This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. (June 2011)

In object-oriented programming, a method is a subroutine (or procedure or function) associated with a class. Methods define the behavior to be exhibited by instances of the associated class at program run time. Methods have the special property that at runtime, they have access to data stored in an instance of the class (or class instance or class object or object) they are associated with and are thereby able to control the state of the instance. ^[1] The association between class and method is called binding. A method associated with a class is said to be bound to the class. Methods can be bound to a class at compile time (static binding) or to an object at runtime (dynamic binding).^[2]

Abstract methods[link]

An abstract method is one with only a signature and no implementation body. It is often used to specify that a subclass must provide an implementation of the method. Abstract methods are used to specify interfaces in some computer languages.^[3]

Accessor and mutator methods[link]

An 'accessor' method is a method that is usually small, simple and provides the sole means for the state of an object to be accessed (retrieved) from other parts of a program.

Although this introduces a new dependency, as stated above, use of methods is preferred, in the object-oriented paradigm, to directly accessing state data - because those methods provide an abstraction layer. For example, if a bank-account class provides a getBalance() accessor method to retrieve the current balance (rather than directly accessing the balance data fields), then later revisions of the same code can implement a more complex mechanism for balance retrieval (say, a database fetch), without the dependent code needing to be changed (However, this often claimed advantage is not unique to object oriented programming, and was earlier implemented - when desirable in critical systems - through conventional modular programming with optional run-time, system-wide locking mechanisms, in the imperative/procedural paradigms)

To compare the value of two data items, two accessor-method calls are normally required before a comparison can take place between the retrieved primitive data type values. Comparator methods are required to compare entire objects for equality. This contrasts with a direct comparison in non-OOP paradigms.

An update, modifier, or mutator method, is an accessor method that changes the state of an object. Objects that provide such methods are considered mutable objects.

Class methods[link]

Class methods are methods that are called on a class (compare this to class instance methods, or object methods). Its meaning may vary depending on the programming language:

• In some languages (e.g. C++, Java), class methods are synonymous with static methods (see section below), which are called with a known class name at compile-time. this cannot be used in static methods.
• In some other languages (e.g. Smalltalk, Ruby, Objective-C), class methods are methods that are called on a class object, which can be computed at runtime, there being no difference between calling a method on a regular object or a class object; thus both instance and class methods are resolved dynamically, and there are no "static" methods. Notably, in these class methods, this refers to the class object.
• Some languages have both. For example, in Python, one can create class methods and static methods using the classmethod and staticmethod decorators, respectively. The former has access to this (i.e. the instance object, conventionally known as self), while the latter does not.

Conversion operator methods[link]

A conversion operator provides a means for the compiler to implicitly (performed by the compiler automatically when appropriate) provide an object of a type other than the type of the class object.

Operator methods[link]

Operator methods define or redefine operator symbols and define the operations to be performed with the symbol and the associated method parameters.

Overloaded methods[link]

Overloaded methods are those with the same name but different formal parameters or return value type, if the language supports overloading on return type.^[4]

Overridden methods[link]

Overridden methods are those that are redefined in a subclass and hide methods of a superclass.^[5]

Special methods[link]

Special methods are very language-specific and a language may support none, some, or all of the special methods defined here. A language's compiler may automatically generate default special methods or a programmer may be allowed to optionally define special methods. Most special methods cannot be directly called, but rather the compiler generates code to call them at appropriate times. The syntax for definition and calling (i.e., when a special method can be called) of special methods varies amongst programming languages.

Constructors[link]

A constructor is a class method that is called automatically at the beginning of an object's lifetime to initialize the object, a process called construction (or instantiation). Initialization may include acquisition of resources. A language may provide a means to control whether a constructor can be called implicitly (by the compiler) or only explicitly (by the programmer). Constructors may have parameters but usually do not return values in most languages.

Destructors[link]

A destructor is a class method that is called automatically at the end of an object's lifetime, a process called destruction. Destructors in most languages do not allow destructor method arguments nor return values. (In some languages (ref required), a destructor can return a value which can then be used to obtain a public representation (transfer encoding) of an instance of a class and simultaneously destroy the copy of the instance stored in current thread's memory). Destructors can be implemented such as to perform clean up chores and other tasks at object destruction.

Copy-assignment operators[link]

Copy-assignment operators define actions to be performed by the compiler when a class object is assigned to a class object of the same type.

Static methods[link]

Static methods neither require an instance of the class nor can they implicitly access the data (or this, self, Me, etc.) of such an instance. A static method is distinguished in some programming languages with the static keyword placed somewhere in the method's signature. Static methods are called "static" because they are resolved statically (i.e. at compile time), in statically typed languages, based on the class they are called on; and not dynamically, as in the case with instance methods which are resolved polymorphically based on the runtime type of the object. Therefore, static methods cannot be overridden.

Virtual methods[link]

Virtual methods are the means by which a class object can achieve polymorphic behavior. Non-virtual methods, or regular methods, are those which do not participate in polymorphism. ^[6]

Learning Methods[link]

There are many programs available on the internet to help with the learning of methods such as Jeroo which is a free educational tool aimed to help students learn computer programming.

References[link]

See also[link]

• Subroutine

Look up method in Wiktionary, the free dictionary.

