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Enhancing Software Fault Prediction With Machine Learning Emerging Research And Opportunities
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Book Synopsis Enhancing Software Fault Prediction With Machine Learning: Emerging Research and Opportunities by : Rashid, Ekbal
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.
Book Synopsis Software Fault Prediction by : Sandeep Kumar
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.
Book Synopsis Intelligent Software Defect Prediction by : Xiao-Yuan Jing
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.
Book Synopsis Advances in System Dynamics and Control by : Azar, Ahmad Taher
Download or read book Advances in System Dynamics and Control written by Azar, Ahmad Taher and published by IGI Global. This book was released on 2018-02-09 with total page 706 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex systems are pervasive in many areas of science. With the increasing requirement for high levels of system performance, complex systems has become an important area of research due to its role in many industries. Advances in System Dynamics and Control provides emerging research on the applications in the field of control and analysis for complex systems, with a special emphasis on how to solve various control design and observer design problems, nonlinear systems, interconnected systems, and singular systems. Featuring coverage on a broad range of topics, such as adaptive control, artificial neural network, and synchronization, this book is an important resource for engineers, professionals, and researchers interested in applying new computational and mathematical tools for solving the complicated problems of mathematical modeling, simulation, and control.
Book Synopsis Handbook of Research on Pattern Engineering System Development for Big Data Analytics by : Tiwari, Vivek
Download or read book Handbook of Research on Pattern Engineering System Development for Big Data Analytics written by Tiwari, Vivek and published by IGI Global. This book was released on 2018-04-20 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. The Handbook of Research on Pattern Engineering System Development for Big Data Analytics is a critical scholarly resource that examines the incorporation of pattern management in business technologies as well as decision making and prediction process through the use of data management and analysis. Featuring coverage on a broad range of topics such as business intelligence, feature extraction, and data collection, this publication is geared towards professionals, academicians, practitioners, and researchers seeking current research on the development of pattern management systems for business applications.
Book Synopsis Safety and Security of Cyber-Physical Systems by : Frank J. Furrer
Download or read book Safety and Security of Cyber-Physical Systems written by Frank J. Furrer and published by Springer Nature. This book was released on 2022-07-20 with total page 559 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cyber-physical systems (CPSs) consist of software-controlled computing devices communicating with each other and interacting with the physical world through sensors and actuators. Because most of the functionality of a CPS is implemented in software, the software is of crucial importance for the safety and security of the CPS. This book presents principle-based engineering for the development and operation of dependable software. The knowledge in this book addresses organizations that want to strengthen their methodologies to build safe and secure software for mission-critical cyber-physical systems. The book: • Presents a successful strategy for the management of vulnerabilities, threats, and failures in mission-critical cyber-physical systems; • Offers deep practical insight into principle-based software development (62 principles are introduced and cataloged into five categories: Business & organization, general principles, safety, security, and risk management principles); • Provides direct guidance on architecting and operating dependable cyber-physical systems for software managers and architects.
Book Synopsis Optimizing Contemporary Application and Processes in Open Source Software by : Khosrow-Pour, Mehdi
Download or read book Optimizing Contemporary Application and Processes in Open Source Software written by Khosrow-Pour, Mehdi and published by IGI Global. This book was released on 2018-02-02 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: As is true of most technological fields, the software industry is constantly advancing and becoming more accessible to a wider range of people. The advancement and accessibility of these systems creates a need for understanding and research into their development. Optimizing Contemporary Application and Processes in Open Source Software is a critical scholarly resource that examines the prevalence of open source software systems as well as the advancement and development of these systems. Featuring coverage on a wide range of topics such as machine learning, empirical software engineering and management, and open source, this book is geared toward academicians, practitioners, and researchers seeking current and relevant research on the advancement and prevalence of open source software systems.
Book Synopsis Defect Prediction in Software Development & Maintainence by : Rudra Kumar
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.
Book Synopsis Emerging Trends in IoT and Computing Technologies by : Suman Lata Tripathi
Download or read book Emerging Trends in IoT and Computing Technologies written by Suman Lata Tripathi and published by CRC Press. This book was released on 2024-08-29 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: Second International Conference on Emerging Trends in IOT and Computing Technologies (ICEICT – 2023) is organised with a vision to address the various issues to promote the creation of intelligent solution for the future. It is expected that researchers will bring new prospects for collaboration across disciplines and gain ideas facilitating novel concepts. Second International Conference of Emerging Trends in IoT and Computer Technologies (ICEICT-2023) is an inventive event organised in Goel Institute of Technology and Management, Lucknow, India, with motive to make available an open International forum for the researches, academicians, technocrats, scientist, engineers, industrialist and students around the globe to exchange their innovations and share the research outcomes which may lead the young researchers, academicians and industrialist to contribute to the global society. The conference ICEICT- 2023 is being organised at Goel Institute of Technology and Management, Lucknow, Uttar Pradesh, during 12-13 January 2024. It will feature world-class keynote speakers, special sessions, along with the regular/oral paper presentations. The conference welcomes paper submissions from researcher, practitioners, academicians and students will cover numerous tracks in the field of Computer Science and Engineering and associated research areas.
