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Selenium WebDriver Practical Guide
by Satya AvasaralaAn easy- to- follow guide, featuring step-by-step practical tutorials to help you understand how to automate web applications for testing purposes. If you are a quality assurance / testing professional, a software developer, or a web application developer looking to create automation test scripts for your web applications, this is the perfect guide for you! As a pre-requisite, this book expects you to have a basic knowledge of Core Java, although any previous knowledge of WebDriver or Selenium-1 is not needed. By the end of this book, you will have acquired a comprehensive knowledge of WebDriver, which will help you in writing your automation tests.
Selenium WebDriver Quick Start Guide: Write clear, readable, and reliable tests with Selenium WebDriver 3
by Pinakin ChaubalGet writing tests and learn to design your own testing framework with Selenium WebDriver API Key Features Learn Selenium from the ground up Design your own testing framework Create reusable functionality in your framework Book Description Selenium WebDriver is a platform-independent API for automating the testing of both browser and mobile applications. It is also a core technology in many other browser automation tools, APIs, and frameworks. This book will guide you through the WebDriver APIs that are used in automation tests. Chapter by chapter, we will construct the building blocks of a page object model framework as you learn about the required Java and Selenium methods and terminology. The book starts with an introduction to the same-origin policy, cross-site scripting dangers, and the Document Object Model (DOM). Moving ahead, we'll learn about XPath, which allows us to select items on a page, and how to design a customized XPath. After that, we will be creating singleton patterns and drivers. Then you will learn about synchronization and handling pop-up windows. You will see how to create a factory for browsers and understand command design patterns applicable to this area. At the end of the book, we tie all this together by creating a framework and implementing multi-browser testing with Selenium Grid. What you will learn Understand what an XPath is and how to design a customized XPath Learn how to create a Maven project and build Create a Singleton driver Get to grips with Jenkins integration Create a factory for browsers Implement multi-browser testing with Selenium Grid Create a sample pop-up window and JavaScript alert Report using Extent Reports Who this book is for This book is for software testers or developers.
Selenium WebDriver Quick Start Guide: Write clear, readable, and reliable tests with Selenium WebDriver 3
by Pinakin ChaubalGet writing tests and learn to design your own testing framework with Selenium WebDriver API Key Features Learn Selenium from the ground up Design your own testing framework Create reusable functionality in your framework Book Description Selenium WebDriver is a platform-independent API for automating the testing of both browser and mobile applications. It is also a core technology in many other browser automation tools, APIs, and frameworks. This book will guide you through the WebDriver APIs that are used in automation tests. Chapter by chapter, we will construct the building blocks of a page object model framework as you learn about the required Java and Selenium methods and terminology. The book starts with an introduction to the same-origin policy, cross-site scripting dangers, and the Document Object Model (DOM). Moving ahead, we'll learn about XPath, which allows us to select items on a page, and how to design a customized XPath. After that, we will be creating singleton patterns and drivers. Then you will learn about synchronization and handling pop-up windows. You will see how to create a factory for browsers and understand command design patterns applicable to this area. At the end of the book, we tie all this together by creating a framework and implementing multi-browser testing with Selenium Grid. What you will learn Understand what an XPath is and how to design a customized XPath Learn how to create a Maven project and build Create a Singleton driver Get to grips with Jenkins integration Create a factory for browsers Implement multi-browser testing with Selenium Grid Create a sample pop-up window and JavaScript alert Report using Extent Reports Who this book is for This book is for software testers or developers.
Selenium WebDriver Recipes in C#: Practical Testing Solutions for Selenium WebDriver
by Courtney ZhanSolve your Selenium WebDriver problems with this quick guide to automated testing of web applications with Selenium WebDriver in C#. This third edition contains hundreds of solutions to real-world problems, with clear explanations and ready-to-run Selenium test scripts that you can use in your own projects. Updated to Selenium version 4, this revision includes Visual Studio Code set up, additional recipes, and new chapters on Selenium DevTools and continuous testing. You'll see how to use Selenium WebDriver for select lists, navigation, assertions, frames, file upload and pop-up dialogs. You'll also learn how to locate web elements and test functions for hyperlinks, buttons, TextFields and TextAreas, radio buttons, CheckBoxes, and more. What You'll Learn Debug test scripts and test data Work with Selenium Remote Control Server Manage and deal with browser profiles and capabilities Monitor tests for advanced user interactions and experiences (UX) Who This Book Is For Experienced .NET and C# Windows application programmers/developers.