Method may refer to:

• Scientific method, a series of steps taken to acquire knowledge
• Method (computer programming), a piece of code associated with a class or object to perform a task
• Method (music), a kind of textbook to help students learning to play a musical instrument
• Method (patent), a series of steps or acts for performing a function
• Methodology, the collection, comparative study, and critique of the individual methods that are used in a given discipline or field of inquiry
• Method acting, a style of acting in which the actor attempts to replicate the conditions under which the character operates
• Method (Godhead), the bassist and programmer for the industrial band Godhead
• Discourse on Method, a philosophical and mathematical treatise by René Descartes
• Method (film), a 2004 film directed by Duncan Roy
• Method Products (branded as "method"), a San Francisco-based corporation which manufactures household products
• Method Studios, a Los Angeles-based visual effects company
• Method Incorporated, an international brand experience agency
• Method ringing, a British style of ringing church bells according to a series of mathematical algorithms
• Method Man, an American rapper.

See also[link]

The Method (disambiguation)

This disambiguation page lists articles associated with the same title. If an internal link led you here, you may wish to change the link to point directly to the intended article.

Computer science or computing science (abbreviated CS or CompSci) designates the scientific and mathematical approach in information technology and its applications, notably computer software and sometimes hardware.^[1][2] A computer scientist is a scientist who specialises in the theory of computation and the design of computers. ^[3]

Its subfields can be divided into practical techniques for its implementation and application in computer systems and purely theoretical areas. Some, such as computational complexity theory, which studies fundamental properties of computational problems, are highly abstract, while others, such as computer graphics, emphasize real-world applications. Still others focus on the challenges in implementing computations. For example, programming language theory studies approaches to description of computations, while the study of computer programming itself investigates various aspects of the use of programming languages and complex systems, and human-computer interaction focuses on the challenges in making computers and computations useful, usable, and universally accessible to humans.

Computer science deals with the theoretical foundations of information and computation, together with practical techniques for the implementation and application of these foundations

History[link]

The earliest foundations of what would become computer science predate the invention of the modern digital computer. Machines for calculating fixed numerical tasks such as the abacus have existed since antiquity. Wilhelm Schickard designed the first mechanical calculator in 1623, but did not complete its construction.^[4] Blaise Pascal designed and constructed the first working mechanical calculator, the Pascaline, in 1642. Charles Babbage designed a difference engine and then a general-purpose Analytical Engine in Victorian times,^[5] for which Ada Lovelace wrote a manual. Because of this work she is regarded today as the world's first programmer.^[6] Around 1900, punched card machines were introduced.

During the 1940s, as new and more powerful computing machines were developed, the term computer came to refer to the machines rather than their human predecessors.^[7] As it became clear that computers could be used for more than just mathematical calculations, the field of computer science broadened to study computation in general. Computer science began to be established as a distinct academic discipline in the 1950s and early 1960s.^[8][9] The world's first computer science degree program, the Cambridge Diploma in Computer Science, began at the University of Cambridge Computer Laboratory in 1953. The first computer science degree program in the United States was formed at Purdue University in 1962.^[10] Since practical computers became available, many applications of computing have become distinct areas of study in their own right.

Although many initially believed it was impossible that computers themselves could actually be a scientific field of study, in the late fifties it gradually became accepted among the greater academic population.^[11] It is the now well-known IBM brand that formed part of the computer science revolution during this time. IBM (short for International Business Machines) released the IBM 704 and later the IBM 709 computers, which were widely used during the exploration period of such devices. "Still, working with the IBM [computer] was frustrating...if you had misplaced as much as one letter in one instruction, the program would crash, and you would have to start the whole process over again".^[11] During the late 1950s, the computer science discipline was very much in its developmental stages, and such issues were commonplace.

Time has seen significant improvements in the usability and effectiveness of computer science technology. Modern society has seen a significant shift from computers being used solely by experts or professionals to a more widespread user base. Initially, computers were quite costly, and for their most-effective use, some degree of human aid was needed, in part by professional computer operators. However, as computers became widespread and far more affordable, less human assistance was needed, although residues of the original assistance still remained.

Major achievements[link]

The German military used the Enigma machine (shown here) during World War II for communication they thought to be secret. The large-scale decryption of Enigma traffic at Bletchley Park was an important factor that contributed to Allied victory in WWII.^[12]

Despite its short history as a formal academic discipline, computer science has made a number of fundamental contributions to science and society. These include:

• The start of the "digital revolution," which includes the current Information Age and the Internet.^[13]
• A formal definition of computation and computability, and proof that there are computationally unsolvable and intractable problems.^[14]
• The concept of a programming language, a tool for the precise expression of methodological information at various levels of abstraction.^[15]
• In cryptography, breaking the Enigma machine was an important factor contributing to the Allied victory in World War II.^[12]
• Scientific computing enabled practical evaluation of processes and situations of great complexity, as well as experimentation entirely by software. It also enabled advanced study of the mind, and mapping of the human genome became possible with the Human Genome Project.^[13] Distributed computing projects such as Folding@home explore protein folding.
• Algorithmic trading has increased the efficiency and liquidity of financial markets by using artificial intelligence, machine learning, and other statistical and numerical techniques on a large scale.^[16] High frequency algorithmic trading can also exacerbate volatility.^[17]
• Image synthesis, including video by computing individual video frames.^{[citation needed]}
• Simulation of various processes, including computational fluid dynamics, physical, electrical, and electronic systems and circuits, as well as societies and social situations (notably war games) along with their habitats, among many others. Modern computers enable optimization of such designs as complete aircraft. Notable in electrical and electronic circuit design are SPICE, as well as software for physical realization of new (or modified) designs. The latter includes essential design software for integrated circuits.^{[citation needed]}

Philosophy[link]