Book Synopsis Leveraging Machine Learning to Improve Software Reliability by : Song Wang
Download or read book Leveraging Machine Learning to Improve Software Reliability written by Song Wang and published by . This book was released on 2018 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: Finding software faults is a critical task during the lifecycle of a software system. While traditional software quality control practices such as statistical defect prediction, static bug detection, regression test, and code review are often inefficient and time-consuming, which cannot keep up with the increasing complexity of modern software systems. We argue that machine learning with its capability in knowledge representation, learning, natural language processing, classification, etc., can be used to extract invaluable information from software artifacts that may be difficult to obtain with other research methodologies to improve existing software reliability practices such as statistical defect prediction, static bug detection, regression test, and code review. This thesis presents a suite of machine learning based novel techniques to improve existing software reliability practices for helping developers find software bugs more effective and efficient. First, it introduces a deep learning based defect prediction technique to improve existing statistical defect prediction models. To build accurate prediction models, previous studies focused on manually designing features that encode the statistical characteristics of programs. However, these features often fail to capture the semantic difference of programs, and such a capability is needed for building accurate prediction models. To bridge the gap between programs' semantics and defect prediction features, this thesis leverages deep learning techniques to learn a semantic representation of programs automatically from source code and further build and train defect prediction models by using these semantic features. We examine the effectiveness of the deep learning based prediction models on both the open-source and commercial projects. Results show that the learned semantic features can significantly outperform existing defect prediction models. Second, it introduces an n-gram language based static bug detection technique, i.e., Bugram, to detect new types of bugs with less false positives. Most of existing static bug detection techniques are based on programming rules inferred from source code. It is known that if a pattern does not appear frequently enough, rules are not learned, thus missing many bugs. To solve this issue, this thesis proposes Bugram, which leverages n-gram language models instead of rules to detect bugs. Specifically, Bugram models program tokens sequentially, using the n-gram language model. Token sequences from the program are then assessed according to their probability in the learned model, and low probability sequences are marked as potential bugs. The assumption is that low probability token sequences in a program are unusual, which may indicate bugs, bad practices, or unusual/special uses of code of which developers may want to be aware. We examine the effectiveness of our approach on the latest versions of 16 open-source projects. Results show that Bugram detected 25 new bugs, 23 of which cannot be detected by existing rule-based bug detection approaches, which suggests that Bugram is complementary to existing bug detection approaches to detect more bugs and generates less false positives. Third, it introduces a machine learning based regression test prioritization technique, i.e., QTEP, to find and run test cases that could reveal bugs earlier. Existing test case prioritization techniques mainly focus on maximizing coverage information between source code and test cases to schedule test cases for finding bugs earlier. While they often do not consider the likely distribution of faults in the source code. However, software faults are not often equally distributed in source code, e.g., around 80\% faults are located in about 20\% source code. Intuitively, test cases that cover the faulty source code should have higher priorities, since they are more likely to find faults. To solve this issue, this thesis proposes QTEP, which leverages machine learning models to evaluate source code quality and then adapt existing test case prioritization algorithms by considering the weighted source code quality. Evaluation on seven open-source projects shows that QTEP can significantly outperform existing test case prioritization techniques to find failed test cases early. Finally, it introduces a machine learning based approach to identifying risky code review requests. Code review has been widely adopted in the development process of both the proprietary and open-source software, which helps improve the maintenance and quality of software before the code changes being merged into the source code repository. Our observation on code review requests from four large-scale projects reveals that around 20\% changes cannot pass the first round code review and require non-trivial revision effort (i.e., risky changes). In addition, resolving these risky changes requires 3X more time and 1.6X more reviewers than the regular changes (i.e., changes pass the first code review) on average. This thesis presents the first study to characterize these risky changes and automatically identify these risky changes with machine learning classifiers. Evaluation on one proprietary project and three large-scale open-source projects (i.e., Qt, Android, and OpenStack) shows that our approach is effective in identifying risky code review requests. Taken together, the results of the four studies provide evidence that machine learning can help improve traditional software reliability such as statistical defect prediction, static bug detection, regression test, and code review.
