Understanding Computation explains theoretical computer science in a context you’ll recognize, helping you appreciate why these ideas matter and how they can inform your day-to-day programming.Rather than use mathematical notation or an unfamiliar academic programming language like Haskell or Lisp, this book uses Ruby in a reductionist manner to present formal semantics, automata theory, and functional programming with the lambda calculus. It’s ideal for programmers versed in modern languages, with little or no formal training in computer science.* Understand fundamental computing concepts, such as Turing completeness in languages* Discover how programs use dynamic semantics to communicate ideas to machines* Explore what a computer can do when reduced to its bare essentials* Learn how universal Turing machines led to today’s general-purpose computers* Perform complex calculations, using simple languages and cellular automata* Determine which programming language features are essential for computation* Examine how halting and self-referencing make some computing problems unsolvable* Analyze programs by using abstract interpretation and type systems

    Building on two widely acclaimed previous editions, Craig Larman has updated this book to fully reflect the new UML 2 standard, to help you master the art of object design, and to promote high-impact, iterative, and skillful agile modeling practices.Developers and students will learn object-oriented analysis and design (OOA/D) through three iterations of two cohesive, start-to-finish case studies. These case studies incrementally introduce key skills, essential OO principles and patterns, UML notation, and best practices. You won’t just learn UML diagrams - you’ll learn how to apply UML in the context of OO software development.Drawing on his unsurpassed experience as a mentor and consultant, Larman helps you understand evolutionary requirements and use cases, domain object modeling, responsibility-driven design, essential OO design, layered architectures, “Gang of Four” design patterns, GRASP, iterative methods, an agile approach to the Unified Process (UP), and much more. This edition’s extensive improvements include:- A stronger focus on helping you master OOA/D through case studies that demonstrate key OO principles and patterns, while also applying the UML- New coverage of UML 2, Agile Modeling, Test-Driven Development, and refactoring- Many new tips on combining iterative and evolutionary development with OOA/D- Updates for easier study, including new learning aids and graphics- New college educator teaching resources- Guidance on applying the UP in a light, agile spirit, complementary with other iterative methods such as XP and Scrum- Techniques for applying the UML to documenting architectures- A new chapter on evolutionary requirements, and much moreApplying UML and Patterns, Third Edition, is a lucid and practical introduction to thinking and designing with objects - and creating systems that are well crafted, robust, and maintainable.

    The authors present updated coverage of compilers based on research and techniques that have been developed in the field over the past few years. The book provides a thorough introduction to compiler design and covers topics such as context-free grammars, fine state machines, and syntax-directed translation.

    But what if you could have your computer do them for you?In "Automate the Boring Stuff with Python," you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand no prior programming experience required. Once you've mastered the basics of programming, you'll create Python programs that effortlessly perform useful and impressive feats of automation to: Search for text in a file or across multiple filesCreate, update, move, and rename files and foldersSearch the Web and download online contentUpdate and format data in Excel spreadsheets of any sizeSplit, merge, watermark, and encrypt PDFsSend reminder emails and text notificationsFill out online formsStep-by-step instructions walk you through each program, and practice projects at the end of each chapter challenge you to improve those programs and use your newfound skills to automate similar tasks.Don't spend your time doing work a well-trained monkey could do. Even if you've never written a line of code, you can make your computer do the grunt work. Learn how in "Automate the Boring Stuff with Python.""

    But the state-of-the-art has moved forward dramatically since Meyers last updated this book in 1997. (For instance, there s now STL. Design patterns. Even new functionality being added through TR1 and Boost.) So Meyers has done a top-to-bottom rewrite, identifying the 55 most valuable techniques you need now to be exceptionally effective with C++. Over half of this edition s content is new. Templates broadly impact C++ development, and you ll find them everywhere. There s extensive coverage of multithreaded systems. There s an entirely new chapter on resource management. You ll find substantial new coverage of exceptions. Much is gained, but nothing s lost: You ll find the same depth of practical insight that first made Effective C++ a classic all those years ago. Bill Camarda, from the July 2005 href="http://www.barnesandnoble.com/newslet... Only

    As traffic volume and data demands increase, these applications become more complicated and brittle, exposing risks and compromising availability. This practical guide shows IT, devops, and system reliability managers how to prevent an application from becoming slow, inconsistent, or downright unavailable as it grows.Scaling isn't just about handling more users; it's also about managing risk and ensuring availability. Author Lee Atchison provides basic techniques for building applications that can handle huge quantities of traffic, data, and demand without affecting the quality your customers expect.In five parts, this book explores:Availability: learn techniques for building highly available applications, and for tracking and improving availability going forwardRisk management: identify, mitigate, and manage risks in your application, test your recovery/disaster plans, and build out systems that contain fewer risksServices and microservices: understand the value of services for building complicated applications that need to operate at higher scaleScaling applications: assign services to specific teams, label the criticalness of each service, and devise failure scenarios and recovery plansCloud services: understand the structure of cloud-based services, resource allocation, and service distribution

    

    However, data structures for these languages do not always translate well to functional languages such as Standard ML, Haskell, or Scheme. This book describes data structures from the point of view of functional languages, with examples, and presents design techniques that allow programmers to develop their own functional data structures. The author includes both classical data structures, such as red-black trees and binomial queues, and a host of new data structures developed exclusively for functional languages. All source code is given in Standard ML and Haskell, and most of the programs are easily adaptable to other functional languages. This handy reference for professional programmers working with functional languages can also be used as a tutorial or for self-study.

    Be part of the solution. With this practical book, you'll learn 10 easy-to-follow guidelines for delivering software that's easy to maintain and adapt. These guidelines have been derived from analyzing hundreds of real-world systems.Written by consultants from the Software Improvement Group (SIG), this book provides clear and concise explanations, with advice for turning the guidelines into practice. Examples are written in Java, but this guide is equally useful for developers working in other programming languages.10 Coding Guidelines- Write short units of code: limit the length of methods and constructors- Write simple units of code: limit the number of branch points per method- Write code once, rather than risk copying buggy code- Keep unit interfaces small by extracting parameters into objects- Separate concerns to avoid building large classes- Couple architecture components loosely- Balance the number and size of top-level components in your code- Keep your codebase as small as possible- Automate tests for your codebase- Write clean code, avoiding "code smells" that indicate deeper problemsWhy you should read this bookTaken in isolation, the guidelines presented in this book are well-known. In fact, many well-known tools for code analysis check a number of the guidelines presented here. The following three characteristics set this book apart from other books on software development: We have selected the ten most important guidelines from experience.We teach how to comply with these ten guidelines.We present statistics and examples from real-world systems.This book is part our Training on Software Maintainability - and subsequent Certification on Quality Software Development program. For more information about this program, please contact training@sig.eu.

    Learn You a Haskell for Great Good! introduces programmers familiar with imperative languages (such as C++, Java, or Python) to the unique aspects of functional programming. Packed with jokes, pop culture references, and the author's own hilarious artwork, Learn You a Haskell for Great Good! eases the learning curve of this complex language, and is a perfect starting point for any programmer looking to expand his or her horizons. The well-known web tutorial on which this book is based is widely regarded as the best way for beginners to learn Haskell, and receives over 30,000 unique visitors monthly.

    As a modern application developer you need to understand the emerging field of data management, both RDBMS and NoSQL. Seven Databases in Seven Weeks takes you on a tour of some of the hottest open source databases today. In the tradition of Bruce A. Tate's Seven Languages in Seven Weeks, this book goes beyond your basic tutorial to explore the essential concepts at the core each technology. Redis, Neo4J, CouchDB, MongoDB, HBase, Riak and Postgres. With each database, you'll tackle a real-world data problem that highlights the concepts and features that make it shine. You'll explore the five data models employed by these databases-relational, key/value, columnar, document and graph-and which kinds of problems are best suited to each. You'll learn how MongoDB and CouchDB are strikingly different, and discover the Dynamo heritage at the heart of Riak. Make your applications faster with Redis and more connected with Neo4J. Use MapReduce to solve Big Data problems. Build clusters of servers using scalable services like Amazon's Elastic Compute Cloud (EC2). Discover the CAP theorem and its implications for your distributed data. Understand the tradeoffs between consistency and availability, and when you can use them to your advantage. Use multiple databases in concert to create a platform that's more than the sum of its parts, or find one that meets all your needs at once.Seven Databases in Seven Weeks will take you on a deep dive into each of the databases, their strengths and weaknesses, and how to choose the ones that fit your needs.What You Need: To get the most of of this book you'll have to follow along, and that means you'll need a *nix shell (Mac OSX or Linux preferred, Windows users will need Cygwin), and Java 6 (or greater) and Ruby 1.8.7 (or greater). Each chapter will list the downloads required for that database.

    You will be able to look over the shoulder of major coding and design experts to see problems through their eyes.This is not simply another design patterns book, or another software engineering treatise on the right and wrong way to do things. The authors think aloud as they work through their project's architecture, the tradeoffs made in its construction, and when it was important to break rules. Beautiful Code is an opportunity for master coders to tell their story. All author royalties will be donated to Amnesty International.

    Is it time to become a manager? Tell your boss he’s a jerk? Join that startup? Author Michael Lopp recalls his own make-or-break moments with Silicon Valley giants such as Apple, Netscape, and Symantec in Being Geek -- an insightful and entertaining book that will help you make better career decisions.With more than 40 standalone stories, Lopp walks through a complete job life cycle, starting with the job interview and ending with the realization that it might be time to find another gig. Many books teach you how to interview for a job or how to manage a project successfully, but only this book helps you handle the baffling circumstances you may encounter throughout your career.Decide what you're worth with the chapter on "The Business"Determine the nature of the miracle your CEO wants with "The Impossible"Give effective presentations with "How Not to Throw Up"Handle liars and people with devious agendas with "Managing Werewolves"Realize when you should be looking for a new gig with "The Itch"

    The twelve-factor app is a methodology for building software-as-a-service apps that: - Use declarative formats for setup automation, to minimize time and cost for new developers joining the project; - Have a clean contract with the underlying operating system, offering maximum portability between execution environments; - Are suitable for deployment on modern cloud platforms, obviating the need for servers and systems administration; - Minimize divergence between development and production, enabling continuous deployment for maximum agility; - And can scale up without significant changes to tooling, architecture, or development practices.The twelve-factor methodology can be applied to apps written in any programming language, and which use any combination of backing services (database, queue, memory cache, etc).

    But recently, the industry has turned to dynamic languages for increased productivity and speed to market.Groovy is one of a new breed of dynamic languages that run on the Java platform. You can use these new languages on the JVM and intermix them with your existing Java code. You can leverage your Java investments while benefiting from advanced features including true Closures, Meta Programming, the ability to create internal DSLs, and a higher level of abstraction.If you're an experienced Java developer, Programming Groovy will help you learn the necessary fundamentals of programming in Groovy. You'll see how to use Groovy to do advanced programming including using Meta Programming, Builders, Unit Testing with Mock objects, processing XML, working with Databases and creating your own Domain-Specific Languages (DSLs).