A number of computer scientists have argued for the distinction of three separate paradigms in computer science. Peter Wegner argued that those paradigms are science, technology, and mathematics.^[18] Peter Denning's working group argued that they are theory, abstraction (modeling), and design.^[19] Amnon H. Eden described them as the "rationalist paradigm" (which treats computer science as branch of mathematics, which is prevalent in theoretical computer science, and mainly employs deductive reasoning), the "technocratic paradigm" (which might be found in engineering approaches, most prominently in software engineering), and the "scientific paradigm" (which approaches computer-related artifacts from the empirical perspective of natural sciences, identifiable in some branches of artificial intelligence).^[20]

Name of the field[link]

The term "computer science" was first coined by the numerical analyst George Forsythe in 1961.^[21] Despite its name, a significant amount of computer science does not involve the study of computers themselves. Because of this, several alternative names have been proposed. Certain departments of major universities prefer the term computing science, to emphasize precisely that difference. Danish scientist Peter Naur suggested the term datalogy, to reflect the fact that the scientific discipline revolves around data and data treatment, while not necessarily involving computers. The first scientific institution to use the term was the Department of Datalogy at the University of Copenhagen, founded in 1969, with Peter Naur being the first professor in datalogy. The term is used mainly in the Scandinavian countries. Also, in the early days of computing, a number of terms for the practitioners of the field of computing were suggested in the Communications of the ACM – turingineer, turologist, flow-charts-man, applied meta-mathematician, and applied epistemologist.^[22] Three months later in the same journal, comptologist was suggested, followed next year by hypologist.^[23] The term computics has also been suggested.^[24] In Europe, terms derived from contracted translations of the espression "automatic information" (e.g. "informazione automatica" in Italian) are often used, e.g. informatique (French), Informatik (German), informatica (Italy), informática (Spain, Portugal) or informatika (Slavic languages) are also used and have also been adopted in the UK (as in the School of Informatics of the University of Edinburgh).^[25]

Renowned computer scientist Edsger Dijkstra once stated: "Computer science is no more about computers than astronomy is about telescopes."^[26] The design and deployment of computers and computer systems is generally considered the province of disciplines other than computer science. For example, the study of computer hardware is usually considered part of computer engineering, while the study of commercial computer systems and their deployment is often called information technology or information systems. However, there has been much cross-fertilization of ideas between the various computer-related disciplines. Computer science research also often intersects other disciplines, such as philosophy, cognitive science, linguistics, mathematics, physics, statistics, and logic.

Computer science is considered by some to have a much closer relationship with mathematics than many scientific disciplines, with some observers saying that computing is a mathematical science.^[8] Early computer science was strongly influenced by the work of mathematicians such as Kurt Gödel and Alan Turing, and there continues to be a useful interchange of ideas between the two fields in areas such as mathematical logic, category theory, domain theory, and algebra.

The relationship between computer science and software engineering is a contentious issue, which is further muddied by disputes over what the term "software engineering" means, and how computer science is defined. David Parnas, taking a cue from the relationship between other engineering and science disciplines, has claimed that the principal focus of computer science is studying the properties of computation in general, while the principal focus of software engineering is the design of specific computations to achieve practical goals, making the two separate but complementary disciplines.^[27]

The academic, political, and funding aspects of computer science tend to depend on whether a department formed with a mathematical emphasis or with an engineering emphasis. Computer science departments with a mathematics emphasis and with a numerical orientation consider alignment with computational science. Both types of departments tend to make efforts to bridge the field educationally if not across all research.

Areas of computer science[link]

As a discipline, computer science spans a range of topics from theoretical studies of algorithms and the limits of computation to the practical issues of implementing computing systems in hardware and software.^[28][29] CSAB, formerly called Computing Sciences Accreditation Board – which is made up of representatives of the Association for Computing Machinery (ACM), and the IEEE Computer Society (IEEE-CS)^[30] – identifies four areas that it considers crucial to the discipline of computer science: theory of computation, algorithms and data structures, programming methodology and languages, and computer elements and architecture. In addition to these four areas, CSAB also identifies fields such as software engineering, artificial intelligence, computer networking and communication, database systems, parallel computation, distributed computation, computer-human interaction, computer graphics, operating systems, and numerical and symbolic computation as being important areas of computer science.^[28]

Theoretical computer science[link]

The broader field of theoretical computer science encompasses both the classical theory of computation and a wide range of other topics that focus on the more abstract, logical, and mathematical aspects of computing.

Theory of computation[link]

According to Peter J. Denning, the fundamental question underlying computer science is, "What can be (efficiently) automated?"^[8] The study of the theory of computation is focused on answering fundamental questions about what can be computed and what amount of resources are required to perform those computations. In an effort to answer the first question, computability theory examines which computational problems are solvable on various theoretical models of computation. The second question is addressed by computational complexity theory, which studies the time and space costs associated with different approaches to solving a multitude of computational problems.

The famous "P=NP?" problem, one of the Millennium Prize Problems,^[31] is an open problem in the theory of computation.

		P = NP ?	GNITIRW-TERCES
Automata theory	Computability theory	Computational complexity theory	Cryptography	Quantum computing theory

Information and coding theory[link]

Information theory is related to the quantification of information.This was developed by Claude E. Shannon to find fundamental limits on signal processing operations such as compressing data and on reliably storing and communicating data. Coding theory is the study of the properties of codes (systems for converting information from one form to another) and their fitness for a specific application. Codes are used for data compression, cryptography, error detection and correction, and more recently also for network coding. Codes are studied for the purpose of designing efficient and reliable data transmission methods.

