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Author: Andy Oram
Publisher: "O'Reilly Media, Inc."
ISBN: 9781449397760
Category : Computers
Languages : en
Pages : 624
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Book Description
Many claims are made about how certain tools, technologies, and practices improve software development. But which claims are verifiable, and which are merely wishful thinking? In this book, leading thinkers such as Steve McConnell, Barry Boehm, and Barbara Kitchenham offer essays that uncover the truth and unmask myths commonly held among the software development community. Their insights may surprise you. Are some programmers really ten times more productive than others? Does writing tests first help you develop better code faster? Can code metrics predict the number of bugs in a piece of software? Do design patterns actually make better software? What effect does personality have on pair programming? What matters more: how far apart people are geographically, or how far apart they are in the org chart? Contributors include: Jorge Aranda Tom Ball Victor R. Basili Andrew Begel Christian Bird Barry Boehm Marcelo Cataldo Steven Clarke Jason Cohen Robert DeLine Madeline Diep Hakan Erdogmus Michael Godfrey Mark Guzdial Jo E. Hannay Ahmed E. Hassan Israel Herraiz Kim Sebastian Herzig Cory Kapser Barbara Kitchenham Andrew Ko Lucas Layman Steve McConnell Tim Menzies Gail Murphy Nachi Nagappan Thomas J. Ostrand Dewayne Perry Marian Petre Lutz Prechelt Rahul Premraj Forrest Shull Beth Simon Diomidis Spinellis Neil Thomas Walter Tichy Burak Turhan Elaine J. Weyuker Michele A. Whitecraft Laurie Williams Wendy M. Williams Andreas Zeller Thomas Zimmermann
Author: Andy Oram
Publisher: "O'Reilly Media, Inc."
ISBN: 9781449397760
Category : Computers
Languages : en
Pages : 624
Get Book Here
Book Description
Many claims are made about how certain tools, technologies, and practices improve software development. But which claims are verifiable, and which are merely wishful thinking? In this book, leading thinkers such as Steve McConnell, Barry Boehm, and Barbara Kitchenham offer essays that uncover the truth and unmask myths commonly held among the software development community. Their insights may surprise you. Are some programmers really ten times more productive than others? Does writing tests first help you develop better code faster? Can code metrics predict the number of bugs in a piece of software? Do design patterns actually make better software? What effect does personality have on pair programming? What matters more: how far apart people are geographically, or how far apart they are in the org chart? Contributors include: Jorge Aranda Tom Ball Victor R. Basili Andrew Begel Christian Bird Barry Boehm Marcelo Cataldo Steven Clarke Jason Cohen Robert DeLine Madeline Diep Hakan Erdogmus Michael Godfrey Mark Guzdial Jo E. Hannay Ahmed E. Hassan Israel Herraiz Kim Sebastian Herzig Cory Kapser Barbara Kitchenham Andrew Ko Lucas Layman Steve McConnell Tim Menzies Gail Murphy Nachi Nagappan Thomas J. Ostrand Dewayne Perry Marian Petre Lutz Prechelt Rahul Premraj Forrest Shull Beth Simon Diomidis Spinellis Neil Thomas Walter Tichy Burak Turhan Elaine J. Weyuker Michele A. Whitecraft Laurie Williams Wendy M. Williams Andreas Zeller Thomas Zimmermann
Author: G. Longo
Publisher: Springer
ISBN: 3709130085
Category : Technology & Engineering
Languages : en
Pages : 337
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Book Description
Author: Sanjeev Arora
Publisher: Cambridge University Press
ISBN: 0521424267
Category : Computers
Languages : en
Pages : 609
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Book Description
New and classical results in computational complexity, including interactive proofs, PCP, derandomization, and quantum computation. Ideal for graduate students.
Author: Allen Downey
Publisher: "O'Reilly Media, Inc."
ISBN: 1449314635
Category : Computers
Languages : en
Pages : 159
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Book Description
Dive into Python's advanced possibilities, including algorithm analysis, graphs, scale-free networks, and cellular automata with this in-depth, hands-on guide.
Author: Max Kanat-Alexander
Publisher: "O'Reilly Media, Inc."
