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Abstraction Computer Science Examples: From Everyday Life to Complex Systems
Author: Dr. Anya Sharma, PhD in Computer Science, Associate Professor at the University of California, Berkeley. Specializing in Software Engineering and Human-Computer Interaction.
Publisher: O'Reilly Media, a leading publisher of technology and computer science books and resources.
Editor: Dr. Ben Carter, PhD in Computer Science, experienced technical editor with over 15 years of experience in publishing computer science textbooks and articles.
Keywords: abstraction computer science examples, abstraction in programming, data abstraction, procedural abstraction, abstract data types, software design principles, computer science concepts, simplification in programming, levels of abstraction.
Abstract: This article explores the crucial concept of abstraction in computer science, providing concrete examples from various domains. We will delve into how abstraction simplifies complex systems, enhances code reusability, and promotes efficient software development. Through real-world examples, personal anecdotes, and case studies, we will demonstrate the power and practicality of abstraction computer science examples.
Understanding Abstraction Computer Science Examples
Abstraction, a fundamental concept in computer science, is the process of hiding complex implementation details and showing only essential information to the user. Think of it as a simplified representation of reality. We use abstraction every day, often without realizing it. For example, when you drive a car, you don't need to understand the intricate workings of the internal combustion engine; you simply interact with the steering wheel, pedals, and gear stick. This is abstraction in action – hiding the complexity and presenting a simplified interface.
This principle is mirrored in computer science. Abstraction allows programmers to manage the complexity of large software systems by breaking them down into smaller, more manageable modules. These modules hide internal details and expose only the necessary functionalities through well-defined interfaces. This approach greatly simplifies the development, maintenance, and understanding of complex programs.
Abstraction Computer Science Examples: Data Abstraction
One key application of abstraction is data abstraction. Consider a simple example: a bank account. As a user, you interact with your account through simple operations like deposit, withdraw, and check balance. You don't need to know the intricate details of how the bank manages your transactions – the database schema, the security protocols, or the internal accounting systems. The bank abstracts away this complexity, providing a clean and user-friendly interface.
Similarly, in programming, we use abstract data types (ADTs). For example, a stack is an ADT that defines operations like `push` (add an element) and `pop` (remove an element). The implementation details – how the stack is actually stored in memory – are hidden from the user. They only need to understand the operations provided by the ADT. This is a crucial example of abstraction computer science examples in action.
During my PhD research, I worked on a project involving a large-scale distributed database. Data abstraction played a critical role in simplifying the development and maintenance of the system. By abstracting away the complexities of data storage and retrieval across multiple servers, we were able to create a clean and consistent API for users to interact with the database.
Procedural Abstraction Computer Science Examples
Another type of abstraction is procedural abstraction, which involves creating procedures or functions that encapsulate a sequence of steps. Consider a function that calculates the factorial of a number. The user doesn't need to know the exact algorithm used to calculate the factorial; they simply call the function and provide the input. The function hides the internal implementation details, making the code more readable and easier to maintain. This exemplifies the practical applications of abstraction computer science examples in daily programming tasks.
This significantly improves code reusability. Once a procedure is created, it can be reused multiple times in different parts of the program without needing to rewrite the same code. This reduces development time and minimizes the risk of errors. In a recent project involving the development of a game engine, we heavily relied on procedural abstraction to encapsulate various game logic routines, improving code organization and making the engine more maintainable and extendable.
Case Study: Developing a Mobile Application
Let’s consider a case study of a mobile application development. Imagine we're building a social media app. We could easily get lost in the complexity of networking, data storage, user interface design, and security measures. Abstraction helps us manage this complexity. We can abstract away the details of the networking layer using libraries that handle communication protocols. The data storage layer can be abstracted using a database management system, hiding the intricacies of database interactions. The UI layer can be abstracted using frameworks that provide pre-built components. By abstracting these layers, the developers can focus on the core functionality – the social features of the app – without getting bogged down in low-level details. This is a powerful demonstration of abstraction computer science examples in a practical software engineering setting.
Levels of Abstraction
Abstraction isn't a one-size-fits-all solution. It exists at multiple levels. High-level abstractions, such as the user interface of a word processor, present a simplified view of complex underlying operations. Low-level abstractions deal with the details of hardware and memory management. The ability to choose the appropriate level of abstraction is critical for building effective and efficient systems. Understanding this nuance is crucial when discussing abstraction computer science examples.
Conclusion
Abstraction is a powerful tool in computer science, crucial for managing complexity, improving code reusability, and enhancing software development efficiency. Through data and procedural abstraction, along with the implementation of ADTs, developers can simplify their designs and create more maintainable and robust systems. The ability to appropriately abstract away details at multiple levels significantly impacts a programmer’s ability to create effective, scalable, and user-friendly software. Mastering abstraction computer science examples is an essential skill for any aspiring computer scientist or software engineer.
FAQs
1. What is the difference between data abstraction and procedural abstraction? Data abstraction focuses on hiding the internal representation of data, while procedural abstraction hides the implementation details of procedures or functions.
2. Why is abstraction important in software design? Abstraction simplifies complex systems, making them easier to understand, develop, maintain, and modify.
3. How does abstraction relate to modularity? Abstraction supports modularity by breaking down complex systems into smaller, independent modules, each with its own level of abstraction.
4. What are some common examples of abstract data types? Stacks, queues, lists, trees, and graphs are all examples of commonly used ADTs.
5. Can you give an example of abstraction in hardware? The operating system abstracts away the complexities of hardware interactions, providing a simplified interface for applications.
6. How does abstraction improve code reusability? By encapsulating implementation details, abstract modules can be reused in different parts of a program or in different projects.
7. What are the challenges associated with using abstraction? Over-abstraction can lead to overly complex or inefficient systems. Balancing the level of abstraction is crucial.
8. How does abstraction help with debugging? By isolating modules through abstraction, it becomes easier to identify and fix errors within specific parts of the system.
9. What are some real-world applications of abstraction besides software development? Traffic lights, remote controls, and even the simplified dashboards of cars are everyday examples of abstraction.
Related Articles:
1. Abstract Data Types (ADTs): A Comprehensive Guide: A detailed explanation of various ADTs, their properties, and implementation techniques.
2. Object-Oriented Programming and Abstraction: An exploration of how abstraction principles are applied in object-oriented programming.
3. Design Patterns and Abstraction: A study of how design patterns leverage abstraction to create flexible and reusable code.
4. The Role of Abstraction in Database Management Systems: An in-depth look at how abstraction simplifies database interactions.
5. Abstraction in Network Programming: An examination of abstraction layers in network communication protocols.
6. Abstraction and Software Testing: How abstraction facilitates effective testing of software components.
7. Abstraction vs. Encapsulation: A clear comparison of these two related but distinct concepts in software engineering.
8. The Importance of Abstraction in Cybersecurity: How abstraction is used to protect sensitive information and enhance system security.
9. Abstraction in Artificial Intelligence: The role of abstraction in building intelligent systems that can reason and learn from data.
| abstraction computer science examples: 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. |
| abstraction computer science examples: 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. |
| abstraction computer science examples: 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. |
| abstraction computer science examples: 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. |
| abstraction computer science examples: 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. |
| abstraction computer science examples: 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? |
| abstraction computer science examples: 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. |
| abstraction computer science examples: 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. |
| abstraction computer science examples: 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. |
| abstraction computer science examples: 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. |
| abstraction computer science examples: How to Design Programs, second edition Matthias Felleisen, Robert Bruce Findler, Matthew Flatt, Shriram Krishnamurthi, 2018-05-04 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. |
| abstraction computer science examples: Computer Science & Technology Michael L. Brodie, 1980 |
| abstraction computer science examples: 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. |
| abstraction computer science examples: Guide to Teaching Computer Science Orit Hazzan, Noa Ragonis, Tami Lapidot, 2020-08-05 This concise yet thorough textbook presents an active-learning model for the teaching of computer science. Offering both a conceptual framework and detailed implementation guidelines, the work is designed to support a Methods of Teaching Computer Science (MTCS) course, but may be applied to the teaching of any area of computer science at any level, from elementary school to university. This text is not limited to any specific curriculum or programming language, but instead suggests various options for lesson and syllabus organization. Fully updated and revised, the third edition features more than 40 new activities, bringing the total to more than 150, together with new chapters on computational thinking, data science, and soft concepts and soft skills. This edition also introduces new conceptual frameworks for teaching such as the MERge model, and new formats for the professional development of computer science educators. Topics and features: includes an extensive set of activities, to further support the pedagogical principles outlined in each chapter; discusses educational approaches to computational thinking, how to address soft concepts and skills in a MTCS course, and the pedagogy of data science (NEW); focuses on teaching methods, lab-based teaching, and research in computer science education, as well as on problem-solving strategies; examines how to recognize and address learners’ misconceptions, and the different types of questions teachers can use to vary their teaching methods; provides coverage of assessment, teaching planning, and designing a MTCS course; reviews high school teacher preparation programs, and how prospective teachers can gain experience in teaching computer science. This easy-to-follow textbook and teaching guide will prove invaluable to computer science educators within all frameworks, including university instructors and high school teachers, as well as to instructors of computer science teacher preparation programs. |
| abstraction computer science examples: 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. |
| abstraction computer science examples: 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. |
| abstraction computer science examples: 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. |
| abstraction computer science examples: 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. |
| abstraction computer science examples: Abstraction , |
| abstraction computer science examples: Computational Thinking: A Perspective on Computer Science Zhiwei Xu, Jialin Zhang, 2022-01-01 This textbook is intended as a textbook for one-semester, introductory computer science courses aimed at undergraduate students from all disciplines. Self-contained and with no prerequisites, it focuses on elementary knowledge and thinking models. The content has been tested in university classrooms for over six years, and has been used in summer schools to train university and high-school teachers on teaching introductory computer science courses using computational thinking. This book introduces computer science from a computational thinking perspective. In computer science the way of thinking is characterized by three external and eight internal features, including automatic execution, bit-accuracy and abstraction. The book is divided into chapters on logic thinking, algorithmic thinking, systems thinking, and network thinking. It also covers societal impact and responsible computing material – from ICT industry to digital economy, from the wonder of exponentiation to wonder of cyberspace, and from code of conduct to best practices for independent work. The book’s structure encourages active, hands-on learning using the pedagogic tool Bloom's taxonomy to create computational solutions to over 200 problems of varying difficulty. Students solve problems using a combination of thought experiment, programming, and written methods. Only 300 lines of code in total are required to solve most programming problems in this book. |
| abstraction computer science examples: Computer Science Illuminated Nell B. Dale, John Lewis, 2004 |
| abstraction computer science examples: 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. |
| abstraction computer science examples: 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 |
| abstraction computer science examples: Learning to Program Steven Foote, 2014-10-16 Everyone can benefit from basic programming skills–and after you start, you just might want to go a whole lot further. Author Steven Foote taught himself to program, figuring out the best ways to overcome every obstacle. Now a professional web developer, he’ll help you follow in his footsteps. He teaches concepts you can use with any modern programming language, whether you want to program computers, smartphones, tablets, or even robots. Learning to Program will help you build a solid foundation in programming that can prepare you to achieve just about any programming goal. Whether you want to become a professional software programmer, or you want to learn how to more effectively communicate with programmers, or you are just curious about how programming works, this book is a great first step in helping to get you there. Learning to Program will help you get started even if you aren’t sure where to begin. • Learn how to simplify and automate many programming tasks • Handle different types of data in your programs • Use regular expressions to find and work with patterns • Write programs that can decide what to do, and when to do it • Use functions to write clean, well-organized code • Create programs others can easily understand and improve • Test and debug software to make it reliable • Work as part of a programming team • Learn the next steps to take to build a lifetime of programming skills |
| abstraction computer science examples: Data Abstraction, Databases, and Conceptual Modelling Michael L. Brodie, 1980 |
| abstraction computer science examples: 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. |
| abstraction computer science examples: 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. |
| abstraction computer science examples: Philosophy of Computer Science William J. Rapaport, 2023-01-16 A unique resource exploring the nature of computers and computing, and their relationships to the world. Philosophy of Computer Science is a university-level textbook designed to guide readers through an array of topics at the intersection of philosophy and computer science. Accessible to students from either discipline, or complete beginners to both, the text brings readers up to speed on a conversation about these issues, so that they can read the literature for themselves, form their own reasoned opinions, and become part of the conversation by contributing their own views. Written by a highly qualified author in the field, the book looks at some of the central questions in the philosophy of computer science, including: What is philosophy? (for readers who might be unfamiliar with it) What is computer science and its relationship to science and to engineering? What are computers, computing, algorithms, and programs?(Includes a line-by-line reading of portions of Turing’s classic 1936 paper that introduced Turing Machines, as well as discussion of the Church-Turing Computability Thesis and hypercomputation challenges to it) How do computers and computation relate to the physical world? What is artificial intelligence, and should we build AIs? Should we trust decisions made by computers? A companion website contains annotated suggestions for further reading and an instructor’s manual. Philosophy of Computer Science is a must-have for philosophy students, computer scientists, and general readers who want to think philosophically about computer science. |
| abstraction computer science examples: Computer Science Illuminated Nell Dale, John Lewis, 2009-11-24 Revised and updated with the latest information in the field, the Fourth Edition of Computer Science Illuminated continues to engage and enlighten students on the fundamental concepts and diverse capabilities of computing. Written by two of today’s most respected computer science educators, Nell Dale and John Lewis, the text provides a broad overview of the many aspects of the discipline from a generic view point. Separate program language chapters are available as bundle items for those instructors who would like to explore a particular programming language with their students. The many layers of computing are thoroughly explained beginning with the information layer, working through the hardware, programming, operating systems, application, and communication layers, and ending with a discussion on the limitations of computing. Perfect for introductory computing and computer science courses, the fourth edition's thorough presentation of computing systems provides computer science majors with a solid foundation for further study, and offers non-majors a comprehensive and complete introduction to computing. |
| abstraction computer science examples: Disciplinary Literacies Evan Ortlieb, Britnie Delinger Kane, Earl H. Cheek, 2023-10-23 Educators increasingly recognize the importance of disciplinary literacy for student success, beginning as early as the primary grades. This cutting-edge volume examines ways to help K–12 students develop the literacy skills and inquiry practices needed for high-level work in different academic domains. Chapters interweave research, theory, and practical applications for teaching literature, mathematics, science, and social studies, as well as subjects outside the standard core--physical education, visual and performing arts, and computer science. Essential topics include use of multimodal and digital texts, culturally responsive and sustaining pedagogy, and new directions for teacher professional development. The book features vivid classroom examples and samples of student work. |
| abstraction computer science examples: Once Upon an Algorithm Martin Erwig, 2022-08-09 This easy-to-follow introduction to computer science reveals how familiar stories like Hansel and Gretel, Sherlock Holmes, and Harry Potter illustrate the concepts and everyday relevance of computing. Picture a computer scientist, staring at a screen and clicking away frantically on a keyboard, hacking into a system, or perhaps developing an app. Now delete that picture. In Once Upon an Algorithm, Martin Erwig explains computation as something that takes place beyond electronic computers, and computer science as the study of systematic problem solving. Erwig points out that many daily activities involve problem solving. Getting up in the morning, for example: You get up, take a shower, get dressed, eat breakfast. This simple daily routine solves a recurring problem through a series of well-defined steps. In computer science, such a routine is called an algorithm. Erwig illustrates a series of concepts in computing with examples from daily life and familiar stories. Hansel and Gretel, for example, execute an algorithm to get home from the forest. The movie Groundhog Day illustrates the problem of unsolvability; Sherlock Holmes manipulates data structures when solving a crime; the magic in Harry Potter’s world is understood through types and abstraction; and Indiana Jones demonstrates the complexity of searching. Along the way, Erwig also discusses representations and different ways to organize data; “intractable” problems; language, syntax, and ambiguity; control structures, loops, and the halting problem; different forms of recursion; and rules for finding errors in algorithms. This engaging book explains computation accessibly and shows its relevance to daily life. Something to think about next time we execute the algorithm of getting up in the morning. |
| abstraction computer science examples: Constructive Methods in Computing Science Manfred Broy, 2012-12-06 Computing Science is a science of constructive methods. The solution of a problem has to be described formally by constructive techniques, if it is to be evaluated on a computer. The Marktoberdorf Advanced Study Institute 1988 presented a comprehensive survey of the recent research in constructive methods in Computing Science. Some approaches to a methodological framework and to supporting tools for specification, development and verification of software systems were discussed in detail. Other lectures dealt with the relevance of the foundations of logic for questions of program construction and with new programming paradigms and formalisms which have proven to be useful for a constructive approach to software development. The construction, specification, design and verification especially of distributed and communicating systems was discussed in a number of complementary lectures. Examples for those approaches were given on several levels such as semaphores, nondeterministic state transition systems with fairness assumptions, decomposition of specifications for concurrent systems in liveness and safety properties and functional specifications of distributed systems. Construction methods in programming that were presented range from type theory, the theory of evidence, theorem provers for proving properties of functional programs to category theory as an abstract and general concept for the description of programming paradigms. |
| abstraction computer science examples: 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. |
| abstraction computer science examples: Putting Information First Patrick Allo, 2011-05-03 Putting Information First focuses on Luciano Floridi’s contributions to the philosophy of information. Respected scholars stimulate the debate on the most distinctive and controversial views he defended, and present the philosophy of information as a specific way of doing philosophy. Contains eight essays by leading scholars, a reply by Luciano Floridi, and an epilogue by Terrell W. Bynum Explains the importance of philosophy of information as a specific way of doing philosophy Focuses directly on the work of Luciano Floridi in the area of philosophy of information, but also connects to contemporary concerns in philosophy more generally Illustrates several debates that arise from core themes in the philosophy of information |
| abstraction computer science examples: Starting Category Theory Paolo Perrone, 2024-04-08 One of the central highlights of this work is the exploration of the Yoneda lemma and its profound implications, during which intuitive explanations are provided, as well as detailed proofs, and specific examples. This book covers aspects of category theory often considered advanced in a clear and intuitive way, with rigorous mathematical proofs. It investigates universal properties, coherence, the relationship between categories and graphs, and treats monads and comonads on an equal footing, providing theorems, interpretations and concrete examples. Finally, this text contains an introduction to monoidal categories and to strong and commutative monads, which are essential tools in current research but seldom found in other textbooks.Starting Category Theory serves as an accessible and comprehensive introduction to the fundamental concepts of category theory. Originally crafted as lecture notes for an undergraduate course, it has been developed to be equally well-suited for individuals pursuing self-study. Most crucially, it deliberately caters to those who are new to category theory, not requiring readers to have a background in pure mathematics, but only a basic understanding of linear algebra. |
| abstraction computer science examples: 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) |
| abstraction computer science examples: Advances in Computer Science - ASIAN 2004, Higher Level Decision Making Michael J. Maher, 2004-12 This book constitutes the refereed proceedings of the 9th Asian Computing Science Conference, ASIAN 2004, dedicated to Jean-Louis Lassez on the occasion of his 60th birthday and held in Chiang Mai, Thailand in December 2004. The 17 revised full papers presented together with 3 keynote papers and 16 invited papers honouring Jean-Louis Lassez were carefully reviewed and selected from 75 submissions. The contributed papers are focusing on higher-level decision making, whereas the invited papers address a broader variety of topics in theoretical computer science. |
| abstraction computer science examples: Advances in Computer Science - ASIAN 2004, Higher Level Decision Making Michael J. Maher, 2004-12-02 |
| abstraction computer science examples: Computer Science Handbook Allen B. Tucker, 2004-06-28 When you think about how far and fast computer science has progressed in recent years, it's not hard to conclude that a seven-year old handbook may fall a little short of the kind of reference today's computer scientists, software engineers, and IT professionals need. With a broadened scope, more emphasis on applied computing, and more than 70 chap |
| abstraction computer science examples: AP Computer Science Principles Seth Reichelson, 2020-07-07 Always study with the most up-to-date prep! Look for AP Computer Science Principles Premium with 6 Practice Tests, ISBN 9781506280400, on sale February 02, 2021. Publisher's Note: Products purchased from third-party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitles included with the product. |
Abstraction - Wikipedia
Abstraction is a process where general rules and concepts are derived from the use and classifying of specific examples, literal (real or concrete) signifiers, first principles, or other …
ABSTRACTION Definition & Meaning - Merriam-Webster
The meaning of ABSTRACTION is the act or process of abstracting : the state of being abstracted. How to use abstraction in a sentence. Did you know?
ABSTRACTION | English meaning - Cambridge Dictionary
ABSTRACTION definition: 1. the quality of existing as or representing an idea, a feeling, etc. and not a material object…. Learn more.
Abstraction Definition & Meaning | Britannica Dictionary
ABSTRACTION meaning: 1 : the act of obtaining or removing something from a source the act of abstracting something; 2 : a general idea or quality rather than an actual person, object, or …
Abstraction - Definition and examples — Conceptually
Abstraction is the process of generalising complex events in the real world to the abstract ideas that underly them, tucking away the complexities of the situation.
What is abstraction? - Abstraction - KS3 Computer Science …
Abstraction is the process of filtering out – ignoring - the characteristics of patterns that we don't need in order to concentrate on those that we do.
What is Abstraction, and Why Is It So Important to Understand?
Jan 7, 2025 · Abstraction is the process of identifying and sharpening perspective on qualities or properties from specific, so-termed ‘objects’ or experiences in which they appear.
Abstraction in Programming: A Beginner’s Guide - Stackify
May 1, 2023 · Abstraction is one of the key concepts of object-oriented programming (OOP) languages. Its main goal is to handle complexity by hiding unnecessary details from the user. …
Abstraction - New World Encyclopedia
In philosophical terminology, abstraction is the thought process wherein ideas are distanced from objects. Abstraction uses a strategy of simplification which ignores formerly concrete details or …
ABSTRACTION definition and meaning | Collins English …
An abstraction is a general idea rather than one relating to a particular object, person, or situation.
Abstraction - Wikipedia
Abstraction is a process where general rules and concepts are derived from the use and classifying of specific examples, literal (real or concrete) signifiers, first principles, or other …
ABSTRACTION Definition & Meaning - Merriam-Webster
The meaning of ABSTRACTION is the act or process of abstracting : the state of being abstracted. How to use abstraction in a sentence. Did you know?
ABSTRACTION | English meaning - Cambridge Dictionary
ABSTRACTION definition: 1. the quality of existing as or representing an idea, a feeling, etc. and not a material object…. Learn more.
Abstraction Definition & Meaning | Britannica Dictionary
ABSTRACTION meaning: 1 : the act of obtaining or removing something from a source the act of abstracting something; 2 : a general idea or quality rather than an actual person, object, or …
Abstraction - Definition and examples — Conceptually
Abstraction is the process of generalising complex events in the real world to the abstract ideas that underly them, tucking away the complexities of the situation.
What is abstraction? - Abstraction - KS3 Computer Science …
Abstraction is the process of filtering out – ignoring - the characteristics of patterns that we don't need in order to concentrate on those that we do.
What is Abstraction, and Why Is It So Important to Understand?
Jan 7, 2025 · Abstraction is the process of identifying and sharpening perspective on qualities or properties from specific, so-termed ‘objects’ or experiences in which they appear.
Abstraction in Programming: A Beginner’s Guide - Stackify
May 1, 2023 · Abstraction is one of the key concepts of object-oriented programming (OOP) languages. Its main goal is to handle complexity by hiding unnecessary details from the user. …
Abstraction - New World Encyclopedia
In philosophical terminology, abstraction is the thought process wherein ideas are distanced from objects. Abstraction uses a strategy of simplification which ignores formerly concrete details or …
ABSTRACTION definition and meaning | Collins English …
An abstraction is a general idea rather than one relating to a particular object, person, or situation.