Algorithms and data structures[link]

Failed to parse (Missing texvc executable; please see math/README to configure.): O(n^2)
Analysis of algorithms	Algorithms	Data structures	Computational geometry

Programming language theory[link]

Programming language theory (PLT) is a branch of computer science that deals with the design, implementation, analysis, characterization, and classification of programming languages and their individual features. It falls within the discipline of computer science, both depending on and affecting mathematics, software engineering and linguistics. It is an active research area, with numerous dedicated academic journals.

Failed to parse (Missing texvc executable; please see math/README to configure.): \Gamma\vdash x: \text{Int}
Type theory	Compiler design	Programming languages

Formal methods[link]

Formal methods are a particular kind of mathematically based technique for the specification, development and verification of software and hardware systems. The use of formal methods for software and hardware design is motivated by the expectation that, as in other engineering disciplines, performing appropriate mathematical analysis can contribute to the reliability and robustness of a design. However, the high cost of using formal methods means that they are usually only used in the development of high-integrity and life-critical systems, where safety or security is of utmost importance. Formal methods are best described as the application of a fairly broad variety of theoretical computer science fundamentals, in particular logic calculi, formal languages, automata theory, and program semantics, but also type systems and algebraic data types to problems in software and hardware specification and verification.

Concurrent, parallel and distributed systems[link]

Concurrency is a property of systems in which several computations are executing simultaneously, and potentially interacting with each other. A number of mathematical models have been developed for general concurrent computation including Petri nets, process calculi and the Parallel Random Access Machine model. A distributed system extends the idea of concurrency onto multiple computers connected through a network. Computers within the same distributed system have their own private memory, and information is often exchanged amongst themselves to achieve a common goal.

Databases and information retrieval[link]

A database is intended to organize, store, and retrieve large amounts of data easily. Digital databases are managed using database management systems to store, create, maintain, and search data, through database models and query languages.

Applied computer science[link]

Artificial intelligence[link]

This branch of computer science aims to synthesise goal-orientated processes such as problem-solving, decision-making, environmental adaptation, learning and communication which are found in humans and animals. From its origins in cybernetics and in the Dartmouth Conference (1956), artificial intelligence (AI) research has been necessarily cross-disciplinary, drawing on areas of expertise such as applied mathematics, symbolic logic, semiotics, electrical engineering, philosophy of mind, neurophysiology, and social intelligence. AI is associated in the popular mind with robotic development, but the main field of practical application has been as an embedded component in areas of software development which require computational understanding and modeling such as finance and economics, data mining and the physical sciences. The starting-point in the late 1940s was Alan Turing's question "Can computers think?", and the question remains effectively unanswered although the "Turing Test" is still used to assess computer output on the scale of human intelligence. But the automation of evaluative and predictive tasks has been increasingly successful as a substitute for human monitoring and intervention in domains of computer application involving complex real-world data.

	96px	96px
Machine learning	Computer vision	Image processing	Pattern recognition
Cognitive science	Data mining	Evolutionary computation	Information retrieval
Knowledge representation	Natural language processing	Robotics

Computer architecture and engineering[link]

Computer architecture, or digital computer organization, is the conceptual design and fundamental operational structure of a computer system. It focuses largely on the way by which the central processing unit performs internally and accesses addresses in memory. The field often involves disciplines of computer engineering and electrical engineering, selecting and interconnection hardware components to create computers that meet functional, performance, and cost goals.

Digital logic	Microarchitecture	Multiprocessing
Operating systems	Computer networks	Databases	Computer security
Ubiquitous computing	Systems architecture	Compiler design	Programming languages

Computer graphics and visualization[link]

Computer graphics is the study of digital visual contents, and involves syntheses and manipulations of image data. The study is connected to many other fields in computer science, including computer vision, image processing, and computational geometry, and are heavily applied in the fields of special effects and video games.

Computer security and cryptography[link]

Computer security is a branch of computer technology, whose objective includes protection of information from unauthorized access, disruption, or modification while maintaining the accessibility and usability of the system for its intended users. Cryptography is the practice and study of hiding (encryption) and therefore deciphering (decryption) information. Modern cryptography is largely related to computer science, for many encryption and decryption algorithms are based on their computational complexity.

Computational science[link]

Computational science (or scientific computing) is the field of study concerned with constructing mathematical models and quantitative analysis techniques and using computers to analyze and solve scientific problems. In practical use, it is typically the application of computer simulation and other forms of computation to problems in various scientific disciplines.

Numerical analysis	Computational physics	Computational chemistry	Bioinformatics

Health Informatics[link]

Health Informatics in computer science is referred to as Computational health informatics and deals with computational techniques for solving problems in health care. It is a sub-branch of both computer science and health informatics.

Information science[link]

Information retrieval	Knowledge representation	Natural language processing	Human–computer interaction

Software engineering[link]

Software engineering is the study of designing, implementing, and modifying software in order to ensure it is of high quality, affordable, maintainable, and fast to build. It is a systematic approach to software design, involving the application of engineering practices to software.

Software engineering deals with the organizing and analyzing software to get the best out of them. It doesn't just deal with the creation or manufacture of new software, but its internal maintenance and arrangement.

Academia[link]

Conferences[link]

This section requires expansion.

Journals[link]

This section requires expansion.