ISBN: 1449334695
Category : Computers
Languages : en
Pages : 85
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Book Description
Good software design is simple and easy to understand. Unfortunately, the average computer program today is so complex that no one could possibly comprehend how all the code works. This concise guide helps you understand the fundamentals of good design through scientific laws—principles you can apply to any programming language or project from here to eternity. Whether you’re a junior programmer, senior software engineer, or non-technical manager, you’ll learn how to create a sound plan for your software project, and make better decisions about the pattern and structure of your system. Discover why good software design has become the missing science Understand the ultimate purpose of software and the goals of good design Determine the value of your design now and in the future Examine real-world examples that demonstrate how a system changes over time Create designs that allow for the most change in the environment with the least change in the software Make easier changes in the future by keeping your code simpler now Gain better knowledge of your software’s behavior with more accurate tests
Author: Ue Kiao
Publisher:
ISBN:
Category :
Languages : en
Pages : 163
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Book Description
This book "Time Complexity Analysis" introduces you to the basics of Time Complexity notations, meaning of the Complexity values and How to analyze various Algorithmic problems. This book includes Time and Space Complexity cheat sheets at the end as a bonus resource. We have tackled several significant problems and demonstrated the approach to analyze them and arrived at the Time and Space Complexity of the problems and Algorithms. This is a MUST-READ book for all Computer Science students and Programmers. Do not miss this opportunity. You will get a better idea to judge which approach will work better and will be able to make better judgements in your development work. See the "Table of content" to get the list of exciting topics you will learn about. Some of the key points you will understand: Random Access Memory does not take O(1) time. It is complicated and in general, has a Time Complexity of O(√N). Multiplication takes O(N^2) time, but the most optimal Algorithm (developed in 2019) takes O(N logN) time which is believed to be the theoretical limit. As per Time Complexity, finding the largest element and the i-th largest element takes the same order of time. It is recommended that you go through this book twice. First time, you may skip the minute details that you may not understand at first go and get the overview. In the second reading, you will get all the ideas, and this will strengthen your insights. In 1950s, Computing was not a Science. It was a collective effort by several Computer Scientists such as Robert Tarjan and Philippe Flajolet who analyzed several computational problems to demonstrate that Computation Problems are equally complicated as Physics and Mathematics Problems. The ideas captured in this book include some of these analyses which glorified Computer Science and made it a Scientific field. Book: Time Complexity Analysis Authors: Aditya Chatterjee; Ue Kiao, PhD. Contributors (7): Vansh Pratap Singh, Shreya Shah, Vikram Shishupalsingh Bais, Mallika Dey, Siddhant Rao, Shweta Bhardwaj, K. Sai Drishya. Table of content: 1. Introduction to Time and Space Complexity (+ different notations) 2. How to calculate Time Complexity? 3. Meaning of different Time Complexity 4. Brief Background on NP and P 5. Does O(1) time exist?: Cost of accessing Memory 6. Time Complexity of Basic Arithmetic Operations 6.1. Bitwise operations 6.2. Addition 6.3. Subtraction 6.4. Multiplication 6.5. Division 7. Analysis of Array 8. Analysis of Dynamic Array 9. Find largest element 10. Find Second largest element 11. Find i-th largest element 12. Time Complexity Bound for comparison-based sorting 12.1. Analysis of Selection Sort 12.2. Analysis of Insertion Sort 12.3. Analysis of Bubble Sort 12.4. Analysis of Quick Sort 13. Bound for non-comparison-based sorting 13.1. Analysis of Counting Sort 13.2. Analysis of Bucket Sort 14. Analysis of Linked List 15. Analysis of Hash functions 16. Analysis of Binary Search 17. Time and Space Complexity Cheat Sheets There is no other book that cover these topics. Many students have several misconceptions which are resolved with the book. Read this book and level up.
