Programming Languages

A programming language is an artificial language designed to express computations that can be performed by a machine, particularly a computer. Programming languages can be used to create programs that control the behavior of a machine, to express algorithms precisely, or as a mode of human communication.
Many programming languages have some form of written specification of their syntax (form) and semantics (meaning). Some languages are defined by a specification document. For example, the C programming language is specified by an ISO Standard. Other languages, such as Perl, have a dominant implementation that is used as a reference.
The earliest programming languages predate the invention of the computer, and were used to direct the behavior of machines such as Jacquard looms and player pianos. Thousands of different programming languages have been created, mainly in the computer field, with many more being created every year. Most programming languages describe computation in an imperative style, i.e., as a sequence of commands, although some languages, such as those that support functional programming or logic programming, use alternative forms of description.

Definitions
A programming language is a notation for writing programs, which are specifications of a computation or algorithm. Some, but not all, authors restrict the term "programming language" to those languages that can express all possible algorithms. Traits often considered important for what constitutes a programming language include:

• Function and target: A computer programming language is a language used to write computer programs, which involve a computer performing some kind of computation or algorithm and possibly control external devices such as printers, disk drives, robots, and so on. For example PostScript programs are frequently created by another program to control a computer printer or display. More generally, a programming language may describe computation on some, possibly abstract, machine. It is generally accepted that a complete specification for a programming language includes a description, possibly idealized, of a machine or processor for that language. In most practical contexts, a programming language involves a computer; consequently programming languages are usually defined and studied this way. Programming languages differ from natural languages in that natural languages are only used for interaction between people, while programming languages also allow humans to communicate instructions to machines.
• Abstractions: Programming languages usually contain abstractions for defining and manipulating data structures or controlling the flow of execution. The practical necessity that a programming language support adequate abstractions is expressed by the abstraction principle; this principle is sometimes formulated as recommendation to the programmer to make proper use of such abstractions.
• Expressive power: The theory of computation classifies languages by the computations they are capable of expressing. All Turing complete languages can implement the same set of algorithms. ANSI/ISO SQL and Charity are examples of languages that are not Turing complete, yet often called programming languages.Markup languages like XML, HTML or troff, which define structured data, are not generally considered programming languages. Programming languages may, however, share the syntax with markup languages if a computational semantics is defined. XSLT, for example, is a Turing complete XML dialect.Moreover, LaTeX, which is mostly used for structuring documents, also contains a Turing complete subset.

Elements
All programming languages have some primitive building blocks for the description of data and the processes or transformations applied to them (like the addition of two numbers or the selection of an item from a collection). These primitives are defined by syntactic and semantic rules which describe their structure and meaning respectively.
The term computer language is sometimes used interchangeably with programming language. However, the usage of both terms varies among authors, including the exact scope of each. One usage describes programming languages as a subset of computer languages. In this vein, languages used in computing that have a different goal than expressing computer programs are generically designated computer languages. For instance, markup languages are sometimes referred to as computer languages to emphasize that they are not meant to be used for programming. Another usage regards programming languages as theoretical constructs for programming abstract machines, and computer languages as the subset thereof that runs on physical computers, which have finite hardware resources. John C. Reynolds emphasizes that formal specification languages are just as much programming languages as are the languages intended for execution. He also argues that textual and even graphical input formats that affect the behavior of a computer are programming languages, despite the fact they are commonly not Turing-complete, and remarks that ignorance of programming language concepts is the reason for many flaws in input formats.

Syntax
A programming language's surface form is known as its syntax. Most programming languages are purely textual; they use sequences of text including words, numbers, and punctuation, much like written natural languages. On the other hand, there are some programming languages which are more graphical in nature, using visual relationships between symbols to specify a program.
The syntax of a language describes the possible combinations of symbols that form a syntactically correct program. The meaning given to a combination of symbols is handled by semantics (either formal or hard-coded in a reference implementation). Since most languages are textual, this article discusses textual syntax.
Programming language syntax is usually defined using a combination of regular expressions (for lexical structure) and Backus–Naur Form (for grammatical structure).
Static semantics
.For compiled languages, static semantics essentially include those semantic rules that can be checked at compile time. Examples include checking that every identifier is declared before it is used (in languages that require such declarations) or that the labels on the arms of a case statement are distinct. Many important restrictions of this type, like checking that identifiers are used in the appropriate context (e.g. not adding a integer to a function name), or that subroutine calls have the appropriate number and type of arguments can be enforced by defining them as rules in a logic called a type system. Other forms of static analyses like data flow analysis may also be part of static semantics. Newer programming languages like Java and C# have definite assignment analysis, a form of data flow analysis, as part of their static semantics.

