Testing at Google Scale Product Description 2012 Jolt Award finalist! Pioneering the Future of Software Test Do you need to get it right, too? Then, learn from Google . Legendary testing expert James Whittaker, until recently a Google testing leader, and two top Google experts reveal exactly how Google tests software, offering brand-new best practices you can use even if you?re not quite Google?s size? yet! Breakthrough Techniques You Can Actually Use Discover 100% practical, amazingly scalable techniques for analyzing risk and planning tests?thinking like real users?implementing exploratory, black box, white box, and acceptance testing?getting usable feedback?tracking issues?choosing and creating tools?testing ?Docs & Mocks,? interfaces, classes, modules, libraries, binaries, services, and infrastructure?reviewing code and refactoring?using test hooks, presubmit scripts, queues, continuous builds, and more. With these techniques, you can transform testing from a bottleneck into an accelerator ?and make your whole organization more productive! Features + Benefits Presents pioneering testing techniques that can help any company moving to the cloud Shows how to achieve web-level scale for integration and system testing Offers expert guidance on managing end-to-end testing, including superior automation strategies Foreword by Alberto Savoia xiii Foreword by Patrick Copeland xvii Preface xxiii Chapter 1: Introduction to Google Software Testing 1 Quality?Test 5 Roles 6 Organizational Structure 8 Crawl, Walk, Run 10 Types of Tests 12 Chapter 2: The Software Engineer in Test 15 The Life of an SET 17 Development and Test Workflow 17 Who Are These SETs Anyway? 22 The Early Phase of a Project 22 Team Structure 24 Design Docs 25 Interfaces and Protocols 27 Automation Planning 28 Testability 29 SET Workflow: An Example 32 Test Execution 40 Test Size Definitions 41 Use of Test Sizes in Shared Infrastructure 44 Benefits of Test Sizes 46 Test Runtime Requirements 48 Case 1: Change in Common Library 52 Test Certified 54 An Interview with the Founders of the Test Certified Program 57 Interviewing SETs 62 An Interview with Tool Developer Ted Mao 68 An Interview with Web Driver Creator Simon Stewart 70 Chapter 3: The Test Engineer 75 A User-Facing Test Role 75 The Life of a TE 76 Test Planning 79 Risk 97 Life of a Test Case 108 Life of a Bug 113 Recruiting TEs 127 Test Leadership at Google 134 Maintenance Mode Testing 137 Quality Bots Experiment 141 BITE Experiment 153 Google Test Analytics 163 Free Testing Workflow 169 External Vendors 173 An Interview with Google Docs TE Lindsay Webster 175 An Interview with YouTube TE Apple Chow 181 Chapter 4: The Test Engineering Manager 187 The Life of a TEM 187 Getting Projects and People 189 Impact 191 An Interview with Gmail TEM Ankit Mehta 193 An Interview with Android TEM Hung Dang 198 An Interview with Chrome TEM Joel Hynoski 202 The Test Engineering Director 206 An Interview with Search and Geo Test Director Shelton Mar 207 An Interview with Engineering Tools Director Ashish Kumar 211 An Interview with Google India Test Director Sujay Sahni 214 An Interview with Engineering Manager Brad Green 219 An Interview with James Whittaker 222 Chapter 5: Improving How Google Tests Software 229 Fatal Flaws in Google´s Process 229 The Future of the SET 231 The Future of the TE 233 The Future of the Test Director and Manager 234 The Future of Test Infrastructure 234 In Conclusion 235 Appendix A: Chrome OS Test Plan 237 Overview of Themes 237 Risk Analysis 238 Per-Build Baseline Testing 239 Per-LKG Day Testing 239 Per-Release Testing 239 Manual Versus Automation 240 Dev Versus Test Quality Focus 240 Release Channels 240 User Input 241 Test Case Repositories 241 Test Dashboarding 241 Virtualization 241 Performance 242 Stress, Long-Running, and Stability 242 Test Execution Framework (Autotest) 242 OEMs 242 Hardware Lab 242 E2E Farm Automation 243 Testing the Browser AppManager 243 Browser Testability 243 Hardware 244 Timeline 244 Primary Test Drivers 246 Relevant Documents 246 Appendix B: Test Tours for Chrome 247 The Shopping Tour 247 The Student Tour 248 Suggested Areas to Test 248 The International Calling Tour 249 Suggested Areas to Test 249 The Landmark Tour 249 Suggested Landmarks in Chrome 249 The All Nighter Tour 250 Suggested Areas to Test 250 The Artisan´s Tour 251 Tools in Chrome 251 The Bad Neighborhood Tour 251 Bad Neighborhoods in Chrome OS 251 The Personalization Tour 252 Ways to Customize
Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of Google Cloud Platform. Author Ekaba Bisong shows you how machine learning tools and techniques are used to predict or classify events based on a set of interactions between variables known as features or attributes in a particular dataset. He teaches you how deep learning extends the machine learning algorithm of neural networks to learn complex tasks that are difficult for computers to perform, such as recognizing faces and understanding languages. And you will know how to leverage cloud computing to accelerate data science and machine learning deployments. Building Machine Learning and Deep Learning Models on Google Cloud Platform is divided into eight parts that cover the fundamentals of machine learning and deep learning, the concept of data science and cloud