Author: G. Longo
Publisher:
ISBN: 9783709130094
Category :
Languages : en
Pages : 352
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Book Description
Author: Neil D. Jones
Publisher: MIT Press
ISBN: 9780262100649
Category : Computers
Languages : en
Pages : 494
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Book Description
Computability and complexity theory should be of central concern to practitioners as well as theorists. Unfortunately, however, the field is known for its impenetrability. Neil Jones's goal as an educator and author is to build a bridge between computability and complexity theory and other areas of computer science, especially programming. In a shift away from the Turing machine- and G�del number-oriented classical approaches, Jones uses concepts familiar from programming languages to make computability and complexity more accessible to computer scientists and more applicable to practical programming problems. According to Jones, the fields of computability and complexity theory, as well as programming languages and semantics, have a great deal to offer each other. Computability and complexity theory have a breadth, depth, and generality not often seen in programming languages. The programming language community, meanwhile, has a firm grasp of algorithm design, presentation, and implementation. In addition, programming languages sometimes provide computational models that are more realistic in certain crucial aspects than traditional models. New results in the book include a proof that constant time factors do matter for its programming-oriented model of computation. (In contrast, Turing machines have a counterintuitive "constant speedup" property: that almost any program can be made to run faster, by any amount. Its proof involves techniques irrelevant to practice.) Further results include simple characterizations in programming terms of the central complexity classes PTIME and LOGSPACE, and a new approach to complete problems for NLOGSPACE, PTIME, NPTIME, and PSPACE, uniformly based on Boolean programs. Foundations of Computing series
Author: Philip Koopman
Publisher: Independently Published
ISBN:
Category :
Languages : en
Pages : 386
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Book Description
A classic book for professional embedded system designers, now in an affordable paperback edition. This book distills the experience of more than 90 design reviews on real embedded systems into a set of bite-size lessons learned in the areas of software development process, requirements, architecture, design, implementation, verification & validation, and critical system properties. This is a concept book rather than a cut-and-paste the code book.Each chapter describes an area that tends to be a problem in embedded system design, symptoms that tend to indicate you need to make changes, the risks of not fixing problems in this area, and concrete ways to make your embedded system software better. Each of the 29 chapters is self-sufficient, permitting developers with a busy schedule to cherry-pick the best ideas to make their systems better right away.If you are relatively new to the area but have already learned the basics, this book will be an invaluable asset for taking your game to the next level. If you are experienced, this book provides a way to fill in any gaps. Once you have mastered this material, the book will serve as a source of reminders to make sure you haven't forgotten anything as you plan your next project. This is version 1.1 with some minor revisions from the 2010 hardcover edition. This is a paperback print-on-demand edition produced by Amazon.
Author: Adam Tornhill
Publisher: Pragmatic Bookshelf
ISBN: 1680505807
Category : Computers
Languages : en
Pages : 362
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Book Description
Are you working on a codebase where cost overruns, death marches, and heroic fights with legacy code monsters are the norm? Battle these adversaries with novel ways to identify and prioritize technical debt, based on behavioral data from how developers work with code. And that's just for starters. Because good code involves social design, as well as technical design, you can find surprising dependencies between people and code to resolve coordination bottlenecks among teams. Best of all, the techniques build on behavioral data that you already have: your version-control system. Join the fight for better code! Use statistics and data science to uncover both problematic code and the behavioral patterns of the developers who build your software. This combination gives you insights you can't get from the code alone. Use these insights to prioritize refactoring needs, measure their effect, find implicit dependencies between different modules, and automatically create knowledge maps of your system based on actual code contributions. In a radical, much-needed change from common practice, guide organizational decisions with objective data by measuring how well your development teams align with the software architecture. Discover a comprehensive set of practical analysis techniques based on version-control data, where each point is illustrated with a case study from a real-world codebase. Because the techniques are language neutral, you can apply them to your own code no matter what programming language you use. Guide organizational decisions with objective data by measuring how well your development teams align with the software architecture. Apply research findings from social psychology to software development, ensuring you get the tools you need to coach your organization towards better code. If you're an experienced programmer, software architect, or technical manager, you'll get a new perspective that will change how you work with code. What You Need: You don't have to install anything to follow along in the book. TThe case studies in the book use well-known open source projects hosted on GitHub. You'll use CodeScene, a free software analysis tool for open source projects, for the case studies. We also discuss alternative tooling options where they exist.