Type system
A type system defines how a programming language classifies values and expressions into types, how it can manipulate those types and how they interact. The goal of a type system is to verify and usually enforce a certain level of correctness in programs written in that language by detecting certain incorrect operations. Any decidable type system involves a trade-off: while it rejects many incorrect programs, it can also prohibit some correct, albeit unusual programs. In order to bypass this downside, a number of languages have type loopholes, usually unchecked casts that may be used by the programmer to explicitly allow a normally disallowed operation between different types. In most typed languages, the type system is used only to type check programs, but a number of languages, usually functional ones, perform type inference, which relieves the programmer from writing type annotations. The formal design and study of type systems is known as type theory
.Typed versus untyped languages
A language is typed if the specification of every operation defines types of data to which the operation is applicable, with the implication that it is not applicable to other types. For example, "this text between the quotes" is a string. In most programming languages, dividing a number by a string has no meaning. Most modern programming languages will therefore reject any program attempting to perform such an operation. In some languages, the meaningless operation will be detected when the program is compiled ("static" type checking), and rejected by the compiler, while in others, it will be detected when the program is run ("dynamic" type checking), resulting in a runtime exception.
A special case of typed languages are the single-type languages. These are often scripting or markup languages, such as REXX or SGML, and have only one data type—most commonly character strings which are used for both symbolic and numeric data.
In contrast, an untyped language, such as most assembly languages, allows any operation to be performed on any data, which are generally considered to be sequences of bits of various lengths. High-level languages which are untyped include BCPL and some varieties of Forth.
In practice, while few languages are considered typed from the point of view of type theory (verifying or rejecting all operations), most modern languages offer a degree of typing. Many production languages provide means to bypass or subvert the type system.

Static versus dynamic typing
In static typing all expressions have their types determined prior to the program being run (typically at compile-time). For example, 1 and (2+2) are integer expressions; they cannot be passed to a function that expects a string, or stored in a variable that is defined to hold dates.
Statically typed languages can be either manifestly typed or type-inferred. In the first case, the programmer must explicitly write types at certain textual positions (for example, at variable declarations). In the second case, the compiler infers the types of expressions and declarations based on context. Most mainstream statically typed languages, such as C++, C# and Java, are manifestly typed. Complete type inference has traditionally been associated with less mainstream languages, such as Haskell and ML. However, many manifestly typed languages support partial type inference; for example, Java and C# both infer types in certain limited cases.
Dynamic typing, also called latent typing, determines the type-safety of operations at runtime; in other words, types are associated with runtime values rather than textual expressions. As with type-inferred languages, dynamically typed languages do not require the programmer to write explicit type annotations on expressions. Among other things, this may permit a single variable to refer to values of different types at different points in the program execution. However, type errors cannot be automatically detected until a piece of code is actually executed, making debugging more difficult. Ruby, Lisp, JavaScript, and Python are dynamically typed.

Weak and strong typing
Weak typing allows a value of one type to be treated as another, for example treating a string as a number. This can occasionally be useful, but it can also allow some kinds of program faults to go undetected at compile time and even at runtime.
Strong typing prevents the above. An attempt to perform an operation on the wrong type of value raises an error. Strongly typed languages are often termed type-safe or safe.
An alternative definition for "weakly typed" refers to languages, such as Perl and JavaScript, which permit a large number of implicit type conversions. In JavaScript, for example, the expression 2 * x implicitly converts x to a number, and this conversion succeeds even if x is null, undefined, an Array, or a string of letters. Such implicit conversions are often useful, but they can mask programming errors.
Strong and static are now generally considered orthogonal concepts, but usage in the literature differs. Some use the term strongly typed to mean strongly, statically typed, or, even more confusingly, to mean simply statically typed. Thus C has been called both strongly typed and weakly, statically typed.

Execution semantics
Once data has been specified, the machine must be instructed to perform operations on the data. For example, the semantics may define the strategy by which expressions are evaluated to values, or the manner in which control structures conditionally execute statements. The execution semantics (also known as dynamic semantics) of a language defines how and when the various constructs of a language should produce a program behavior. There are many ways of defining execution semantics. Natural language is often used to specify the execution semantics of languages commonly used in practice. A significant amount of academic research went into formal semantics of programming languages, which allow execution semantics to be specified in a formal manner. Results from this field of research have seen limited application to programming language design and implementation outside academia.

