Advertisement
| examples of abstraction in computer science: Computer Science National Research Council, Division on Engineering and Physical Sciences, Computer Science and Telecommunications Board, Committee on the Fundamentals of Computer Science: Challenges and Opportunities, 2004-10-06 Computer Science: Reflections on the Field, Reflections from the Field provides a concise characterization of key ideas that lie at the core of computer science (CS) research. The book offers a description of CS research recognizing the richness and diversity of the field. It brings together two dozen essays on diverse aspects of CS research, their motivation and results. By describing in accessible form computer science's intellectual character, and by conveying a sense of its vibrancy through a set of examples, the book aims to prepare readers for what the future might hold and help to inspire CS researchers in its creation. |
| examples of abstraction in computer science: Program Development in Java Barbara Liskov, John Guttag, 2001 Liskov (engineering, Massachusetts Institute of Technology) and Guttag (computer science and engineering, also at MIT) present a component- based methodology for software program development. The book focuses on modular program construction: how to get the modules right and how to organize a program as a collection of modules. It explains the key types of abstractions, demonstrates how to develop specifications that define these abstractions, and illustrates how to implement them using numerous examples. An introduction to key Java concepts is included. Annotation copyrighted by Book News, Inc., Portland, OR. |
| examples of abstraction in computer science: Principles of Abstract Interpretation Patrick Cousot, 2021-09-21 Introduction to abstract interpretation, with examples of applications to the semantics, specification, verification, and static analysis of computer programs. Formal methods are mathematically rigorous techniques for the specification, development, manipulation, and verification of safe, robust, and secure software and hardware systems. Abstract interpretation is a unifying theory of formal methods that proposes a general methodology for proving the correctness of computing systems, based on their semantics. The concepts of abstract interpretation underlie such software tools as compilers, type systems, and security protocol analyzers. This book provides an introduction to the theory and practice of abstract interpretation, offering examples of applications to semantics, specification, verification, and static analysis of programming languages with emphasis on calculational design. The book covers all necessary computer science and mathematical concepts--including most of the logic, order, linear, fixpoint, and discrete mathematics frequently used in computer science--in separate chapters before they are used in the text. Each chapter offers exercises and selected solutions. Chapter topics include syntax, parsing, trace semantics, properties and their abstraction, fixpoints and their abstractions, reachability semantics, abstract domain and abstract interpreter, specification and verification, effective fixpoint approximation, relational static analysis, and symbolic static analysis. The main applications covered include program semantics, program specification and verification, program dynamic and static analysis of numerical properties and of such symbolic properties as dataflow analysis, software model checking, pointer analysis, dependency, and typing (both for forward and backward analysis), and their combinations. Principles of Abstract Interpretation is suitable for classroom use at the graduate level and as a reference for researchers and practitioners. |
| examples of abstraction in computer science: How to Design Programs, second edition Matthias Felleisen, Robert Bruce Findler, Matthew Flatt, Shriram Krishnamurthi, 2018-05-25 A completely revised edition, offering new design recipes for interactive programs and support for images as plain values, testing, event-driven programming, and even distributed programming. This introduction to programming places computer science at the core of a liberal arts education. Unlike other introductory books, it focuses on the program design process, presenting program design guidelines that show the reader how to analyze a problem statement, how to formulate concise goals, how to make up examples, how to develop an outline of the solution, how to finish the program, and how to test it. Because learning to design programs is about the study of principles and the acquisition of transferable skills, the text does not use an off-the-shelf industrial language but presents a tailor-made teaching language. For the same reason, it offers DrRacket, a programming environment for novices that supports playful, feedback-oriented learning. The environment grows with readers as they master the material in the book until it supports a full-fledged language for the whole spectrum of programming tasks. This second edition has been completely revised. While the book continues to teach a systematic approach to program design, the second edition introduces different design recipes for interactive programs with graphical interfaces and batch programs. It also enriches its design recipes for functions with numerous new hints. Finally, the teaching languages and their IDE now come with support for images as plain values, testing, event-driven programming, and even distributed programming. |
| examples of abstraction in computer science: Concrete Abstractions Max Hailperin, Barbara Kaiser, Karl Knight, 1999 CONCRETE ABSTRACTIONS offers students a hands-on, abstraction-based experience of thinking like a computer scientist. This text covers the basics of programming and data structures, and gives first-time computer science students the opportunity to not only write programs, but to prove theorems and analyze algorithms as well. Students learn a variety of programming styles, including functional programming, assembly-language programming, and object-oriented programming (OOP). While most of the book uses the Scheme programming language, Java is introduced at the end as a second example of an OOP system and to demonstrate concepts of concurrent programming. |
| examples of abstraction in computer science: Programming Abstractions in C++ Eric Roberts, 2014 This text is intended for use in the second programming course Programming is a matter of learning by doing. Eric Roberts' Programming Abstractions in C++ gives students opportunities to practice and learn with engaging graphical assignments. A client-first approach to data structures helps students absorb, and then apply the material. Teaching and Learning Experience This program presents a better teaching and learning experience--for you and your students. It will help: Improve Student Comprehension with a Client-first Approach to Data Structures: To aid in student understanding, this book presents the full set of collection classes early. Defer the Presentation of C++ Features that Require a Detailed Understanding of the Underlying Machine: Introducing collection classes early enables students to master other equally important topics without having to struggle with low-level details at the same time. Engage Students with Exciting Graphical Assignments: An open-source library supports graphics and interactivity in a simple, pedagogically appropriate way. Support Instructors and Students: The companion website provides source code, sample run PDFs, answers to review questions, and more. |
| examples of abstraction in computer science: Simply Scheme Brian Harvey, Matthew Wright, 1999 Showing off scheme - Functions - Expressions - Defining your own procedures - Words and sentences - True and false - Variables - Higher-order functions - Lambda - Introduction to recursion - The leap of faith - How recursion works - Common patterns in recursive procedures - Advanced recursion - Example : the functions program - Files - Vectors - Example : a spreadsheet program - Implementing the spreadsheet program - What's next? |
| examples of abstraction in computer science: An Introduction to Functional Programming Through Lambda Calculus Greg Michaelson, 2013-04-10 Well-respected text for computer science students provides an accessible introduction to functional programming. Cogent examples illuminate the central ideas, and numerous exercises offer reinforcement. Includes solutions. 1989 edition. |
| examples of abstraction in computer science: Abstract Computing Machines Werner Kluge, 2005-02-18 The book emphasizes the design of full-fledged, fully normalizing lambda calculus machinery, as opposed to the just weakly normalizing machines. |
| examples of abstraction in computer science: Data Abstraction and Problem Solving with Java: Walls and Mirrors Janet Prichard, Frank M. Carrano, 2014-09-18 This edition of Data Abstraction and Problem Solving with Java: Walls and Mirrors employs the analogies of Walls (data abstraction) and Mirrors (recursion) to teach Java programming design solutions, in a way that beginning students find accessible. The book has a student-friendly pedagogical approach that carefully accounts for the strengths and weaknesses of the Java language. With this book, students will gain a solid foundation in data abstraction, object-oriented programming, and other problem-solving techniques. The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you'll gain instant access to this eBook. Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed. |
| examples of abstraction in computer science: Abstraction in Artificial Intelligence and Complex Systems Lorenza Saitta, Jean-Daniel Zucker, 2013-06-05 Abstraction is a fundamental mechanism underlying both human and artificial perception, representation of knowledge, reasoning and learning. This mechanism plays a crucial role in many disciplines, notably Computer Programming, Natural and Artificial Vision, Complex Systems, Artificial Intelligence and Machine Learning, Art, and Cognitive Sciences. This book first provides the reader with an overview of the notions of abstraction proposed in various disciplines by comparing both commonalities and differences. After discussing the characterizing properties of abstraction, a formal model, the KRA model, is presented to capture them. This model makes the notion of abstraction easily applicable by means of the introduction of a set of abstraction operators and abstraction patterns, reusable across different domains and applications. It is the impact of abstraction in Artificial Intelligence, Complex Systems and Machine Learning which creates the core of the book. A general framework, based on the KRA model, is presented, and its pragmatic power is illustrated with three case studies: Model-based diagnosis, Cartographic Generalization, and learning Hierarchical Hidden Markov Models. |
| examples of abstraction in computer science: Computer Science Subrata Dasgupta, 2016 While the development of Information Technology has been obvious to all, the underpinning computer science has been less apparent. Subrata Dasgupta provides a thought-provoking introduction to the field and its core principles, considering computer science as a science of symbol processing. |
| examples of abstraction in computer science: Hibernate Tips Thorben Janssen, 2018-01-09 When you use Hibernate in your projects, you quickly recognize that you need to do more than just add @Entity annotations to your domain model classes. Real-world applications often require advanced mappings, complex queries, custom data types and caching. Hibernate can do all of that. You just have to know which annotations and APIs you need to use. Hibernate Tips - More than 70 solutions to common Hibernate problems shows you how to efficiently implement your persistence layer with Hibernate's basic and advanced features. Each Hibernate Tip consists of one or more code samples and an easy to follow step-by-step explanation. You can also download an example project with executable test cases for each Hibernate Tip. Throughout this book, you will get more than 70 ready-to-use solutions that show you how to: - Define standard mappings for basic attributes and entity associations. - Implement your own attribute mappings and support custom data types. - Use Hibernate's Java 8 support and other proprietary features. - Read data from the database with JPQL, Criteria API, and native SQL queries. - Call stored procedures and database functions. This book is for developers who are already working with Hibernate and who are looking for solutions for their current development tasks. It's not a book for beginners who are looking for extensive descriptions of Hibernate's general concepts. The tips are designed as self-contained recipes which provide a specific solution and can be accessed when needed. Most of them contain links to related tips which you can follow if you want to dive deeper into a topic or need a slightly different solution. There is no need to read the tips in a specific order. Feel free to read the book from cover to cover or to just pick the tips that help you in your current project. |
| examples of abstraction in computer science: Learning to Program Steven Foote, 2014 Learning to Program will help students build a solid foundation in programming that can prepare them to achieve just about any programming goal. Whether they want to become a professional software programmer, learn how to more effectively communicate with programmers, or are just curious about how programming works, this book is a great first step in helping to get there. |
| examples of abstraction in computer science: Abstraction, Refinement and Proof for Probabilistic Systems Annabelle McIver, Carroll Morgan, 2005 Provides an integrated coverage of random/probabilistic algorithms, assertion-based program reasoning, and refinement programming models, providing a focused survey on probabilistic program semantics. This book illustrates, by examples, the typical steps necessary to build a mathematical model of any programming paradigm. |
| examples of abstraction in computer science: The Productive Programmer Neal Ford, 2008-07-03 Anyone who develops software for a living needs a proven way to produce it better, faster, and cheaper. The Productive Programmer offers critical timesaving and productivity tools that you can adopt right away, no matter what platform you use. Master developer Neal Ford not only offers advice on the mechanics of productivity-how to work smarter, spurn interruptions, get the most out your computer, and avoid repetition-he also details valuable practices that will help you elude common traps, improve your code, and become more valuable to your team. You'll learn to: Write the test before you write the code Manage the lifecycle of your objects fastidiously Build only what you need now, not what you might need later Apply ancient philosophies to software development Question authority, rather than blindly adhere to standards Make hard things easier and impossible things possible through meta-programming Be sure all code within a method is at the same level of abstraction Pick the right editor and assemble the best tools for the job This isn't theory, but the fruits of Ford's real-world experience as an Application Architect at the global IT consultancy ThoughtWorks. Whether you're a beginner or a pro with years of experience, you'll improve your work and your career with the simple and straightforward principles in The Productive Programmer. |
| examples of abstraction in computer science: Vacant Fire Ray Gardener, 2019-05-17 Alan Fisher was a young engineer with a dream of deriving morality from the laws of physics. But he got more than he bargained for when he accidentally discovered a shocking possibility: that not all people are conscious. Now he and an emergency team at DARPA must find the answers - and the cure - before the world implodes in a hotbed of prejudice and fear, and the powerful, greedy, and racist exploit his discovery to risk evil beyond imagining.A tense and often disturbing near-future thriller that examines science, discrimination, and just how thin society's veneer of acceptance and tolerance really is. A gripping and entertaining read. -- J.V. Bolkan for IndieReader (4.6 rating) |
| examples of abstraction in computer science: Exploring Computer Science with Scheme Oliver Grillmeyer, 2013-04-17 A presentation of the central and basic concepts, techniques, and tools of computer science, with the emphasis on presenting a problem-solving approach and on providing a survey of all of the most important topics covered in degree programmes. Scheme is used throughout as the programming language and the author stresses a functional programming approach to create simple functions so as to obtain the desired programming goal. Such simple functions are easily tested individually, which greatly helps in producing programs that work correctly first time. Throughout, the author aids to writing programs, and makes liberal use of boxes with Mistakes to Avoid. Programming examples include: * abstracting a problem; * creating pseudo code as an intermediate solution; * top-down and bottom-up design; * building procedural and data abstractions; * writing progams in modules which are easily testable. Numerous exercises help readers test their understanding of the material and develop ideas in greater depth, making this an ideal first course for all students coming to computer science for the first time. |
| examples of abstraction in computer science: A Certain Ambiguity Gaurav Suri, Hartosh Singh Bal, 2010-07-01 While taking a class on infinity at Stanford in the late 1980s, Ravi Kapoor discovers that he is confronting the same mathematical and philosophical dilemmas that his mathematician grandfather had faced many decades earlier--and that had landed him in jail. Charged under an obscure blasphemy law in a small New Jersey town in 1919, Vijay Sahni is challenged by a skeptical judge to defend his belief that the certainty of mathematics can be extended to all human knowledge--including religion. Together, the two men discover the power--and the fallibility--of what has long been considered the pinnacle of human certainty, Euclidean geometry. As grandfather and grandson struggle with the question of whether there can ever be absolute certainty in mathematics or life, they are forced to reconsider their fundamental beliefs and choices. Their stories hinge on their explorations of parallel developments in the study of geometry and infinity--and the mathematics throughout is as rigorous and fascinating as the narrative and characters are compelling and complex. Moving and enlightening, A Certain Ambiguity is a story about what it means to face the extent--and the limits--of human knowledge. |
| examples of abstraction in computer science: Software Abstractions Daniel Jackson, 2012 An approach to software design that introduces a fully automated analysis giving designers immediate feedback, now featuring the latest version of the Alloy language. In Software Abstractions Daniel Jackson introduces an approach to software design that draws on traditional formal methods but exploits automated tools to find flaws as early as possible. This approach—which Jackson calls “lightweight formal methods” or “agile modeling”—takes from formal specification the idea of a precise and expressive notation based on a tiny core of simple and robust concepts but replaces conventional analysis based on theorem proving with a fully automated analysis that gives designers immediate feedback. Jackson has developed Alloy, a language that captures the essence of software abstractions simply and succinctly, using a minimal toolkit of mathematical notions. This revised edition updates the text, examples, and appendixes to be fully compatible with Alloy 4. |
| examples of abstraction in computer science: The Sciences of the Artificial, reissue of the third edition with a new introduction by John Laird Herbert A. Simon, 2019-08-13 Herbert Simon's classic work on artificial intelligence in the expanded and updated third edition from 1996, with a new introduction by John E. Laird. Herbert Simon's classic and influential The Sciences of the Artificial declares definitively that there can be a science not only of natural phenomena but also of what is artificial. Exploring the commonalities of artificial systems, including economic systems, the business firm, artificial intelligence, complex engineering projects, and social plans, Simon argues that designed systems are a valid field of study, and he proposes a science of design. For this third edition, originally published in 1996, Simon added new material that takes into account advances in cognitive psychology and the science of design while confirming and extending the book's basic thesis: that a physical symbol system has the necessary and sufficient means for intelligent action. Simon won the Nobel Prize for Economics in 1978 for his research into the decision-making process within economic organizations and the Turing Award (considered by some the computer science equivalent to the Nobel) with Allen Newell in 1975 for contributions to artificial intelligence, the psychology of human cognition, and list processing. The Sciences of the Artificial distills the essence of Simon's thought accessibly and coherently. This reissue of the third edition makes a pioneering work available to a new audience. |
| examples of abstraction in computer science: Computational Thinking and Coding for Every Student Jane Krauss, Kiki Prottsman, 2016-10-28 Empower tomorrow’s tech innovators Our students are avid users and consumers of technology. Isn’t it time that they see themselves as the next technological innovators, too? Computational Thinking and Coding for Every Student is the beginner’s guide for K-12 educators who want to learn to integrate the basics of computer science into their curriculum. Readers will find Practical strategies for teaching computational thinking and the beginning steps to introduce coding at any grade level, across disciplines, and during out-of-school time Instruction-ready lessons and activities for every grade Specific guidance for designing a learning pathway for elementary, middle, or high school students Justification for making coding and computer science accessible to all A glossary with definitions of key computer science terms, a discussion guide with tips for making the most of the book, and companion website with videos, activities, and other resources Momentum for computer science education is growing as educators and parents realize how fundamental computing has become for the jobs of the future. This book is for educators who see all of their students as creative thinkers and active contributors to tomorrow’s innovations. Kiki Prottsman and Jane Krauss have been at the forefront of the rising popularity of computer science and are experts in the issues that the field faces, such as equity and diversity. In this book, they’ve condensed years of research and practitioner experience into an easy to read narrative about what computer science is, why it is important, and how to teach it to a variety of audiences. Their ideas aren’t just good, they are research-based and have been in practice in thousands of classrooms...So to the hundreds and thousands of teachers who are considering, learning, or actively teaching computer science—this book is well worth your time. Pat Yongpradit Chief Academic Officer, Code.org |
| examples of abstraction in computer science: Structure and Interpretation of Computer Programs Harold Abelson, Gerald Jay Sussman, 2022-05-03 A new version of the classic and widely used text adapted for the JavaScript programming language. Since the publication of its first edition in 1984 and its second edition in 1996, Structure and Interpretation of Computer Programs (SICP) has influenced computer science curricula around the world. Widely adopted as a textbook, the book has its origins in a popular entry-level computer science course taught by Harold Abelson and Gerald Jay Sussman at MIT. SICP introduces the reader to central ideas of computation by establishing a series of mental models for computation. Earlier editions used the programming language Scheme in their program examples. This new version of the second edition has been adapted for JavaScript. The first three chapters of SICP cover programming concepts that are common to all modern high-level programming languages. Chapters four and five, which used Scheme to formulate language processors for Scheme, required significant revision. Chapter four offers new material, in particular an introduction to the notion of program parsing. The evaluator and compiler in chapter five introduce a subtle stack discipline to support return statements (a prominent feature of statement-oriented languages) without sacrificing tail recursion. The JavaScript programs included in the book run in any implementation of the language that complies with the ECMAScript 2020 specification, using the JavaScript package sicp provided by the MIT Press website. |
| examples of abstraction in computer science: Foundations of Computer Science Alfred V. Aho, Jeffrey D. Ullman, 1994-10-15 |
| examples of abstraction in computer science: Programming Abstractions in Java Eric Roberts, |
| examples of abstraction in computer science: The Object of Data Abstraction and Structures Using Java David D. Riley, 2003 *JS123-6, 0-201-71359-4, Riley, David; The Object of Data Abstraction and Structures (Using Java) This book covers traditional data structures using an early object-oriented approach, and by paying special attention to developing sound software engineering skills. Provides extensive coverage of foundational material needed to study data structures (objects and classes, software specification, inheritance, exceptions, and recursion). Provides an object-oriented approach to abstract design using UML class diagrams and several design patterns. Emphasizes software-engineering skills as used in professional practice.MARKET Readers who want to use the most powerful features of Java to program data structures. |
| examples of abstraction in computer science: Principles of Computer System Design Jerome H. Saltzer, M. Frans Kaashoek, 2009-05-21 Principles of Computer System Design is the first textbook to take a principles-based approach to the computer system design. It identifies, examines, and illustrates fundamental concepts in computer system design that are common across operating systems, networks, database systems, distributed systems, programming languages, software engineering, security, fault tolerance, and architecture.Through carefully analyzed case studies from each of these disciplines, it demonstrates how to apply these concepts to tackle practical system design problems. To support the focus on design, the text identifies and explains abstractions that have proven successful in practice such as remote procedure call, client/service organization, file systems, data integrity, consistency, and authenticated messages. Most computer systems are built using a handful of such abstractions. The text describes how these abstractions are implemented, demonstrates how they are used in different systems, and prepares the reader to apply them in future designs.The book is recommended for junior and senior undergraduate students in Operating Systems, Distributed Systems, Distributed Operating Systems and/or Computer Systems Design courses; and professional computer systems designers. - Concepts of computer system design guided by fundamental principles - Cross-cutting approach that identifies abstractions common to networking, operating systems, transaction systems, distributed systems, architecture, and software engineering - Case studies that make the abstractions real: naming (DNS and the URL); file systems (the UNIX file system); clients and services (NFS); virtualization (virtual machines); scheduling (disk arms); security (TLS) - Numerous pseudocode fragments that provide concrete examples of abstract concepts - Extensive support. The authors and MIT OpenCourseWare provide on-line, free of charge, open educational resources, including additional chapters, course syllabi, board layouts and slides, lecture videos, and an archive of lecture schedules, class assignments, and design projects |
| examples of abstraction in computer science: Design Patterns Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides, 1995 Software -- Software Engineering. |
| examples of abstraction in computer science: The Cambridge Handbook of Computing Education Research Sally A. Fincher, Anthony V. Robins, 2019-02-13 This is an authoritative introduction to Computing Education research written by over 50 leading researchers from academia and the industry. |
| examples of abstraction in computer science: Computational Thinking Education Siu-Cheung Kong, Harold Abelson, 2019-07-04 This This book is open access under a CC BY 4.0 license.This book offers a comprehensive guide, covering every important aspect of computational thinking education. It provides an in-depth discussion of computational thinking, including the notion of perceiving computational thinking practices as ways of mapping models from the abstraction of data and process structures to natural phenomena. Further, it explores how computational thinking education is implemented in different regions, and how computational thinking is being integrated into subject learning in K-12 education. In closing, it discusses computational thinking from the perspective of STEM education, the use of video games to teach computational thinking, and how computational thinking is helping to transform the quality of the workforce in the textile and apparel industry. |
| examples of abstraction in computer science: Program Development in Java Barbara Liskov, John Guttag, 2000-06-06 Written by a world-renowned expert on programming methodology, and the winner of the 2008 Turing Award, this book shows how to build production-quality programs--programs that are reliable, easy to maintain, and quick to modify. Its emphasis is on modular program construction: how to get the modules right and how to organize a program as a collection of modules. The book presents a methodology effective for either an individual programmer, who may be writing a small program or a single module in a larger one; or a software engineer, who may be part of a team developing a complex program comprised of many modules. Both audiences will acquire a solid foundation for object-oriented program design and component-based software development from this methodology. Because each module in a program corresponds to an abstraction, such as a collection of documents or a routine to search the collection for documents of interest, the book first explains the kinds of abstractions most useful to programmers: procedures; iteration abstractions; and, most critically, data abstractions. Indeed, the author treats data abstraction as the central paradigm in object-oriented program design and implementation. The author also shows, with numerous examples, how to develop informal specifications that define these abstractions--specifications that describe what the modules do--and then discusses how to implement the modules so that they do what they are supposed to do with acceptable performance. Other topics discussed include: Encapsulation and the need for an implementation to provide the behavior defined by the specification Tradeoffs between simplicity and performance Techniques to help readers of code understand and reason about it, focusing on such properties as rep invariants and abstraction functions Type hierarchy and its use in defining families of related data abstractions Debugging, testing, and requirements analysis Program design as a top-down, iterative process, and design patterns The Java programming language is used for the book's examples. However, the techniques presented are language independent, and an introduction to key Java concepts is included for programmers who may not be familiar with the language. |
| examples of abstraction in computer science: Data Structures and Abstractions with Java Frank M. Carrano, 2007 For one- or two-semester courses in data structures (CS-2) in the departments of Computer Science, Computer Engineering, Business, and Management Information Systems. This is the most student-friendly data structures text available that introduces ADTs in individual, brief chapters - each with pedagogical tools to help students master each concept. Using the latest features of Java 5, this unique object-oriented presentation makes a clear distinction between specification and implementation to simplify learning, while providing maximum classroom flexibility. |
| examples of abstraction in computer science: Abstraction and Specification in Program Development B. Liskov, John Guttag, 1986 Abstraction and Specification in Program Development offers professionals in program design and software engineering a methodology that will enable them to construct programs that are reliable and reasonably easy to understand, modify, and maintain. Good programming involves the systematic mastery of complexity, and this book provides the first unified treatment of the techniques of abstraction and specification, which, the authors argue, are the linchpin of any effective approach to programming. They place particular emphasis on the use of data abstraction to produce highly modular programs.The authors focus on the process of decomposing large program projects into independent modules that can be assigned to independent working groups. They discuss methods of decomposition, the kinds of modules that are most useful in this process, and techniques to increase the likelihood that modules produced can in fact be recombined to solve the original programming problem. There are many examples of abstractions throughout the text, and each chapter ends with pertinent references and exercises.Most of the sample implementations in the book are written in CLU, one of a growing number of languages able to support data abstraction. Sufficient material is included, however, to allow the reader to work in Pascal as well.The material in this book was developed by the authors during a decade of teaching undergraduate, graduate, and professional-level courses. Barbara Liskov, the developer of CLU, is Professor and John Guttag an Associate Professor of Computer Science at MIT. Abstraction and Specification in Program Development is included in the MIT Electrical Engineering and Computer Science series. |
| examples of abstraction in computer science: Data-Oriented Programming Yehonathan Sharvit, 2022-08-16 Eliminate the unavoidable complexity of object-oriented designs. The innovative data-oriented programming paradigm makes your systems less complex by making it simpler to access and manipulate data. In Data-Oriented Programming you will learn how to: Separate code from data Represent data with generic data structures Manipulate data with general-purpose functions Manage state without mutating data Control concurrency in highly scalable systems Write data-oriented unit tests Specify the shape of your data Benefit from polymorphism without objects Debug programs without a debugger Data-Oriented Programming is a one-of-a-kind guide that introduces the data-oriented paradigm. This groundbreaking approach represents data with generic immutable data structures. It simplifies state management, eases concurrency, and does away with the common problems you’ll find in object-oriented code. The book presents powerful new ideas through conversations, code snippets, and diagrams that help you quickly grok what’s great about DOP. Best of all, the paradigm is language-agnostic—you’ll learn to write DOP code that can be implemented in JavaScript, Ruby, Python, Clojure, and also in traditional OO languages like Java or C#. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Code that combines behavior and data, as is common in object-oriented designs, can introduce almost unmanageable complexity for state management. The Data-oriented programming (DOP) paradigm simplifies state management by holding application data in immutable generic data structures and then performing calculations using non-mutating general-purpose functions. Your applications are free of state-related bugs and your code is easier to understand and maintain. About the book Data-Oriented Programming teaches you to design software using the groundbreaking data-oriented paradigm. You’ll put DOP into action to design data models for business entities and implement a library management system that manages state without data mutation. The numerous diagrams, intuitive mind maps, and a unique conversational approach all help you get your head around these exciting new ideas. Every chapter has a lightbulb moment that will change the way you think about programming. What's inside Separate code from data Represent data with generic data structures Manage state without mutating data Control concurrency in highly scalable systems Write data-oriented unit tests Specify the shape of your data About the reader For programmers who have experience with a high-level programming language like JavaScript, Java, Python, C#, Clojure, or Ruby. About the author Yehonathan Sharvit has over twenty years of experience as a software engineer. He blogs, speaks at conferences, and leads Data-Oriented Programming workshops around the world. Table of Contents PART 1 FLEXIBILITY 1 Complexity of object-oriented programming 2 Separation between code and data 3 Basic data manipulation 4 State management 5 Basic concurrency control 6 Unit tests PART 2 SCALABILITY 7 Basic data validation 8 Advanced concurrency control 9 Persistent data structures 10 Database operations 11 Web services PART 3 MAINTAINABILITY 12 Advanced data validation 13 Polymorphism 14 Advanced data manipulation 15 Debugging |
| examples of abstraction in computer science: Practical Object-Oriented Design Sandi Metz, 2018-07-10 The Complete Guide to Writing Maintainable, Manageable, Pleasing, and Powerful Object-Oriented Applications Object-oriented programming languages exist to help you create beautiful, straightforward applications that are easy to change and simple to extend. Unfortunately, the world is awash with object-oriented (OO) applications that are difficult to understand and expensive to change. Practical Object-Oriented Design, Second Edition, immerses you in an OO mindset and teaches you powerful, real-world, object-oriented design techniques with simple and practical examples. Sandi Metz demonstrates how to build new applications that can “survive success” and repair existing applications that have become impossible to change. Each technique is illustrated with extended examples in the easy-to-understand Ruby programming language, all downloadable from the companion website, poodr.com. Fully updated for Ruby 2.5, this guide shows how to Decide what belongs in a single class Avoid entangling objects that should be kept separate Define flexible interfaces among objects Reduce programming overhead costs with duck typing Successfully apply inheritance Build objects via composition Whatever your previous object-oriented experience, this concise guide will help you achieve the superior outcomes you’re looking for. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| examples of abstraction in computer science: Handbook of Model Checking Edmund M. Clarke, Thomas A. Henzinger, Helmut Veith, Roderick Bloem, 2018-05-18 Model checking is a computer-assisted method for the analysis of dynamical systems that can be modeled by state-transition systems. Drawing from research traditions in mathematical logic, programming languages, hardware design, and theoretical computer science, model checking is now widely used for the verification of hardware and software in industry. The editors and authors of this handbook are among the world's leading researchers in this domain, and the 32 contributed chapters present a thorough view of the origin, theory, and application of model checking. In particular, the editors classify the advances in this domain and the chapters of the handbook in terms of two recurrent themes that have driven much of the research agenda: the algorithmic challenge, that is, designing model-checking algorithms that scale to real-life problems; and the modeling challenge, that is, extending the formalism beyond Kripke structures and temporal logic. The book will be valuable for researchers and graduate students engaged with the development of formal methods and verification tools. |
| examples of abstraction in computer science: Program Verification Timothy T.R. Colburn, J.H. Fetzer, R.L. Rankin, 2012-12-06 Among the most important problems confronting computer science is that of developing a paradigm appropriate to the discipline. Proponents of formal methods - such as John McCarthy, C.A.R. Hoare, and Edgar Dijkstra - have advanced the position that computing is a mathematical activity and that computer science should model itself after mathematics. Opponents of formal methods - by contrast, suggest that programming is the activity which is fundamental to computer science and that there are important differences that distinguish it from mathematics, which therefore cannot provide a suitable paradigm. Disagreement over the place of formal methods in computer science has recently arisen in the form of renewed interest in the nature and capacity of program verification as a method for establishing the reliability of software systems. A paper that appeared in Communications of the ACM entitled, `Program Verification: The Very Idea', by James H. Fetzer triggered an extended debate that has been discussed in several journals and that has endured for several years, engaging the interest of computer scientists (both theoretical and applied) and of other thinkers from a wide range of backgrounds who want to understand computer science as a domain of inquiry. The editors of this collection have brought together many of the most interesting and important studies that contribute to answering questions about the nature and the limits of computer science. These include early papers advocating the mathematical paradigm by McCarthy, Naur, R. Floyd, and Hoare (in Part I), others that elaborate the paradigm by Hoare, Meyer, Naur, and Scherlis and Scott (in Part II), challenges, limits and alternatives explored by C. Floyd, Smith, Blum, and Naur (in Part III), and recent work focusing on formal verification by DeMillo, Lipton, and Perlis, Fetzer, Cohn, and Colburn (in Part IV). It provides essential resources for further study. This volume will appeal to scientists, philosophers, and laypersons who want to understand the theoretical foundations of computer science and be appropriately positioned to evaluate the scope and limits of the discipline. |
| examples of abstraction in computer science: An Episodic History of Mathematics Steven G. Krantz, 2010-04 A series of snapshots of the history of mathematics from ancient times to the twentieth century. |
| examples of abstraction in computer science: Programming Languages: Design and Implementation Terrence W. Pratt, 1975 |
| examples of abstraction in computer science: Computer Aided Verification Nicolas Halbwachs, Doron Peled, 1999-06-23 This book constitutes the refereed proceedings of the 11th International Conference on Computer Aided Verification, CAV'99, held in Trento, Italy in July 1999 as part of FLoC'99. The 34 revised full papers presented were carefully reviewed and selected from a total of 107 submissions. Also included are six invited contributions and five tool presentations. The book is organized in topical sections on processor verification, protocol verification and testing, infinite state spaces, theory of verification, linear temporal logic, modeling of systems, symbolic model checking, theorem proving, automata-theoretic methods, and abstraction. |
Examples - Apache ECharts
Apache ECharts，一款基于JavaScript的数据可视化图表库，提供直观，生动，可交互，可个性化定制的数据可视化图表。
Examples - Apache ECharts
Examples; Resources. Spread Sheet Tool; Theme Builder; Cheat Sheet; More Resources; Community. Events; Committers; Mailing List; How to Contribute; Dependencies; Code …
Examples - Apache ECharts
Examples; Resources. Spread Sheet Tool; Theme Builder; Cheat Sheet; More Resources; Community. Events; Committers; Mailing List; How to Contribute; Dependencies; Code …
Apache ECharts
ECharts: A Declarative Framework for Rapid Construction of Web-based Visualization. 如果您在科研项目、产品、学术论文、技术报告、新闻报告、教育、专利以及其他相关活动中使用了 …
Events - Apache ECharts
Examples; Resources. Spread Sheet Tool; Theme Builder; Cheat Sheet; More Resources; Community. Events; Committers; Mailing List; How to Contribute; Dependencies; Code …
Examples - Apache ECharts
Apache ECharts，一款基于JavaScript的数据可视化图表库，提供直观，生动，可交互，可个性化定制的数据可视化图表。
Examples - Apache ECharts
Examples; Resources. Spread Sheet Tool; Theme Builder; Cheat Sheet; More Resources; Community. Events; Committers; Mailing List; How to Contribute; Dependencies; Code Standard; …
Examples - Apache ECharts
Examples; Resources. Spread Sheet Tool; Theme Builder; Cheat Sheet; More Resources; Community. Events; Committers; Mailing List; How to Contribute; Dependencies; Code Standard; …
Apache ECharts
ECharts: A Declarative Framework for Rapid Construction of Web-based Visualization. 如果您在科研项目、产品、学术论文、技术报告、新闻报告、教育、专利以及其他相关活动中使用了 Apache …
Events - Apache ECharts
Examples; Resources. Spread Sheet Tool; Theme Builder; Cheat Sheet; More Resources; Community. Events; Committers; Mailing List; How to Contribute; Dependencies; Code Standard; …
