It guides you step by step from simple tests to tests that are maintainable, readable, and trustworthy. It covers advanced subjects like mocks, stubs, and frameworks such as Typemock Isolator and Rhino Mocks. And you'll learn about advanced test patterns and organization, working with legacy code and even untestable code. The book discusses tools you need when testing databases and other technologies. It's written for .NET developers but others will also benefit from this book.Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.Table of ContentsThe basics of unit testingA first unit testUsing stubs to break dependenciesInteraction testing using mock objectsIsolation (mock object) frameworksTest hierarchies and organizationThe pillars of good testsIntegrating unit testing into the organizationWorking with legacy code

    With "React: Up and Running" you'll learn how to get off the ground with React, with no prior knowledge.This book teaches you how to build components, the building blocks of your apps, as well as how to organize the components into large-scale apps. In addition, you ll learn about unit testing and optimizing performance, while focusing on the application s data (and letting the UI take care of itself)."

    One slogan of Perl is that it makes easy things easy and hard things possible. "Intermediate Perl" is about making the leap from the easy things to the hard ones.Originally released in 2003 as "Learning Perl Objects, References, and Modules" and revised and updated for Perl 5.8, this book offers a gentle but thorough introduction to intermediate programming in Perl. Written by the authors of the best-selling "Learning Perl," it picks up where that book left off. Topics include: Packages and namespacesReferences and scopingManipulating complex data structuresObject-oriented programmingWriting and using modulesTesting Perl codeContributing to CPANFollowing the successful format of "Learning Perl," we designed each chapter in the book to be small enough to be read in just an hour or two, ending with a series of exercises to help you practice what you've learned. To use the book, you just need to be familiar with the material in "Learning Perl" and have ambition to go further.Perl is a different language to different people. It is a quick scripting tool for some, and a fully-featured object-oriented language for others. It is used for everything from performing quick global replacements on text files, to crunching huge, complex sets of scientific data that take weeks to process. Perl is what you make of it. But regardless of what you use Perl for, this book helps you do it more effectively, efficiently, and elegantly."Intermediate Perl" is about learning to use Perl as a programming language, and not just a scripting language. This is the book that turns the Perl dabbler into the Perl programmer.

    This groundbreaking new book sets out the principles and technical practices that enable rapid, incremental delivery of high quality, valuable new functionality to users. Through automation of the build, deployment, and testing process, and improved collaboration between developers, testers, and operations, delivery teams can get changes released in a matter of hours-- sometimes even minutes-no matter what the size of a project or the complexity of its code base. Jez Humble and David Farley begin by presenting the foundations of a rapid, reliable, low-risk delivery process. Next, they introduce the "deployment pipeline," an automated process for managing all changes, from check-in to release. Finally, they discuss the "ecosystem" needed to support continuous delivery, from infrastructure, data and configuration management to governance. The authors introduce state-of-the-art techniques, including automated infrastructure management and data migration, and the use of virtualization. For each, they review key issues, identify best practices, and demonstrate how to mitigate risks. Coverage includes - Automating all facets of building, integrating, testing, and deploying software - Implementing deployment pipelines at team and organizational levels - Improving collaboration between developers, testers, and operations - Developing features incrementally on large and distributed teams - Implementing an effective configuration management strategy - Automating acceptance testing, from analysis to implementation - Testing capacity and other non-functional requirements - Implementing continuous deployment and zero-downtime releases - Managing infrastructure, data, components and dependencies - Navigating risk management, compliance, and auditing Whether you're a developer, systems administrator, tester, or manager, this book will help your organization move from idea to release faster than ever--so you can deliver value to your business rapidly and reliably.

    Through example code, this guide demonstrates how CoffeeScript abstracts JavaScript, providing syntactical sugar and preventing many common errors. You’ll learn CoffeeScript’s syntax and idioms step by step, from basic variables and functions to complex comprehensions and classes.Written by Alex MacCaw, author of JavaScript Web Applications (O’Reilly), with contributions from CoffeeScript creator Jeremy Ashkenas, this book quickly teaches you best practices for using this language—not just on the client side, but for server-side applications as well. It’s time to take a ride with the little language that could.Discover how CoffeeScript’s syntax differs from JavaScriptLearn about features such as array comprehensions, destructuring assignments, and classesExplore CoffeeScript idioms and compare them to their JavaScript counterpartsCompile CoffeeScript files in static sites with the Cake build systemUse CommonJS modules to structure and deploy CoffeeScript client-side applicationsExamine JavaScript’s bad parts—including features CoffeeScript was able to fix

    Written from a computer science perspective by three leading experts in the field, it gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Although originally designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also create a buzz for researchers and professionals alike.

    Git permits virtually an infinite variety of methods for development and collaboration. Created by Linus Torvalds to manage development of the Linux kernel, it's become the principal tool for distributed version control. But Git's flexibility also means that some users don't understand how to use it to their best advantage. Version Control with Git offers tutorials on the most effective ways to use it, as well as friendly yet rigorous advice to help you navigate Git's many functions. With this book, you will:Learn how to use Git in several real-world development environments Gain insight into Git's common-use cases, initial tasks, and basic functions Understand how to use Git for both centralized and distributed version control Use Git to manage patches, diffs, merges, and conflicts Acquire advanced techniques such as rebasing, hooks, and ways to handle submodules (subprojects) Learn how to use Git with Subversion Git has earned the respect of developers around the world. Find out how you can benefit from this amazing tool with Version Control with Git.

    KEY TOPICS: Written in a reader-friendly style, the book encourages broad problem-solving skills while thoroughly covering the material required for introductory algorithms. The author emphasizes conceptual understanding before the introduction of the formal treatment of each technique. Popular puzzles are used to motivate readers' interest and strengthen their skills in algorithmic problem solving. Other enhancement features include chapter summaries, hints to the exercises, and a solution manual. MARKET: For those interested in learning more about algorithms.

    Python is easy to learn, and it's perfect for exploring topics like statistics, geometry, probability, and calculus. You’ll learn to write programs to find derivatives, solve equations graphically, manipulate algebraic expressions, even examine projectile motion.Rather than crank through tedious calculations by hand, you'll learn how to use Python functions and modules to handle the number crunching while you focus on the principles behind the math. Exercises throughout teach fundamental programming concepts, like using functions, handling user input, and reading and manipulating data. As you learn to think computationally, you'll discover new ways to explore and think about math, and gain valuable programming skills that you can use to continue your study of math and computer science.If you’re interested in math but have yet to dip into programming, you’ll find that Python makes it easy to go deeper into the subject—let Python handle the tedious work while you spend more time on the math.

    If you're a reasonably proficient programmer who can think logically, you have all the background you'll need. Stepanov and Rose introduce the relevant abstract algebra and number theory with exceptional clarity. They carefully explain the problems mathematicians first needed to solve, and then show how these mathematical solutions translate to generic programming and the creation of more effective and elegant code. To demonstrate the crucial role these mathematical principles play in many modern applications, the authors show how to use these results and generalized algorithms to implement a real-world public-key cryptosystem. As you read this book, you'll master the thought processes necessary for effective programming and learn how to generalize narrowly conceived algorithms to widen their usefulness without losing efficiency. You'll also gain deep insight into the value of mathematics to programming--insight that will prove invaluable no matter what programming languages and paradigms you use. You will learn aboutHow to generalize a four thousand-year-old algorithm, demonstrating indispensable lessons about clarity and efficiencyAncient paradoxes, beautiful theorems, and the productive tension between continuous and discreteA simple algorithm for finding greatest common divisor (GCD) and modern abstractions that build on itPowerful mathematical approaches to abstractionHow abstract algebra provides the idea at the heart of generic programmingAxioms, proofs, theories, and models: using mathematical techniques to organize knowledge about your algorithms and data structuresSurprising subtleties of simple programming tasks and what you can learn from themHow practical implementations can exploit theoretical knowledge

    Ready to master AngularJS? What if you could master the entire framework – with solid foundations – in less time without beating your head against a wall? Imagine how quickly you could work if you knew the best practices and the best tools? Stop wasting your time searching and have everything you need to be productive in one, well-organized place, with complete examples to get your project up without needing to resort to endless hours of research.

    Who are these people, what motivates them, and why should you care?Consider these facts: Everything around us is turning into computers. Your typewriter is gone, replaced by a computer. Your phone has turned into a computer. So has your camera. Soon your TV will. Your car was not only designed on computers, but has more processing power in it than a room-sized mainframe did in 1970. Letters, encyclopedias, newspapers, and even your local store are being replaced by the Internet.Hackers & Painters: Big Ideas from the Computer Age, by Paul Graham, explains this world and the motivations of the people who occupy it. In clear, thoughtful prose that draws on illuminating historical examples, Graham takes readers on an unflinching exploration into what he calls “an intellectual Wild West.”The ideas discussed in this book will have a powerful and lasting impact on how we think, how we work, how we develop technology, and how we live. Topics include the importance of beauty in software design, how to make wealth, heresy and free speech, the programming language renaissance, the open-source movement, digital design, internet startups, and more.

    This book will help those familiar the command line and basic shell scripting start using Ansible to provision and manage anywhere from one to thousands of servers.The book begins with fundamentals, like installing Ansible, setting up a basic inventory file, and basic concepts, then guides you through Ansible's many uses, including ad-hoc commands, basic and advanced playbooks, application deployments, custom modules, and special cases like running ansible in 'pull' mode when you have thousands of servers to manage (or more). Everything is explained with pertinent real-world examples, often using Vagrant-managed virtual machines.

    Using a downloadable programming environment developed by the author, Visualizing Data demonstrates methods for representing data accurately on the Web and elsewhere, complete with user interaction, animation, and more. How do the 3.1 billion A, C, G and T letters of the human genome compare to those of a chimp or a mouse? What do the paths that millions of visitors take through a web site look like? With Visualizing Data, you learn how to answer complex questions like these with thoroughly interactive displays. We're not talking about cookie-cutter charts and graphs. This book teaches you how to design entire interfaces around large, complex data sets with the help of a powerful new design and prototyping tool called "Processing". Used by many researchers and companies to convey specific data in a clear and understandable manner, the Processing beta is available free. With this tool and Visualizing Data as a guide, you'll learn basic visualization principles, how to choose the right kind of display for your purposes, and how to provide interactive features that will bring users to your site over and over. This book teaches you:The seven stages of visualizing data -- acquire, parse, filter, mine, represent, refine, and interact How all data problems begin with a question and end with a narrative construct that provides a clear answer without extraneous details Several example projects with the code to make them work Positive and negative points of each representation discussed. The focus is on customization so that each one best suits what you want to convey about your data set The book does not provide ready-made "visualizations" that can be plugged into any data set. Instead, with chapters divided by types of data rather than types of display, you'll learn how each visualization conveys the unique properties of the data it represents -- why the data was collected, what's interesting about it, and what stories it can tell. Visualizing Data teaches you how to answer questions, not simply display information.

    This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results