services, programming for data science using the Python stack, Google Cloud Platform (GCP) infrastructure and products, advanced analytics on GCP, and deploying end-to-end machine learning solution pipelines on GCP. What You´ll Learn Understand the principles and fundamentals of machine learning and deep learning, the algorithms, how to use them, when to use them, and how to interpret your results Know the programming concepts relevant to machine and deep learning design and development using the Python stack Build and interpret machine and deep learning models Use Google Cloud Platform tools and services to develop and deploy large-scale machine learning and deep learning products Be aware of the different facets and design choices to consider when modeling a learning problem Productionalize machine learning models into software products Who This Book Is For Beginners to the practice of data science and applied machine learning, data scientists at all levels, machine learning engineers, Google Cloud Platform data engineers/architects, and software developers
Wo liegen strategische Risiken für Google? Sollten Supermärkte ein Cashback-System einführen? Würden alle deutschen Pkw auf unser Autobahnnetz passen? Dies alles sind typische Case-Fragen aus Bewerbungsgesprächen von namhaften Unternehmensberatungen. Wie schaffen Sie es, in einer realen Interview-Situation, solche komplexen Probleme in kurzer Zeit anzugehen? Geschrieben von Recruiting-erfahrenen Unternehmensberatern, die bei Firmen wie Accenture, BCG, Booz & Co., McKinsey, Oliver Wyman oder Roland Berger gearbeitet haben. Zahlreiche Zwischenfragen zum Trainieren von analytischen, strukturierenden und quantitativen Fähigkeiten Spezielle Cases zum Üben zu zweit oder in der Gruppe Verschiedene Aufgabenstellungen zu Strategie, Market Sizing, Competitive Response, Market Entry und Kreativität Einblicke in branchenspezifische Case-Knackpunkte sowie Insider-Tipps und Kommentare zur Optimierung der Case-Lösung
Broad and up-to-date coverage of the principles and practice in the fast moving area of Distributed Systems. Distributed Systems provides students of computer science and engineering with the skills they will need to design and maintain software for distributed applications. It will also be invaluable to software engineers and systems designers wishing to understand new and future developments in the field. From mobile phones to the Internet, our lives depend increasingly on distributed systems linking computers and other devices together in a seamless and transparent way. The fifth edition of this best-selling text continues to provide a comprehensive source of material on the principles and practice of distributed computer systems and the exciting new developments based on them, using a wealth of modern case studies to illustrate their design and development . The depth of coverage will enable students to evaluate existing distributed systems and design new ones. Features + Benefits Provides an understanding of the principles on which the Internet and other distributed systems are based, their architecture, algorithms and design and how they meet the demands of contemporary distributed applications. Broad and up-to-date coverage of the principles and practice in the fast moving area of Distributed Systems. Includes the key issues in the debate between components and web services as the way forward for industry. The depth of coverage will enable students to evaluate existing distributed systems and design new ones. Incorporates and anticipates the major developments in distributed systems technology. Case studies illustrate the design concepts for each major topic. Foundations 1 Characterization of DS 2 System Models 3 Networking and Internetworking 4 Interprocess Communication 5 Remote Invocation 6 Indirect Communication 7 Operating System Support Middleware 8 Dist. Objects and Components 9 Web Services 10Peer-to-Peer Systems System services 11 Security 12 Distributed File Systems 13 Name Services Distributed algorithms 14 Time and Global States 15 Coordination and Agreement Shared data 16 Transactions and Concurrency Control 17 Distributed Transactions 18 Replication New challenges 19 Mobile and Ubiquitous Computing 20 Distributed Multimedia Systems Substantial Case Study 21 Designing Distributed Systems: Google Case Study Broad and up-to-date coverage of the principles and practice in the fast moving area of Distributed Systems. Distributed Systems provides students of computer science and engineering with the skills they will need to design and maintain software for distributed applications. It will also be invaluable to software engineers and systems designers wishing to understand new and future developments in the field. From mobile phones to the Internet, our lives depend increasingly on distributed systems linking computers and other devices together in a seamless and transparent way. The fifth edition of this best-selling text continues to provide a comprehensive source of material on the principles and practice of distributed computer systems and the exciting new developments based on them, using a wealth of modern case studies to illustrate their design and development . The depth of coverage will enable students to evaluate existing distributed systems and design new ones.