Selenium WebDriver Recipes in C#: Second Edition
by Zhimin ZhanSolve your Selenium WebDriver problems with this quick guide to automated testing of web applications with Selenium WebDriver in C#. Selenium WebDriver Recipes in C#, Second Edition contains hundreds of solutions to real-world problems, with clear explanations and ready-to-run Selenium test scripts that you can use in your own projects. You'll learn: How to locate web elements and test functions for hyperlinks, buttons, TextFields and TextAreas, radio buttons, CheckBoxes, and more How to use Selenium WebDriver for select lists, navigation, assertions, frames, file upload and pop-up dialogs How to debug test scripts and test data How to manage and deal with browser profiles and capabilities How to manage tests for advanced user interactions and experiences (UX) How to work with and manage tests and testing using Selenium Remote Control and Selenium Server AudienceThis book is for experienced .NET and C# Windows application programmers/developers.
Self-Adaptive Heuristics for Evolutionary Computation (Studies in Computational Intelligence #147)
by Oliver KramerEvolutionary algorithms are successful biologically inspired meta-heuristics. Their success depends on adequate parameter settings. The question arises: how can evolutionary algorithms learn parameters automatically during the optimization? Evolution strategies gave an answer decades ago: self-adaptation. Their self-adaptive mutation control turned out to be exceptionally successful. But nevertheless self-adaptation has not achieved the attention it deserves. This book introduces various types of self-adaptive parameters for evolutionary computation. Biased mutation for evolution strategies is useful for constrained search spaces. Self-adaptive inversion mutation accelerates the search on combinatorial TSP-like problems. After the analysis of self-adaptive crossover operators the book concentrates on premature convergence of self-adaptive mutation control at the constraint boundary. Besides extensive experiments, statistical tests and some theoretical investigations enrich the analysis of the proposed concepts.
Self-Adaptive Software: Second International Workshop, IWSAS 2001, Balatonfüred, Hungary, May 17-19, 2001, Revised Papers (Lecture Notes in Computer Science #2614)
by Robert Laddaga Paul Robertson Howie ShrobeThe 18 revised full papers presented in this book together with an introductory survey were carefully reviewed and constitute the documentation of the Second International Workshop on Self-adaptive Software, IWSAS 2001, held in Balatonfüred, Hungary in May 2001. Self-adaptive software evaluates its own behavior and changes it when the evaluation indicates that the software does not accomplish what it is intended to do or when better functionality or better performance is possible. The self-adaptive approach in software engineering builds on well known dynamic features familiar to Lisp or Java programmes and aims at improving the robustness of software systems by gradually adding new features of self-adaption or autonomy.
Self-Adaptive Software: First International Workshop, IWSAS 2000 Oxford, UK, April 17-19, 2000 Revised Papers (Lecture Notes in Computer Science #1936)
by Paul Robertson Howie Shrobe Robert LaddagaSelf-adaptive software evaluates its own behavior and changes its behavior when the evaluation indicates that the software does not accomplish what it is intended to do or when better functionality or better performance is possible. The self-adaptive approach in software engineering builds on well-known features like the use of errors and the handling of exceptions in languages like Lisp or Java and aims at improving the robustness of software systems by gradually adding new features of self-adaption and autonomity.This book originates from the First International Workshop on Self-Adaptive Software, IWSAS 2000, held in Oxford, UK in April 2000. The revised full papers presented in the volume together with an introductory survey by the volume editors assess the state of the art in this emerging new field and set the scene for future research and development work.
Self-Adaptive Systems for Machine Intelligence
by Haibo HeThis book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This will provide an opportunity to evaluate the strengths and weaknesses of the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain. Self-adaptive intelligent systems have wide applications from military security systems to civilian daily life. In this book, different application problems, including pattern recognition, classification, image recovery, and sequence learning, will be presented to show the capability of the proposed systems in learning, memory, and prediction. Therefore, this book will also provide potential new solutions to many real-world applications.