Education[link]

Some universities teach computer science as a theoretical study of computation and algorithmic reasoning. These programs often feature the theory of computation, analysis of algorithms, formal methods, concurrency theory, databases, computer graphics, and systems analysis, among others. They typically also teach computer programming, but treat it as a vessel for the support of other fields of computer science rather than a central focus of high-level study.

Other colleges and universities, as well as secondary schools and vocational programs that teach computer science, emphasize the practice of advanced programming rather than the theory of algorithms and computation in their computer science curricula. Such curricula tend to focus on those skills that are important to workers entering the software industry. The process aspects of computer programming are often referred to as software engineering.

While computer science professions increasingly drive the U.S. economy, computer science education is absent in most American K-12 curricula. A report entitled "Running on Empty: The Failure to Teach K-12 Computer Science in the Digital Age" was released in October 2010 by Association for Computing Machinery (ACM) and Computer Science Teachers Association (CSTA), and revealed that only 14 states have adopted significant education standards for high school computer science. The report also found that only nine states count high school computer science courses as a core academic subject in their graduation requirements. In tandem with "Running on Empty", a new non-partisan advocacy coalition - Computing in the Core (CinC) - was founded to influence federal and state policy, such as the Computer Science Education Act, which calls for grants to states to develop plans for improving computer science education and supporting computer science teachers.

Within the United States a gender gap in computer science education has been observed as well. Research conducted by the WGBH Educational Foundation and the Association for Computing Machinery (ACM) revealed that more than twice as many high school boys considered computer science to be a “very good” or “good” college major than high school girls.^[32] In addition, the high school Advanced Placement (AP) exam for computer science has displayed a disparity in gender. Compared to other AP subjects it has the lowest number of female participants, with a composition of about 15 percent women.^[33] This gender gap in computer science is further witnessed at the college level, where 31 percent of undergraduate computer science degrees are earned by women and only 8 percent of computer science faculty consists of women.^[34]

See also[link]

Computer science portal

	Book: Computer science
Wikipedia books are collections of articles that can be downloaded or ordered in print.

References[link]

Further reading[link]

External links[link]

Find more about Computer science on Wikipedia's sister projects:
	Definitions and translations from Wiktionary
	Images and media from Commons
	Learning resources from Wikiversity
	News stories from Wikinews
	Quotations from Wikiquote
	Source texts from Wikisource
	Textbooks from Wikibooks

• Computer science at the Open Directory Project
• Scholarly Societies in Computer Science
• Best Papers Awards in Computer Science since 1996
• Photographs of computer scientists by Bertrand Meyer
• EECS.berkeley.edu

Bibliography and academic search engines

• CiteSeer^x (article): search engine, digital library and repository for scientific and academic papers with a focus on computer and information science.
• DBLP Computer Science Bibliography (article): computer science bibliography website hosted at Universität Trier, in Germany.
• The Collection of Computer Science Bibliographies (article)

Professional organizations

• Association for Computing Machinery
• IEEE Computer Society
• Informatics Europe

Misc

• Computer Science - Stack Exchange a community run Question and Answer site for Computer Science

Computer

A computer is a general purpose device which can be programmed to carry out a finite set of arithmetic or logical operations. Since a sequence of operations can be readily changed, the computer can solve more than one kind of problem. The essential point of a computer is to implement an idea, the terms of which are satisfied by Alan Turing's Universal Turing machine.

Conventionally, a computer consists of at least one processing element and some form of memory. The processing element carries out arithmetic and logic operations, and a sequencing and control unit that can change the order of operations based on stored information. Peripheral devices allow information to be retrieved from an external source, and the result of operations saved.

A computer's processing unit executes a series of instructions that make it read, manipulate and then store data. Conditional instructions change the sequence of instructions as a function of the current state of the machine or its environment.

In order to interact with such a machine, programmers and engineers developed the concept of a user interface in order to accept input from humans and return results for human consumption.

The first electronic digital computers were developed between 1940 and 1945 in the United Kingdom and United States. Originally, they were the size of a large room, consuming as much power as several hundred modern personal computers (PCs).^[1] In this era mechanical analog computers were used for military applications.

Modern computers based on integrated circuits are millions to billions of times more capable than the early machines, and occupy a fraction of the space.^[2] Simple computers are small enough to fit into mobile devices, and mobile computers can be powered by small batteries. Personal computers in their various forms are icons of the Information Age and are what most people think of as "computers". However, the embedded computers found in many devices from mp3 players to fighter aircraft and from toys to industrial robots are the most numerous.

History of computing[link]

The Jacquard loom, on display at the Museum of Science and Industry in Manchester, England, was one of the first programmable devices.

The first use of the word "computer" was recorded in 1613, referring to a person who carried out calculations, or computations, and the word continued with the same meaning until the middle of the 20th century. From the end of the 19th century the word began to take on its more familiar meaning, a machine that carries out computations.^[3]

Limited-function early computers[link]

The history of the modern computer begins with two separate technologies, automated calculation and programmability, but no single device can be identified as the earliest computer, partly because of the inconsistent application of that term. A few devices are worth mentioning though, like some mechanical aids to computing, which were very successful and survived for centuries until the advent of the electronic calculator, like the Sumerian abacus, designed around 2500 BC^[4] of which a descendant won a speed competition against a modern desk calculating machine in Japan in 1946,^[5] the slide rules, invented in the 1620s, which were carried on five Apollo space missions, including to the moon^[6] and arguably the astrolabe and the Antikythera mechanism, an ancient astronomical computer built by the Greeks around 80 BC.^[7] The Greek mathematician Hero of Alexandria (c. 10–70 AD) built a mechanical theater which performed a play lasting 10 minutes and was operated by a complex system of ropes and drums that might be considered to be a means of deciding which parts of the mechanism performed which actions and when.^[8] This is the essence of programmability.

