suggested."
Ck Metrics As A Software Fault Proneness Predictor
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Author : Sunil Sikka
language : en
Publisher: BookRix
Release Date : 2018-06-18
Ck Metrics As A Software Fault Proneness Predictor written by Sunil Sikka and has been published by BookRix this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-18 with Education categories.
Predicting Fault-proneness of software modules is essential for cost-effective test planning. Fault-proneness could play a key role in quality control of software. Various studies have shown the importance of software metrics in predicting fault-proneness of the software. “Classic” set of metrics was planned by Chidamber and Kemerer in 1991. Chidamber and Kemerer (CK) metrics suite is the most widely used metrics suite for the purpose of object-oriented software fault-proneness prediction. CK metrics are used for numerous function of study, e.g. defect prediction. CK metrics are the good predictor of fault-proneness of classes.C5.0 algorithm is one of the classification techniques of data mining. It is necessarily selected to partition data set into several smaller subsets in every recursion of creating decision tree. Object-oriented metrics play a very important role to quantify the effect of key factors to determine the fault-proneness. For fault-prediction model CK Metrics: Weighted Methods for Class (WMC), Depth of Inheritance Tree (DIT), Number of Children (NOC), Lack of Cohesion of Methods (LCOM), Response for Class (RFC), and Coupling Between Objects (CBO), are used as a independent variables.