Book Synopsis State of the Art Software Development in the Automotive Industry and Analysis upon Applicability of Software Fault Prediction by : Harald Altinger
Download or read book State of the Art Software Development in the Automotive Industry and Analysis upon Applicability of Software Fault Prediction written by Harald Altinger and published by Cuvillier Verlag. This book was released on 2023-08-08 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years the amount of software within automobiles has increased up to 100 Million LOC in modern day premium vehicles. Virtually all innovations in automotive engineering in the last decade include software components. Parallel to this increasing amount, testing becomes more vital. Automotive software development follows restrictive guidelines in terms of coding standard, language limitations and processes. Traditionally testing is a core part of automotive development, but the raising number of features increases the time and money required to perform all tests. Repeating them multiple times due to programming errors might jeopardises a cars introduction on the market. SFP is a new approach to forecast bugs already at time of commit, thus to guide test engineers upon defining testing hotspots. This work reports on the first successful application using model driven and code generated automotive software as a case study and a success prediction rate up to 97% upon a bug or fault free commit. A compiled and published dataset is presented along with analysis upon the used software metrics. Performance data achieved using different machine learning algorithms is given. An indepth analysis upon factors preventing CPFP is conducted. Further usage and practical application areas will conclude the work.
Book Synopsis Empirical Research in Software Engineering by : Ruchika Malhotra
Download or read book Empirical Research in Software Engineering written by Ruchika Malhotra and published by CRC Press. This book was released on 2016-03-09 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: Empirical research has now become an essential component of software engineering yet software practitioners and researchers often lack an understanding of how the empirical procedures and practices are applied in the field. Empirical Research in Software Engineering: Concepts, Analysis, and Applications shows how to implement empirical research pro
Book Synopsis Innovations in Software-Defined Networking and Network Functions Virtualization by : Dumka, Ankur
Download or read book Innovations in Software-Defined Networking and Network Functions Virtualization written by Dumka, Ankur and published by IGI Global. This book was released on 2018-02-16 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: The advancement of technology is a standard of modern daily life, whether it be the release of a new cellphone, computer, or a self-driving car. Due to this constant advancement, the networks on which these technologies operate must advance as well. Innovations in Software-Defined Networking and Network Functions Virtualization is a critical scholarly publication that observes the advances made in network infrastructure through achieving cost efficacy while maintaining maximum flexibility for the formation and operation of these networks. Featuring coverage on a broad selection of topics, such as software-defined storage, openflow controller, and storage virtualization, this publication is geared toward professionals, computer engineers, academicians, students, and researchers seeking current and relevant research on the advancements made to network infrastructures.
Book Synopsis Incorporating Nature-Inspired Paradigms in Computational Applications by : Khosrow-Pour, Mehdi
Download or read book Incorporating Nature-Inspired Paradigms in Computational Applications written by Khosrow-Pour, Mehdi and published by IGI Global. This book was released on 2018-04-06 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many techniques have been developed to control the variety of dynamic systems. To develop those control techniques, it is fundamental to know the mathematical relations between the system inputs and outputs. Incorporating Nature-Inspired Paradigms in Computational Applications is a critical scholarly resource that examines the application of nature-inspired paradigms on system identification. Featuring coverage on a broad range of topics such as biogeographic computation, evolutionary control systems, and natural computing, this book is geared towards IT professionals, engineers, computer scientists, academicians, researchers, and graduate-level students seeking current research on the application of nature-inspired paradigms.
Book Synopsis Green Computing Strategies for Competitive Advantage and Business Sustainability by : Khosrow-Pour, Mehdi
Download or read book Green Computing Strategies for Competitive Advantage and Business Sustainability written by Khosrow-Pour, Mehdi and published by IGI Global. This book was released on 2018-02-02 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the age of corporate responsibility, green technology and sustainability continue to grip the consciousness of businesses. However, the development of appropriate business-driven green computing applications requires an awareness of the best practices of the green agenda. Green Computing Strategies for Competitive Advantage and Business Sustainability provides emerging research on maintaining an eco-friendly environment regarding cloud computing and promoting reusability. While highlighting competitive advantages, power consumption, and project evaluation, readers will learn about various techniques for reducing energy consumption in cloud performance and information technology systems. This book is a vital resource for academics, researchers, students, professionals, and managers interested in novel trends in green computing applications and technology.
Book Synopsis Emerging Trends and Applications in Artificial Intelligence by : Fausto Pedro García Márquez
Download or read book Emerging Trends and Applications in Artificial Intelligence written by Fausto Pedro García Márquez and published by Springer Nature. This book was released on with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Software Fault Prediction Models Using Machine Learning Approach by : Golnoush Abaei
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:
