And then we wonder why software is notorious for being delivered late and full of bugs, while other engineers routinely deliver finished bridges, automobiles, electrical appliances, etc., on time and with only minor defects. This book sets out to redress this imbalance. Members of my advanced development team at Adobe who took the course based on the same material all benefited greatly from the time invested. It may appear as a highly technical text intended only for computer scientists, but it should be required reading for all practicing software engineers." --Martin Newell, Adobe Fellow"The book contains some of the most beautiful code I have ever seen." --Bjarne Stroustrup, Designer of C++"I am happy to see the content of Alex's course, the development and teaching of which I strongly supported as the CTO of Silicon Graphics, now available to all programmers in this elegant little book." --Forest Baskett, General Partner, New Enterprise Associates"Paul's patience and architectural experience helped to organize Alex's mathematical approach into a tightly-structured edifice--an impressive feat!" --Robert W. Taylor, Founder of Xerox PARC CSL and DEC Systems Research Center Elements of Programming provides a different understanding of programming than is presented elsewhere. Its major premise is that practical programming, like other areas of science and engineering, must be based on a solid mathematical foundation. The book shows that algorithms implemented in a real programming language, such as C++, can operate in the most general mathematical setting. For example, the fast exponentiation algorithm is defined to work with any associative operation. Using abstract algorithms leads to efficient, reliable, secure, and economical software.This is not an easy book. Nor is it a compilation of tips and tricks for incremental improvements in your programming skills. The book's value is more fundamental and, ultimately, more critical for insight into programming. To benefit fully, you will need to work through it from beginning to end, reading the code, proving the lemmas, and doing the exercises. When finished, you will see how the application of the deductive method to your programs assures that your system's software components will work together and behave as they must.The book presents a number of algorithms and requirements for types on which they are defined. The code for these descriptions--also available on the Web--is written in a small subset of C++ meant to be accessible to any experienced programmer. This subset is defined in a special language appendix coauthored by Sean Parent and Bjarne Stroustrup.Whether you are a software developer, or any other professional for whom programming is an important activity, or a committed student, you will come to understand what the book's experienced authors have been teaching and demonstrating for years--that mathematics is good for programming, and that theory is good for practice.

    Building these systems is complicated and, because few formally established patterns are available for designing them, most of these systems end up looking very unique. This practical guide shows you how to use existing software design patterns for designing and building reliable distributed applications.Although patterns such as those developed more than 20 years ago by the Gang of Four were largely restricted to running on single machines, author Brendan Burns--a Partner Architect in Microsoft Azure--demonstrates how you can reuse several of them in modern distributed applications.Systems engineers and application developers will learn how these patterns provide a common language and framework for dramatically increasing the quality of your system.

    Displaying extraordinary creativity and programming virtuosity, Alexandrescu offers a cutting-edge approach to design that unites design patterns, generic programming, and C++, enabling programmers to achieve expressive, flexible, and highly reusable code. This book introduces the concept of generic components--reusable design templates that produce boilerplate code for compiler consumption--all within C++. Generic components enable an easier and more seamless transition from design to application code, generate code that better expresses the original design intention, and support the reuse of design structures with minimal recoding. The author describes the specific C++ techniques and features that are used in building generic components and goes on to implement industrial strength generic components for real-world applications. Recurring issues that C++ developers face in their day-to-day activity are discussed in depth and implemented in a generic way. These include: Policy-based design for flexibility Partial template specialization Typelists--powerful type manipulation structures Patterns such as Visitor, Singleton, Command, and Factories Multi-method engines For each generic component, the book presents the fundamental problems and design options, and finally implements a generic solution.

    You want to learn HTML so you can finally create those web pages you've always wanted, so you can communicate more effectively with friends, family, fans, and fanatic customers. You also want to do it right so you can actually maintain and expand your Web pages over time, and so your web pages work in all the browsers and mobile devices out there. Oh, and if you've never heard of CSS, that's okay - we won't tell anyone you're still partying like it's 1999 - but if you're going to create Web pages in the 21st century then you'll want to know and understand CSS.Learn the real secrets of creating Web pages, and why everything your boss told you about HTML tables is probably wrong (and what to do instead). Most importantly, hold your own with your co-worker (and impress cocktail party guests) when he casually mentions how his HTML is now strict, and his CSS is in an external style sheet.With Head First HTML with CSS & XHTML, you'll avoid the embarrassment of thinking web-safe colors still matter, and the foolishness of slipping a font tag into your pages. Best of all, you'll learn HTML and CSS in a way that won't put you to sleep. If you've read a Head First book, you know what to expect: a visually-rich format designed for the way your brain works. Using the latest research in neurobiology, cognitive science, and learning theory, this book will load HTML, CSS, and XHTML into your brain in a way that sticks.So what are you waiting for? Leave those other dusty books behind and come join us in Webville. Your tour is about to begin."Elegant design is at the core of every chapter here, each concept conveyed with equal doses of pragmatism and wit." --Ken Goldstein, Executive Vice President, Disney Online"This book is a thoroughly modern introduction to forward-looking practices in web page markup and presentation." --Danny Goodman, author of Dynamic HTML: The Definitive Guide"What used to be a long trial and error learning process has now been reduced neatly into an engaging paperback." --Mike Davidson, CEO, Newsvine, Inc."I love Head First HTML with CSS & XHTML--it teaches you everything you need to learn in a 'fun coated' format!" --Sally Applin, UI Designer and Artist"I haven't had as much fun reading a book (other than Harry Potter) in years. And your book finally helped me break out of my hapless so-last-century way of creating web pages." --Professor David M. Arnow, Department of Computer and Information Science, Brooklyn College"If you've ever had a family member who wanted you to design a website for them, buy them Head First HTML with CSS and XHTML. If you've ever asked a family member to design you a web site, buy this book. If you've ever bought an HTML book and ended up using it to level your desk, or for kindling on a cold winter day, buy this book. This is the book you've been waiting for. This is the learning system you've been waiting for." --Warren Kelly, Blogcritics.org

    It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing.Use the IPython interactive shell as your primary development environmentLearn basic and advanced NumPy (Numerical Python) featuresGet started with data analysis tools in the pandas libraryUse high-performance tools to load, clean, transform, merge, and reshape dataCreate scatter plots and static or interactive visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsMeasure data by points in time, whether it's specific instances, fixed periods, or intervalsLearn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

    The authors explain each category of vulnerability using real-world examples, screen shots and code extracts. The book is extremely practical in focus, and describes in detail the steps involved in detecting and exploiting each kind of security weakness found within a variety of applications such as online banking, e-commerce and other web applications. The topics covered include bypassing login mechanisms, injecting code, exploiting logic flaws and compromising other users. Because every web application is different, attacking them entails bringing to bear various general principles, techniques and experience in an imaginative way. The most successful hackers go beyond this, and find ways to automate their bespoke attacks. This handbook describes a proven methodology that combines the virtues of human intelligence and computerized brute force, often with devastating results.The authors are professional penetration testers who have been involved in web application security for nearly a decade. They have presented training courses at the Black Hat security conferences throughout the world. Under the alias "PortSwigger," Dafydd developed the popular Burp Suite of web application hack tools.

    A focus is placed on the principles and practices of decomposing a complex problem space as well as the implementation patterns and best practices for shaping a maintainable solution space.

    These principles have revolutionized manufacturing and have been adopted by the most innovative product companies including Toyota and 3M. In 2003 the Poppendieck's published Lean Software Development: An Agile Toolkit which showed how these same lean principles can be successfully applied to software development. Since that publication the authors have increased their understanding of Lean and Agile problems faced by large organizations and have emerged as leading advocates for bringing Lean production techniques to software development. While their first book provides an introduction, theoretical advice and a reference to Lean, this follow-up incorporates their gained knowledge and understanding of what works and goes steps further to provide hands-on guidance for implementing a Lean system. Using historical case studies from prominent companies such as Polaris, Lockheed and Fujistu the authors prove the overall value of Lean practices and shows how to effectively apply these methods to software production.

    The book teaches a range of design and analysis techniques for problems that arise in computing applications. The text encourages an understanding of the algorithm design process and an appreciation of the role of algorithms in the broader field of computer science.

    There are currently a significant number of books on creating, documenting, and implementing software architecture, but precious few resources have addressed how to build a software architecture that aligns with a customer's overall business goals. In this new book, Luke Hohmann borrows from his extensive experience managing successful enterprise software projects to provide practical wisdom on creating and sustaining winning software solutions. This book helps technologists grasp the business ramifications of their decisions, and provides business-oriented software professionals (e.g. sales people and marketers) with better knowledge of how robust software can be built and maintained.

    How can software developers, programmers and managers meet the challenges of the 90s and begin to resolve the software crisis?

    You thought TDD was for someone else, but it's not! It's for you, the embedded C programmer. TDD helps you prevent defects and build software with a long useful life. This is the first book to teach the hows and whys of TDD for C programmers. TDD is a modern programming practice C developers need to know. It's a different way to program---unit tests are written in a tight feedback loop with the production code, assuring your code does what you think. You get valuable feedback every few minutes. You find mistakes before they become bugs. You get early warning of design problems. You get immediate notification of side effect defects. You get to spend more time adding valuable features to your product. James is one of the few experts in applying TDD to embedded C. With his 1.5 decades of training, coaching, and practicing TDD in C, C++, Java, and C# he will lead you from being a novice in TDD to using the techniques that few have mastered. This book is full of code written for embedded C programmers. You don't just see the end product, you see code and tests evolve. James leads you through the thought process and decisions made each step of the way. You'll learn techniques for test-driving code right next to the hardware, and you'll learn design principles and how to apply them to C to keep your code clean and flexible. To run the examples in this book, you will need a C/C++ development environment on your machine, and the GNU GCC tool chain or Microsoft Visual Studio for C++ (some project conversion may be needed).

    Expanded and updated for .NET 3.5, this new edition focuses on the design issues that directly affect the programmability of a class library, specifically its publicly accessible APIs. This book can improve the work of any .NET developer producing code that other developers will use. It includes copious annotations to the guidelines by thirty-five prominent architects and practitioners of the .NET Framework, providing a lively discussion of the reasons for the guidelines as well as examples of when to break those guidelines. Microsoft architects Krzysztof Cwalina and Brad Abrams teach framework design from the top down. From their significant combined experience and deep insight, you will learnThe general philosophy and fundamental principles of framework design Naming guidelines for the various parts of a framework Guidelines for the design and extending of types and members of types Issues affecting-and guidelines for ensuring-extensibility How (and how "not") to design exceptions Guidelines for-and examples of-common framework design patternsGuidelines in this book are presented in four major forms: Do, Consider, Avoid, and Do not. These directives help focus attention on practices that should "always" be used, those that should "generally" be used, those that should "rarely" be used, and those that should "never" be used. Every guideline includes a discussion of its applicability, and most include a code example to help illuminate the dialogue. "Framework Design Guidelines, Second Edition, " is the only definitive source of best practices for managed code API development, direct from the architects themselves. A companion DVD includes the Designing .NET Class Libraries video series, instructional presentations by the authors on design guidelines for developing classes and components that extend the .NET Framework. A sample API specification and other useful resources and tools are also included.

    This cookbook includes more than 100 recipes to help you take advantage of REST, HTTP, and the infrastructure of the Web. You'll learn ways to design RESTful web services for client and server applications that meet performance, scalability, reliability, and security goals, no matter what programming language and development framework you use.Each recipe includes one or two problem statements, with easy-to-follow, step-by-step instructions for solving them, as well as examples using HTTP requests and responses, and XML, JSON, and Atom snippets. You'll also get implementation guidelines, and a discussion of the pros, cons, and trade-offs that come with each solution.Learn how to design resources to meet various application scenariosSuccessfully design representations and URIsImplement the hypertext constraint using links and link headersUnderstand when and how to use Atom and AtomPubKnow what and what not to do to support cachingLearn how to implement concurrency controlDeal with advanced use cases involving copying, merging, transactions, batch processing, and partial updatesSecure web services and support OAuth

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms