Download or read book Fault Prediction Modeling for the Prediction of Number of Software Faults written by Santosh Singh Rathore and published by Springer. This book was released on 2019-04-03 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Software Fault Prediction written by Sandeep Kumar and published by Springer. This book was released on 2018-06-06 with total page 81 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Fault Prediction Approach written by Dhana Laxmi and published by . This book was released on 2019-03-28 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book The Cold start Problem in Software Fault Prediction written by Inbal Roshanski and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Enhancing Software Fault Prediction With Machine Learning Emerging Research and Opportunities written by Rashid, Ekbal and published by IGI Global. This book was released on 2017-09-13 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Software Fault Detection and Correction Modeling and Applications written by Rui Peng and published by Springer. This book was released on 2018-11-01 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Defect Prediction in Software Development Maintainence written by Rudra Kumar and published by Partridge Publishing. This book was released on 2018-04-11 with total page 57 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book 2021 2nd International Conference on Secure Cyber Computing and Communications ICSCCC written by IEEE Staff and published by . This book was released on 2021-05-21 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The area of computing and communication has grown evidently since past two decades with wide ranging aspects Computing includes designing, developing and building hardware and software systems processing, structuring, and managing various kinds of information doing scientific research on and with computers making computer systems behave intelligently and creating and using communications and entertainment media The field of computing includes computer engineering, software engineering, computer science, information systems, information technology and the list is virtually endless, and the possibilities are vast Communication means to share, it is the act of conveying intended meanings from one entity or group to another through the use of mutually understood signs and semiotic rules It also has wide ranging applied areas which includes Network communication, Security etc

Download or read book Software Fault Prediction Models Using Machine Learning Approach written by Golnoush Abaei and published by . This book was released on 2015 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book CK Metrics as a Software Fault Proneness Predictor written by Sunil Sikka and published by BookRix. This book was released on 2018-06-18 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book 2021 9th International Conference in Software Engineering Research and Innovation CONISOFT written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Application of Multivariate Analysis for Software Fault Prediction written by Niclas Ohlsson and published by . This book was released on 1996 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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."

Download or read book Intelligent Software Defect Prediction written by Xiao-Yuan Jing and published by Springer Nature. This book was released on 2024-01-17 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the increasing complexity of and dependency on software, software products may suffer from low quality, high prices, be hard to maintain, etc. Software defects usually produce incorrect or unexpected results and behaviors. Accordingly, software defect prediction (SDP) is one of the most active research fields in software engineering and plays an important role in software quality assurance. Based on the results of SDP analyses, developers can subsequently conduct defect localization and repair on the basis of reasonable resource allocation, which helps to reduce their maintenance costs. This book offers a comprehensive picture of the current state of SDP research. More specifically, it introduces a range of machine-learning-based SDP approaches proposed for different scenarios (i.e., WPDP, CPDP, and HDP). In addition, the book shares in-depth insights into current SDP approaches’ performance and lessons learned for future SDP research efforts. We believe these theoretical analyses and emerging challenges will be of considerable interest to all researchers, graduate students, and practitioners who want to gain deeper insights into and/or find new research directions in SDP. It offers a comprehensive introduction to the current state of SDP and detailed descriptions of representative SDP approaches.

Download or read book Software Defect and Operational Profile Modeling written by Kai-Yuan Cai and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: also in: THE KLUWER INTERNATIONAL SERIES ON ASIAN STUDIES IN COMPUTER AND INFORMATION SCIENCE, Volume 1

Download or read book Data Science and Big Data Analytics written by Durgesh Mishra and published by Springer Nature. This book was released on with total page 733 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Vision and Augmented Intelligence written by Koushlendra Kumar Singh and published by Springer Nature. This book was released on 2023-06-01 with total page 645 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprises the proceedings of the International Conference on Machine Vision and Augmented Intelligence (MAI 2022). The conference proceedings encapsulate the best deliberations held during the conference. The diversity of participants in the event from academia, industry, and research reflects in the articles appearing in the book. The book encompasses all industrial and non-industrial applications. This book covers a wide range of topics such as modeling of disease transformation, epidemic forecast, image processing, and computer vision, augmented intelligence, soft computing, deep learning, image reconstruction, artificial intelligence in health care, brain-computer interface, cybersecurity, social network analysis, and natural language processing.​

Download or read book Fundamentals and Methods of Machine and Deep Learning written by Pradeep Singh and published by John Wiley & Sons. This book was released on 2022-02-01 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. Audience Researchers and engineers in artificial intelligence, computer scientists as well as software developers.