Core library
Most programming languages have an associated core library (sometimes known as the 'standard library', especially if it is included as part of the published language standard), which is conventionally made available by all implementations of the language. Core libraries typically include definitions for commonly used algorithms, data structures, and mechanisms for input and output.
A language's core library is often treated as part of the language by its users, although the designers may have treated it as a separate entity. Many language specifications define a core that must be made available in all implementations, and in the case of standardized languages this core library may be required. The line between a language and its core library therefore differs from language to language. Indeed, some languages are designed so that the meanings of certain syntactic constructs cannot even be described without referring to the core library. For example, in Java, a string literal is defined as an instance of the java.lang.String class; similarly, in Smalltalk, an anonymous function expression (a "block") constructs an instance of the library's BlockContext class. Conversely, Scheme contains multiple coherent subsets that suffice to construct the rest of the language as library macros, and so the language designers do not even bother to say which portions of the language must be implemented as language constructs, and which must be implemented as parts of a library.

Design and implementation
Programming languages share properties with natural languages related to their purpose as vehicles for communication, having a syntactic form separate from its semantics, and showing language families of related languages branching one from another. But as artificial constructs, they also differ in fundamental ways from languages that have evolved through usage. A significant difference is that a programming language can be fully described and studied in its entirety, since it has a precise and finite definition. By contrast, natural languages have changing meanings given by their users in different communities. While constructed languages are also artificial languages designed from the ground up with a specific purpose, they lack the precise and complete semantic definition that a programming language has.
Many languages have been designed from scratch, altered to meet new needs, combined with other languages, and eventually fallen into disuse. Although there have been attempts to design one "universal" programming language that serves all purposes, all of them have failed to be generally accepted as filling this role. The need for diverse programming languages arises from the diversity of contexts in which languages are used:

• Programs range from tiny scripts written by individual hobbyists to huge systems written by hundreds of programmers.
• Programmers range in expertise from novices who need simplicity above all else, to experts who may be comfortable with considerable complexity.
• Programs must balance speed, size, and simplicity on systems ranging from microcontrollers to supercomputers.
• Programs may be written once and not change for generations, or they may undergo continual modification.
• Finally, programmers may simply differ in their tastes: they may be accustomed to discussing problems and expressing them in a particular language.

One common trend in the development of programming languages has been to add more ability to solve problems using a higher level of abstraction. The earliest programming languages were tied very closely to the underlying hardware of the computer. As new programming languages have developed, features have been added that let programmers express ideas that are more remote from simple translation into underlying hardware instructions. Because programmers are less tied to the complexity of the computer, their programs can do more computing with less effort from the programmer. This lets them write more functionality per time unit.
Natural language processors have been proposed as a way to eliminate the need for a specialized language for programming. However, this goal remains distant and its benefits are open to debate. Edsger W. Dijkstra took the position that the use of a formal language is essential to prevent the introduction of meaningless constructs, and dismissed natural language programming as "foolish". Alan Perlis was similarly dismissive of the idea. Hybrid approaches have been taken in Structured English and SQL.
A language's designers and users must construct a number of artifacts that govern and enable the practice of programming. The most important of these artifacts are the language specification and implementation.

Specification
The specification of a programming language is intended to provide a definition that the language users and the implementors can use to determine whether the behavior of a program is correct, given its source code.
A programming language specification can take several forms, including the following:

• An explicit definition of the syntax, static semantics, and execution semantics of the language. While syntax is commonly specified using a formal grammar, semantic definitions may be written in natural language (e.g., as in the C language), or a formal semantics (e.g., as in Standard ML and Scheme specifications).
• A description of the behavior of a translator for the language (e.g., the C++ and Fortran specifications). The syntax and semantics of the language have to be inferred from this description, which may be written in natural or a formal language.
• A reference or model implementation, sometimes written in the language being specified (e.g., Prolog or ANSI REXX). The syntax and semantics of the language are explicit in the behavior of the reference implementation.

Implementation

An implementation of a programming language provides a way to execute that program on one or more configurations of hardware and software. There are, broadly, two approaches to programming language implementation: compilation and interpretation. It is generally possible to implement a language using either technique.
The output of a compiler may be executed by hardware or a program called an interpreter. In some implementations that make use of the interpreter approach there is no distinct boundary between compiling and interpreting. For instance, some implementations of BASIC compile and then execute the source a line at a time.
Programs that are executed directly on the hardware usually run several orders of magnitude faster than those that are interpreted in software.One technique for improving the performance of interpreted programs is just-in-time compilation. Here the virtual machine, just before execution, translates the blocks of bytecode which are going to be used to machine code, for direct execution on the hardware.

Usage
Thousands of different programming languages have been created, mainly in the computing field. Programming languages differ from most other forms of human expression in that they require a greater degree of precision and completeness. When using a natural language to communicate with other people, human authors and speakers can be ambiguous and make small errors, and still expect their intent to be understood. However, figuratively speaking, computers "do exactly what they are told to do", and cannot "understand" what code the programmer intended to write. The combination of the language definition, a program, and the program's inputs must fully specify the external behavior that occurs when the program is executed, within the domain of control of that program.
A programming language provides a structured mechanism for defining pieces of data, and the operations or transformations that may be carried out automatically on that data. A programmer uses the abstractions present in the language to represent the concepts involved in a computation. These concepts are represented as a collection of the simplest elements available (called primitives). Programming is the process by which programmers combine these primitives to compose new programs, or adapt existing ones to new uses or a changing environment.
Programs for a computer might be executed in a batch process without human interaction, or a user might type commands in an interactive session of an interpreter. In this case the "commands" are simply programs, whose execution is chained together. When a language is used to give commands to a software application (such as a shell) it is called a scripting language.

 Measuring language usage
It is difficult to determine which programming languages are most widely used, and what usage means varies by context. One language may occupy the greater number of programmer hours, a different one have more lines of code, and a third utilize the most CPU time. Some languages are very popular for particular kinds of applications. For example, COBOL is still strong in the corporate data center, often on large mainframes; FORTRAN in engineering applications; C in embedded applications and operating systems; and other languages are regularly used to write many different kinds of applications.
Various methods of measuring language popularity, each subject to a different bias over what is measured, have been proposed:

• counting the number of job advertisements that mention the language
• the number of books sold that teach or describe the language
• estimates of the number of existing lines of code written in the language—which may underestimate languages not often found in public searches
• counts of language references (i.e., to the name of the language) found using a web search engine.

Combining and averaging information from various internet sites, langpop.com claims that  in 2008 the 10 most cited programming languages are (in alphabetical order): C, C++, C#, Java, JavaScript, Perl, PHP, Python, Ruby, and SQL.

Taxonomies
There is no overarching classification scheme for programming languages. A given programming language does not usually have a single ancestor language. Languages commonly arise by combining the elements of several predecessor languages with new ideas in circulation at the time. Ideas that originate in one language will diffuse throughout a family of related languages, and then leap suddenly across familial gaps to appear in an entirely different family.
The task is further complicated by the fact that languages can be classified along multiple axes. For example, Java is both an object-oriented language (because it encourages object-oriented organization) and a concurrent language (because it contains built-in constructs for running multiple threads in parallel). Python is an object-oriented scripting language.
In broad strokes, programming languages divide into programming paradigms and a classification by intended domain of use. Traditionally, programming languages have been regarded as describing computation in terms of imperative sentences, i.e. issuing commands. These are generally called imperative programming languages. A great deal of research in programming languages has been aimed at blurring the distinction between a program as a set of instructions and a program as an assertion about the desired answer, which is the main feature of declarative programming. More refined paradigms include procedural programming, object-oriented programming, functional programming, and logic programming; some languages are hybrids of paradigms or multi-paradigmatic. An assembly language is not so much a paradigm as a direct model of an underlying machine architecture. By purpose, programming languages might be considered general purpose, system programming languages, scripting languages, domain-specific languages, or concurrent/distributed languages (or a combination of these). Some general purpose languages were designed largely with educational goals.
A programming language may also be classified by factors unrelated to programming paradigm. For instance, most programming languages use English language keywords, while a minority do not. Other languages may be classified as being esoteric or not.

Computer Software

Computer software, or just software, is the collection of computer programs and related data that provide the instructions telling a computer what to do. We can also say software refers to one or more computer programs and data held in the storage of the computer for some purposes. Program software performs the function of the program it implements, either by directly providing instructions to the computer hardware or by serving as input to another piece of software. The term was coined to contrast to the old term hardware (meaning physical devices). In contrast to hardware, software is intangible, meaning it "cannot be touched". Software is also sometimes used in a more narrow sense, meaning application software only. Sometimes the term includes data that has not traditionally been associated with computers, such as film, tapes, and records.
Examples of computer software include:

• Application software includes end-user applications of computers such as word processors or video games, and ERP software for groups of users.
• Middleware controls and co-ordinates distributed systems.
• Programming languages define the syntax and semantics of computer programs. For example, many mature banking applications were written in the COBOL language, originally invented in 1959. Newer applications are often written in more modern programming languages.
• System software includes operating systems, which govern computing resources. Today large applications running on remote machines such as Websites are considered to be system software, because the end-user interface is generally through a graphical user interface, such as a web browser.
• Testware is software for testing hardware or a software package.
• Firmware is low-level software often stored on electrically programmable memory devices. Firmware is given its name because it is treated like hardware and run ("executed") by other software programs.
• Shrinkware is the older name given to consumer-purchased software, because it was often sold in retail stores in a shrink-wrapped box.
• Device drivers control parts of computers such as disk drives, printers, CD drives, or computer monitors.
• Programming tools help conduct computing tasks in any category listed above. For programmers, these could be tools for debugging or reverse engineering older legacy systems in order to check source code compatibility.