The spread of the Internet into all areas of business activities has put a particular focus on business models. The digitalization of business processes is the driver of changes in company strategies and management practices alike. This textbook provides a structured and conceptual approach, allowing students and other readers to understand the commonalities and specifics of the respective business models. The book begins with an overview of the business model concept in general by presenting the development of business models, analyzing definitions of business models and discussing the significance of the success of business model management. In turn, Chapter 2 offers insights into and explanations of the business model concept and provides the underlying approaches and ideas behind business models. Building on these foundations, Chapter 3 outlines the fundamental aspects of the digital economy. In the following chapters the book examines various core models in the business to consumer (B2C) context. The chapters follow a 4-C approach that divides the digital B2C businesses into models focusing on content, commerce, context and connection. Each chapter describes one of the four models and provides information on the respective business model types, the value chain, core assets and competencies as well as a case study. Based on the example of Google, Chapter 8 merges these approaches and describes the development of a hybrid digital business model. Chapter 9 is dedicated to business-to-business (B2B) digital business models. It shows how companies focus on business solutions such as online provision of sourcing, sales, supportive collaboration and broker services. Chapter 10 shares insight into the innovation aspect of digital business models, presenting structures and processes of digital business model innovation. The book is rounded out by a comprehensive case study on Google/Alphabet that combines all aspects of digital business models. Conceived as a textbook for students in advanced undergraduate courses, the book will also be useful for professionals and practitioners involved in business model innovation, and applied researchers.
The third edition of Exploring Innovation offers an engaging new perspective on innovation. The book provides business students with a clear understanding of the nature of innovation and how it can be managed and fostered. Written in an accessible style, Exploring Innovation encourages students to challenge their pre-conceived ideas about innovation and to see it as a continuous, on-going process, by exploring some of the biggest developments in innovation. Lively discussions of key concepts are provide through numerous case studies, on a range of original products and services, bringing business theories to life. The new edition has been fully revised and updated with a more intuitive structure to now feature: * A greater emphasis on what innovation involves. * A new chapter on Value Capture. * Expanded coverage on Services and Process Innovations. * Two new chapters covering Global and Green trends in innovation. * 8 new major case studies and more than 40 new mini-cases including Twitter, Angry Birds, Netflick, Google and Toyota.
This book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis. The Big Data phenomenon is increasingly impacting all sectors of business and industry, producing an emerging new information ecosystem. On the applications front, the book offers detailed descriptions of various application areas for Big Data Analytics in the important domains of Social Semantic Web Mining, Banking and Financial Services, Capital Markets, Insurance, Advertisement, Recommendation Systems, Bio-Informatics, the IoT and Fog Computing, before delving into issues of security and privacy. With regard to machine learning techniques, the book presents all the standard algorithms for learning - including supervised, semi-supervised and unsupervised techniques such as clustering and reinforcement learning techniques to perform collective Deep Learning. Multi-layered and nonlinear learning for Big Data are also covered. In turn, the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deutsche Bank, the power provider Opower, Delta Airlines and a Chinese City Transportation application represent a valuable addition. Given its comprehensive coverage of Big Data Analytics, the book offers a unique resource for undergraduate and graduate students, researchers, educators and IT professionals alike.
Research Methods in Human-Computer Interaction is a comprehensive guide to performing research and is essential reading for both quantitative and qualitative methods. Since the first edition was published in 2009, the book has been adopted for use at leading universities around the world, including Harvard University, Carnegie-Mellon University, the University of Washington, the University of Toronto, HiOA (Norway), KTH (Sweden), Tel Aviv University (Israel), and many others. Chapters cover a broad range of topics relevant to the collection and analysis of HCI data, going beyond experimental design and surveys, to cover ethnography, diaries, physiological measurements, case studies, crowdsourcing, and other essential elements in the well-informed HCI researcher´s toolkit. Continual technological evolution has led to an explosion of new techniques and a need for this updated 2nd edition, to reflect the most recent research in the field and newer trends in research methodology. This Research Methods in HCI revision contains updates throughout, including more detail on statistical tests, coding qualitative data, and data collection via mobile devices and sensors. Other new material covers performing research with children, older adults, and people with cognitive impairments. Comprehensive and updated guide to the latest research methodologies and approaches, and now available in EPUB3 format (choose any of the ePub or Mobi formats after purchase of the eBook) Expanded discussions of online datasets, crowdsourcing, statistical tests, coding qualitative data, laws and regulations relating to the use of human participants, and data collection via mobile devices and sensors New material on performing research with children, older adults, and people with cognitive impairments, two new case studies from Google and Yahoo!, and techniques for expanding the influence of your research to reach non-researcher audiences, including software developers and policymakers
The seventh edition of The Business Environment has been perfectly tailored to cover the core topics that will be studied on an introductory Business Environment module. This fully updated new edition provides comprehensive coverage of the varying factors that make up the business environment, with a particular focus on how these factors impact business organisations and the decisions organisations make. Key Features: Up-to-date coverage The business environment continues to evolve, and this new edition takes on board recent issues including: 1. The after-effects of the ´credit crunch´ 2. The emerging economic power of China, India and Brazil 3. Data security and privacy 4. Business ethics 5. Cultural identity 6. Climate change Real life examples New opening vignettes introduce the main topic and show the business environment in real life. In addition, the book contains a wealth of shorter and longer case studies featuring companies such as Google, Amazon and Virgin Trains. Pedagogy Clearly written and user friendly, the book boasts a full range of learning tools which include: Learning Objectives, Thinking Around the Subject boxes, Review Questions, and Activities.