Around the end of the 10th century, the French monk Gerbert d'Aurillac brought back from Spain the drawings of a machine invented by the Moors that answered either Yes or No to the questions it was asked.^[9] Again in the 13th century, the monks Albertus Magnus and Roger Bacon built talking androids without any further development (Albertus Magnus complained that he had wasted forty years of his life when Thomas Aquinas, terrified by his machine, destroyed it).^[10]

In 1642, the Renaissance saw the invention of the mechanical calculator,^[11] a device that could perform all four arithmetic operations without relying on human intelligence.^[12] The mechanical calculator was at the root of the development of computers in two separate ways. Initially, it was in trying to develop more powerful and more flexible calculators^[13] that the computer was first theorized by Charles Babbage^[14][15] and then developed.^[16] Secondly, development of a low-cost electronic calculator, successor to the mechanical calculator, resulted in the development by Intel^[17] of the first commercially available microprocessor integrated circuit.

First general-purpose computers[link]

In 1801, Joseph Marie Jacquard made an improvement to the textile loom by introducing a series of punched paper cards as a template which allowed his loom to weave intricate patterns automatically. The resulting Jacquard loom was an important step in the development of computers because the use of punched cards to define woven patterns can be viewed as an early, albeit limited, form of programmability.

The Most Famous Image in the Early History of Computing^[18]

This portrait of Jacquard was woven in silk on a Jacquard loom and required 24,000 punched cards to create (1839). It was only produced to order. Charles Babbage owned one of these portraits ; it inspired him in using perforated cards in his analytical engine^[19]

The Zuse Z3, 1941, considered the world's first working programmable, fully automatic computing machine.

It was the fusion of automatic calculation with programmability that produced the first recognizable computers. In 1837, Charles Babbage was the first to conceptualize and design a fully programmable mechanical computer, his analytical engine.^[20] Limited finances and Babbage's inability to resist tinkering with the design meant that the device was never completed—nevertheless his son, Henry Babbage, completed a simplified version of the analytical engine's computing unit (the mill) in 1888. He gave a successful demonstration of its use in computing tables in 1906. This machine was given to the Science museum in South Kensington in 1910.

In the late 1880s, Herman Hollerith invented the recording of data on a machine-readable medium. Earlier uses of machine-readable media had been for control, not data. "After some initial trials with paper tape, he settled on punched cards ..."^[21] To process these punched cards he invented the tabulator, and the keypunch machines. These three inventions were the foundation of the modern information processing industry. Large-scale automated data processing of punched cards was performed for the 1890 United States Census by Hollerith's company, which later became the core of IBM. By the end of the 19th century a number of ideas and technologies, that would later prove useful in the realization of practical computers, had begun to appear: Boolean algebra, the vacuum tube (thermionic valve), punched cards and tape, and the teleprinter.

During the first half of the 20th century, many scientific computing needs were met by increasingly sophisticated analog computers, which used a direct mechanical or electrical model of the problem as a basis for computation. However, these were not programmable and generally lacked the versatility and accuracy of modern digital computers.

Alan Turing is widely regarded as the father of modern computer science. In 1936 Turing provided an influential formalisation of the concept of the algorithm and computation with the Turing machine, providing a blueprint for the electronic digital computer.^[22] Of his role in the creation of the modern computer, Time magazine in naming Turing one of the 100 most influential people of the 20th century, states: "The fact remains that everyone who taps at a keyboard, opening a spreadsheet or a word-processing program, is working on an incarnation of a Turing machine".^[22]

The ENIAC, which became operational in 1946, is considered to be the first general-purpose electronic computer.

EDSAC was one of the first computers to implement the stored-program (von Neumann) architecture.

The Atanasoff–Berry Computer (ABC) was the world's first electronic digital computer, albeit not programmable.^[23] Atanasoff is considered to be one of the fathers of the computer.^[24] Conceived in 1937 by Iowa State College physics professor John Atanasoff, and built with the assistance of graduate student Clifford Berry,^[25] the machine was not programmable, being designed only to solve systems of linear equations. The computer did employ parallel computation. A 1973 court ruling in a patent dispute found that the patent for the 1946 ENIAC computer derived from the Atanasoff–Berry Computer.

The first program-controlled computer was invented by Konrad Zuse, who built the Z3, an electromechanical computing machine, in 1941.^[26] The first programmable electronic computer was the Colossus, built in 1943 by Tommy Flowers.

George Stibitz is internationally recognized as a father of the modern digital computer. While working at Bell Labs in November 1937, Stibitz invented and built a relay-based calculator he dubbed the "Model K" (for "kitchen table", on which he had assembled it), which was the first to use binary circuits to perform an arithmetic operation. Later models added greater sophistication including complex arithmetic and programmability.^[27]

A succession of steadily more powerful and flexible computing devices were constructed in the 1930s and 1940s, gradually adding the key features that are seen in modern computers. The use of digital electronics (largely invented by Claude Shannon in 1937) and more flexible programmability were vitally important steps, but defining one point along this road as "the first digital electronic computer" is difficult.^{Shannon 1940} Notable achievements include:

• Konrad Zuse's electromechanical "Z machines". The Z3 (1941) was the first working machine featuring binary arithmetic, including floating point arithmetic and a measure of programmability. In 1998 the Z3 was proved to be Turing complete, therefore being the world's first operational computer.^[28]
• The non-programmable Atanasoff–Berry Computer (commenced in 1937, completed in 1941) which used vacuum tube based computation, binary numbers, and regenerative capacitor memory. The use of regenerative memory allowed it to be much more compact than its peers (being approximately the size of a large desk or workbench), since intermediate results could be stored and then fed back into the same set of computation elements.
• The secret British Colossus computers (1943),^[29] which had limited programmability but demonstrated that a device using thousands of tubes could be reasonably reliable and electronically reprogrammable. It was used for breaking German wartime codes.
• The Harvard Mark I (1944), a large-scale electromechanical computer with limited programmability.^[30]
• The U.S. Army's Ballistic Research Laboratory ENIAC (1946), which used decimal arithmetic and is sometimes called the first general purpose electronic computer (since Konrad Zuse's Z3 of 1941 used electromagnets instead of electronics). Initially, however, ENIAC had an inflexible architecture which essentially required rewiring to change its programming.

Stored-program architecture[link]

Several developers of ENIAC, recognizing its flaws, came up with a far more flexible and elegant design, which came to be known as the "stored-program architecture" or von Neumann architecture. This design was first formally described by John von Neumann in the paper First Draft of a Report on the EDVAC, distributed in 1945. A number of projects to develop computers based on the stored-program architecture commenced around this time, the first of which was completed in 1948 at the University of Manchester in England, the Manchester Small-Scale Experimental Machine (SSEM or "Baby"). The Electronic Delay Storage Automatic Calculator (EDSAC), completed a year after the SSEM at Cambridge University, was the first practical, non-experimental implementation of the stored-program design and was put to use immediately for research work at the university. Shortly thereafter, the machine originally described by von Neumann's paper—EDVAC—was completed but did not see full-time use for an additional two years.

Nearly all modern computers implement some form of the stored-program architecture, making it the single trait by which the word "computer" is now defined. While the technologies used in computers have changed dramatically since the first electronic, general-purpose computers of the 1940s, most still use the von Neumann architecture.

Die of an Intel 80486DX2 microprocessor (actual size: 12×6.75 mm) in its packaging.

Beginning in the 1950s, Soviet scientists Sergei Sobolev and Nikolay Brusentsov conducted research on ternary computers, devices that operated on a base three numbering system of −1, 0, and 1 rather than the conventional binary numbering system upon which most computers are based. They designed the Setun, a functional ternary computer, at Moscow State University. The device was put into limited production in the Soviet Union, but supplanted by the more common binary architecture.

Semiconductors and microprocessors[link]

Computers using vacuum tubes as their electronic elements were in use throughout the 1950s, but by the 1960s had been largely replaced by semiconductor transistor-based machines, which were smaller, faster, cheaper to produce, required less power, and were more reliable. The first transistorised computer was demonstrated at the University of Manchester in 1953.^[31] In the 1970s, integrated circuit technology and the subsequent creation of microprocessors, such as the Intel 4004, further decreased size and cost and further increased speed and reliability of computers. By the late 1970s, many products such as video recorders contained dedicated computers called microcontrollers, and they started to appear as a replacement to mechanical controls in domestic appliances such as washing machines. The 1980s witnessed home computers and the now ubiquitous personal computer. With the evolution of the Internet, personal computers are becoming as common as the television and the telephone in the household^{[citation needed]}.

Modern smartphones are fully programmable computers in their own right, and as of 2009 may well be the most common form of such computers in existence^{[citation needed]}.

Programs[link]

Replica of the Small-Scale Experimental Machine (SSEM), the world's first stored-program computer, at the Museum of Science and Industry in Manchester, England

The defining feature of modern computers which distinguishes them from all other machines is that they can be programmed. That is to say that some type of instructions (the program) can be given to the computer, and it will process them. While some computers may have strange concepts "instructions" and "output" (see quantum computing), modern computers based on the von Neumann architecture often have machine code in the form of an imperative programming language.

In practical terms, a computer program may be just a few instructions or extend to many millions of instructions, as do the programs for word processors and web browsers for example. A typical modern computer can execute billions of instructions per second (gigaflops) and rarely makes a mistake over many years of operation. Large computer programs consisting of several million instructions may take teams of programmers years to write, and due to the complexity of the task almost certainly contain errors.

Stored program architecture[link]

This section applies to most common RAM machine-based computers.

In most cases, computer instructions are simple: add one number to another, move some data from one location to another, send a message to some external device, etc. These instructions are read from the computer's memory and are generally carried out (executed) in the order they were given. However, there are usually specialized instructions to tell the computer to jump ahead or backwards to some other place in the program and to carry on executing from there. These are called "jump" instructions (or branches). Furthermore, jump instructions may be made to happen conditionally so that different sequences of instructions may be used depending on the result of some previous calculation or some external event. Many computers directly support subroutines by providing a type of jump that "remembers" the location it jumped from and another instruction to return to the instruction following that jump instruction.

Program execution might be likened to reading a book. While a person will normally read each word and line in sequence, they may at times jump back to an earlier place in the text or skip sections that are not of interest. Similarly, a computer may sometimes go back and repeat the instructions in some section of the program over and over again until some internal condition is met. This is called the flow of control within the program and it is what allows the computer to perform tasks repeatedly without human intervention.

Comparatively, a person using a pocket calculator can perform a basic arithmetic operation such as adding two numbers with just a few button presses. But to add together all of the numbers from 1 to 1,000 would take thousands of button presses and a lot of time, with a near certainty of making a mistake. On the other hand, a computer may be programmed to do this with just a few simple instructions. For example:

      mov No. 0, sum     ; set sum to 0
      mov No. 1, num     ; set num to 1
loop: add num, sum    ; add num to sum
      add No. 1, num     ; add 1 to num
      cmp num, #1000  ; compare num to 1000
      ble loop        ; if num <= 1000, go back to 'loop'
      halt            ; end of program. stop running

Once told to run this program, the computer will perform the repetitive addition task without further human intervention. It will almost never make a mistake and a modern PC can complete the task in about a millionth of a second.^[32]

Bugs[link]

The actual first computer bug, a moth found trapped on a relay of the Harvard Mark II computer

Errors in computer programs are called "bugs". They may be benign and not affect the usefulness of the program, or have only subtle effects. But in some cases they may cause the program or the entire system to "hang" – become unresponsive to input such as mouse clicks or keystrokes – to completely fail, or to crash. Otherwise benign bugs may sometimes be harnessed for malicious intent by an unscrupulous user writing an exploit, code designed to take advantage of a bug and disrupt a computer's proper execution. Bugs are usually not the fault of the computer. Since computers merely execute the instructions they are given, bugs are nearly always the result of programmer error or an oversight made in the program's design.^[33]

Rear Admiral Grace Hopper is credited for having first used the term "bugs" in computing after a dead moth was found shorting a relay in the Harvard Mark II computer in September 1947.^[34]

Machine code[link]

In most computers, individual instructions are stored as machine code with each instruction being given a unique number (its operation code or opcode for short). The command to add two numbers together would have one opcode, the command to multiply them would have a different opcode and so on. The simplest computers are able to perform any of a handful of different instructions; the more complex computers have several hundred to choose from, each with a unique numerical code. Since the computer's memory is able to store numbers, it can also store the instruction codes. This leads to the important fact that entire programs (which are just lists of these instructions) can be represented as lists of numbers and can themselves be manipulated inside the computer in the same way as numeric data. The fundamental concept of storing programs in the computer's memory alongside the data they operate on is the crux of the von Neumann, or stored program, architecture. In some cases, a computer might store some or all of its program in memory that is kept separate from the data it operates on. This is called the Harvard architecture after the Harvard Mark I computer. Modern von Neumann computers display some traits of the Harvard architecture in their designs, such as in CPU caches.

While it is possible to write computer programs as long lists of numbers (machine language) and while this technique was used with many early computers,^[35] it is extremely tedious and potentially error-prone to do so in practice, especially for complicated programs. Instead, each basic instruction can be given a short name that is indicative of its function and easy to remember – a mnemonic such as ADD, SUB, MULT or JUMP. These mnemonics are collectively known as a computer's assembly language. Converting programs written in assembly language into something the computer can actually understand (machine language) is usually done by a computer program called an assembler.

A 1970s punched card containing one line from a FORTRAN program. The card reads: "Z(1) = Y + W(1)" and is labelled "PROJ039" for identification purposes.

Programming language[link]

Programming languages provide various ways of specifying programs for computers to run. Unlike natural languages, programming languages are designed to permit no ambiguity and to be concise. They are purely written languages and are often difficult to read aloud. They are generally either translated into machine code by a compiler or an assembler before being run, or translated directly at run time by an interpreter. Sometimes programs are executed by a hybrid method of the two techniques.

Low-level languages[link]

Machine languages and the assembly languages that represent them (collectively termed low-level programming languages) tend to be unique to a particular type of computer. For instance, an ARM architecture computer (such as may be found in a PDA or a hand-held videogame) cannot understand the machine language of an Intel Pentium or the AMD Athlon 64 computer that might be in a PC.^[36]

Higher-level languages[link]

Though considerably easier than in machine language, writing long programs in assembly language is often difficult and is also error prone. Therefore, most practical programs are written in more abstract high-level programming languages that are able to express the needs of the programmer more conveniently (and thereby help reduce programmer error). High level languages are usually "compiled" into machine language (or sometimes into assembly language and then into machine language) using another computer program called a compiler.^[37] High level languages are less related to the workings of the target computer than assembly language, and more related to the language and structure of the problem(s) to be solved by the final program. It is therefore often possible to use different compilers to translate the same high level language program into the machine language of many different types of computer. This is part of the means by which software like video games may be made available for different computer architectures such as personal computers and various video game consoles.

Program design[link]

Program design of small programs is relatively simple and involves the analysis of the problem, collection of inputs, using the programming constructs within languages, devising or using established procedures and algorithms, providing data for output devices and solutions to the problem as applicable. As problems become larger and more complex, features such as subprograms, modules, formal documentation, and new paradigms such as object-oriented programming are encountered. Large programs involving thousands of line of code and more require formal software methodologies. The task of developing large software systems presents a significant intellectual challenge. Producing software with an acceptably high reliability within a predictable schedule and budget has historically been difficult; the academic and professional discipline of software engineering concentrates specifically on this challenge.

Components[link]

A general purpose computer has four main components: the arithmetic logic unit (ALU), the control unit, the memory, and the input and output devices (collectively termed I/O). These parts are interconnected by busses, often made of groups of wires.

Inside each of these parts are thousands to trillions of small electrical circuits which can be turned off or on by means of an electronic switch. Each circuit represents a bit (binary digit) of information so that when the circuit is on it represents a "1", and when off it represents a "0" (in positive logic representation). The circuits are arranged in logic gates so that one or more of the circuits may control the state of one or more of the other circuits.