Self-Adaptive Systems for Machine Intelligence
by Haibo HeThis book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This will provide an opportunity to evaluate the strengths and weaknesses of the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain. Self-adaptive intelligent systems have wide applications from military security systems to civilian daily life. In this book, different application problems, including pattern recognition, classification, image recovery, and sequence learning, will be presented to show the capability of the proposed systems in learning, memory, and prediction. Therefore, this book will also provide potential new solutions to many real-world applications.
Self- and Co-regulation in Cybercrime, Cybersecurity and National Security (SpringerBriefs in Cybersecurity)
by Tatiana Tropina Cormac CallananThe ever increasing use of computers, networks and the Internet has led to the need for regulation in the fields of cybercrime, cybersecurity and national security. This SpringerBrief provides insights into the development of self- and co-regulatory approaches to cybercrime and cybersecurity in the multi-stakeholder environment. It highlights the differences concerning the ecosystem of stakeholders involved in each area and covers government supported initiatives to motivate industry to adopt self-regulation. Including a review of the drawbacks of existing forms of public-private collaboration, which can be attributed to a specific area (cybercrime, cybersecurity and national security), it provides some suggestions with regard to the way forward in self- and co-regulation in securing cyberspace.
Self-* and P2P for Network Management: Design Principles and Case Studies (SpringerBriefs in Computer Science)
by Clarissa Cassales Marquezan Lisandro Zambenedetti GranvilleThe network management community has been pushed towards the design of alternative management approaches able to support heterogeneity, scalability, reliability, and minor human intervention. The employment of self-* properties and Peer-To-Peer (P2P) are seen as promising alternatives, able to provide the sophisticated solutions required. Despite being developed in parallel, and with minor direct connections perceived between them, self-* properties and P2P can be used concurrently. In Self-* and P2P for Network Management: Design Principles and Case Studies, the authors explore the issues behind the joint use of self-* properties and P2P, and present: a survey relating autonomic computing and self-* properties, P2P, and network and service management; the design of solutions that explore parallel and cooperative behavior of management peers; the change in angle of network management solution development from APIs, protocols, architectures, and frameworks to the design of management algorithms.
Self-Assembled Quantum Dots (Lecture Notes in Nanoscale Science and Technology #1)
by Zhiming M. WangThis multidisciplinary book provides up-to-date coverage of carrier and spin dynamics and energy transfer and structural interaction among nanostructures. Coverage also includes current device applications such as quantum dot lasers and detectors, as well as future applications to quantum information processing. The book will serve as a reference for anyone working with or planning to work with quantum dots.
The Self-Assembling Brain: How Neural Networks Grow Smarter
by Peter Robin HiesingerWhat neurobiology and artificial intelligence tell us about how the brain builds itself How does a neural network become a brain? While neurobiologists investigate how nature accomplishes this feat, computer scientists interested in artificial intelligence strive to achieve this through technology. The Self-Assembling Brain tells the stories of both fields, exploring the historical and modern approaches taken by the scientists pursuing answers to the quandary: What information is necessary to make an intelligent neural network?As Peter Robin Hiesinger argues, “the information problem” underlies both fields, motivating the questions driving forward the frontiers of research. How does genetic information unfold during the years-long process of human brain development—and is there a quicker path to creating human-level artificial intelligence? Is the biological brain just messy hardware, which scientists can improve upon by running learning algorithms on computers? Can AI bypass the evolutionary programming of “grown” networks? Through a series of fictional discussions between researchers across disciplines, complemented by in-depth seminars, Hiesinger explores these tightly linked questions, highlighting the challenges facing scientists, their different disciplinary perspectives and approaches, as well as the common ground shared by those interested in the development of biological brains and AI systems. In the end, Hiesinger contends that the information content of biological and artificial neural networks must unfold in an algorithmic process requiring time and energy. There is no genome and no blueprint that depicts the final product. The self-assembling brain knows no shortcuts.Written for readers interested in advances in neuroscience and artificial intelligence, The Self-Assembling Brain looks at how neural networks grow smarter.
Self-Aware Computing Systems
by Samuel Kounev Jeffrey O. Kephart Aleksandar Milenkoski Xiaoyun ZhuThis book provides formal and informal definitions and taxonomies for self-aware computing systems, and explains how self-aware computing relates to many existing subfields of computer science, especially software engineering. It describes architectures and algorithms for self-aware systems as well as the benefits and pitfalls of self-awareness, and reviews much of the latest relevant research across a wide array of disciplines, including open research challenges. The chapters of this book are organized into five parts: Introduction, System Architectures, Methods and Algorithms, Applications and Case Studies, and Outlook. Part I offers an introduction that defines self-aware computing systems from multiple perspectives, and establishes a formal definition, a taxonomy and a set of reference scenarios that help to unify the remaining chapters. Next, Part II explores architectures for self-aware computing systems, such as generic concepts and notations that allow a wide range of self-aware system architectures to be described and compared with both isolated and interacting systems. It also reviews the current state of reference architectures, architectural frameworks, and languages for self-aware systems. Part III focuses on methods and algorithms for self-aware computing systems by addressing issues pertaining to system design, like modeling, synthesis and verification. It also examines topics such as adaptation, benchmarks and metrics. Part IV then presents applications and case studies in various domains including cloud computing, data centers, cyber-physical systems, and the degree to which self-aware computing approaches have been adopted within those domains. Lastly, Part V surveys open challenges and future research directions for self-aware computing systems. It can be used as a handbook for professionals and researchers working in areas related to self-aware computing, and can also serve as an advanced textbook for lecturers and postgraduate students studying subjects like advanced software engineering, autonomic computing, self-adaptive systems, and data-center resource management. Each chapter is largely self-contained, and offers plenty of references for anyone wishing to pursue the topic more deeply.
Self-aware Computing Systems: An Engineering Approach (Natural Computing Series)
by Peter R. Lewis Marco Platzner Bernhard Rinner Jim Tørresen Xin YaoTaking inspiration from self-awareness in humans, this book introduces the new notion of computational self-awareness as a fundamental concept for designing and operating computing systems. The basic ability of such self-aware computing systems is to collect information about their state and progress, learning and maintaining models containing knowledge that enables them to reason about their behaviour. Self-aware computing systems will have the ability to utilise this knowledge to effectively and autonomously adapt and explain their behaviour, in changing conditions. This book addresses these fundamental concepts from an engineering perspective, aiming at developing primitives for building systems and applications. It will be of value to researchers, professionals and graduate students in computer science and engineering.
Self Aware Security for Real Time Task Schedules in Reconfigurable Hardware Platforms
by Amlan Chakrabarti Krishnendu Guha Sangeet SahaThis book focuses on how real-time task schedules for reconfigurable hardware-based embedded platforms may be affected due to the vulnerability of hardware and proposes self-aware security strategies to counteract the various threats. The emergence of Industry 4.0 has witnessed the deployment of reconfigurable hardware or field programmable gate arrays (FPGAs) in diverse embedded applications. These are associated with the execution of several real-time tasks arranged in schedules. However, they are associated with several issues. Development of fully and partially reconfigurable task schedules are discussed that eradicates the existing problems. However, such real-time task schedules may be jeopardized due to hardware threats. Analysis of such threats is discussed and self-aware security techniques are proposed that can detect and mitigate such threats at runtime.
Self-Dual Chern-Simons Theories (Lecture Notes in Physics Monographs #36)
by Gerald DunneSelf-duality greatly reduces the mathematical difficulties of a theory but it is also a notion of considerable physical significance. The new class of self-dual Chern-Simons theories discussed in detail in this book arise in the context of anyonic quantum field theory and have applications to models such as the quantum Hall effect, anyonic superconductivity, and Aharonov-Bohm scattering. There are also interesting connections with the theory of integrable models. The author presents the abelian and non-abelian models for relativistic and non-relativistic realizations of the self-dual Chern-Simons theories and finishes with some applications in quantum physics. The book is written for advanced students and researchers in mathematical, particle, and condensed matter physics.
Self-Dual Codes and Invariant Theory (Algorithms and Computation in Mathematics #17)
by Gabriele Nebe Eric M. Rains Neil J. SloaneOne of the most remarkable and beautiful theorems in coding theory is Gleason's 1970 theorem about the weight enumerators of self-dual codes and their connections with invariant theory, which has inspired hundreds of papers about generalizations and applications of this theorem to different types of codes. This self-contained book develops a new theory which is powerful enough to include all the earlier generalizations.
Self-Efficacy in Instructional Technology Contexts
by Charles B. HodgesThis edited volume contains reports of current research, and literature reviews of research, involving self-efficacy in various instructional technology contexts. The chapters represent international perspectives across the broad areas of K- 12 education, higher education, teacher self-efficacy, and learner self-efficacy to capture a diverse cross section of research on these topics. The book includes reviews of existing literature and reports of new research, thus creating a comprehensive resource for researchers and designers interested in this general topic. The book is especially relevant to students and researchers in educational technology, instructional technology, instructional design, learning sciences, and educational psychology.
Self Engineering: Learning From Failures (SpringerBriefs in Applied Sciences and Technology)
by Shuichi FukudaThis book demonstrates how the creation of emotional satisfaction will change in tomorrow’s connected, IoT world. The importance of emotional satisfaction will increase in the IoT Connected Society of World 2.0, in which humans and machines work together as members of the same team with no walls between the two, and where production is also team-based. Developing emotional satisfaction in such a diverse team and in a very different environment is a major challenge and needs to be studied from a broad perspective. This book describes the emerging issues and how they can be to tackled, introducing paths for moving beyond static value toward developing dynamic value.
Self-Healing Systems and Wireless Networks Management
by Junaid Ahsenali ChaudhryDo you believe in open-source development? Would you like to see your security system grow and learn by itself? Are you sick of paying for software license fees every year that produce little return on investment? And, would you prefer to invest in something you could sell later on to other IT security departments? If you answered yes to these ques
Self-Improving Teaching Systems: An Application of Artificial Intelligence to Computer Assisted Instruction (Interdisciplinary Systems Research)
by O'SHEA
Self-Learning and Adaptive Algorithms for Business Applications: A Guide to Adaptive Neuro-Fuzzy Systems for Fuzzy Clustering Under Uncertainty Conditions (Emerald Points)
by Zhengbing Hu Yevgeniy V. Bodyanskiy Oleksii TyshchenkoIn today’s data-driven world, more sophisticated algorithms for data processing are in high demand, mainly when the data cannot be handled with the help of traditional techniques. Self-learning and adaptive algorithms are now widely used by such leading giants that as Google, Tesla, Microsoft, and Facebook in their projects and applications. In this guide designed for researchers and students of computer science, readers will find a resource for how to apply methods that work on real-life problems to their challenging applications, and a go-to work that makes fuzzy clustering issues and aspects clear. Including research relevant to those studying cybernetics, applied mathematics, statistics, engineering, and bioinformatics who are working in the areas of machine learning, artificial intelligence, complex system modeling and analysis, neural networks, and optimization, this is an ideal read for anyone interested in learning more about the fascinating new developments in machine learning.
Self-Learning and Adaptive Algorithms for Business Applications: A Guide to Adaptive Neuro-Fuzzy Systems for Fuzzy Clustering Under Uncertainty Conditions (Emerald Points)
by Zhengbing Hu Yevgeniy V. Bodyanskiy Oleksii TyshchenkoIn today’s data-driven world, more sophisticated algorithms for data processing are in high demand, mainly when the data cannot be handled with the help of traditional techniques. Self-learning and adaptive algorithms are now widely used by such leading giants that as Google, Tesla, Microsoft, and Facebook in their projects and applications. In this guide designed for researchers and students of computer science, readers will find a resource for how to apply methods that work on real-life problems to their challenging applications, and a go-to work that makes fuzzy clustering issues and aspects clear. Including research relevant to those studying cybernetics, applied mathematics, statistics, engineering, and bioinformatics who are working in the areas of machine learning, artificial intelligence, complex system modeling and analysis, neural networks, and optimization, this is an ideal read for anyone interested in learning more about the fascinating new developments in machine learning.