History

For the history prior to 1946, see History of computing hardware.

The first theory about software was proposed by Alan Turing in his 1935 essay Computable numbers with an application to the Entscheidungsproblem (Decision problem). The term "software" was first used in print by John W. Tukey in 1958. Colloquially, the term is often used to mean application software. In computer science and software engineering, software is all information processed by computer system, programs and data. The academic fields studying software are computer science and software engineering.
The history of computer software is most often traced back to the first software bug in 1946. As more and more programs enter the realm of firmware, and the hardware itself becomes smaller, cheaper and faster due to Moore's law, elements of computing first considered to be software, join the ranks of hardware. Most hardware companies today have more software programmers on the payroll than hardware designers, since software tools have automated many tasks of Printed circuit board engineers. Just like the Auto industry, the Software industry has grown from a few visionaries operating out of their garage with prototypes. Steve Jobs and Bill Gates were the Henry Ford and Louis Chevrolet of their times, who capitalized on ideas already commonly known before they started in the business. In the case of Software development, this moment is generally agreed to be the publication in the 1980s of the specifications for the IBM Personal Computer published by IBM employee Philip Don Estridge. Today his move would be seen as a type of crowd-sourcing.
Until that time, software was bundled with the hardware by Original equipment manufacturers (OEMs) such as Data General, Digital Equipment and IBM. When a customer bought a minicomputer, at that time the smallest computer on the market, the computer did not come with Pre-installed software, but needed to be installed by engineers employed by the OEM. Computer hardware companies not only bundled their software, they also placed demands on the location of the hardware in a refrigerated space called a computer room. Most companies had their software on the books for 0 dollars, unable to claim it as an asset (this is similar to financing of popular music in those days). When Data General introduced the Data General Nova, a company called Digidyne wanted to use its RDOS operating system on its own hardware clone. Data General refused to license their software (which was hard to do, since it was on the books as a free asset), and claimed their "bundling rights". The Supreme Court set a precedent called Digidyne v. Data General in 1985. The Supreme Court let a 9th circuit decision stand, and Data General was eventually forced into licensing the Operating System software because it was ruled that restricting the license to only DG hardware was an illegal tying arrangement. Soon after, IBM 'published' its DOS source for free, and Microsoft was born. Unable to sustain the loss from lawyer's fees, Data General ended up being taken over by EMC Corporation. The Supreme Court decision made it possible to value software, and also purchase Software patents. The move by IBM was almost a protest at the time. Few in the industry believed that anyone would profit from it other than IBM (through free publicity). Microsoft and Apple were able to thus cash in on 'soft' products. It is hard to imagine today that people once felt that software was worthless without a machine. There are many successful companies today that sell only software products, though there are still many common software licensing problems due to the complexity of designs and poor documentation, leading to patent trolls.
With open software specifications and the possibility of software licensing, new opportunities arose for software tools that then became the de facto standard, such as DOS for operating systems, but also various proprietary word processing and spreadsheet programs. In a similar growth pattern, proprietary development methods became standard Software development methodology.

Software includes all the various forms and roles that digitally stored data may have and play in a computer (or similar system), regardless of whether the data is used as code for a CPU, or other interpreter, or whether it represents other kinds of information. Software thus encompasses a wide array of products that may be developed using different techniques such as ordinary programming languages, scripting languages, microcode, or an FPGA configuration.

The types of software include web pages developed in languages and frameworks like HTML, PHP, Perl, JSP, ASP.NET, XML, and desktop applications like OpenOffice.org, Microsoft Word developed in languages like C, C++, Java, C#, or Smalltalk. Application software usually runs on an underlying software operating systems such as Linux or Microsoft Windows. Software (or firmware) is also used in video games and for the configurable parts of the logic systems of automobiles, televisions, and other consumer electronics.
Computer software is so called to distinguish it from computer hardware, which encompasses the physical interconnections and devices required to store and execute (or run) the software. At the lowest level, executable code consists of machine language instructions specific to an individual processor. A machine language consists of groups of binary values signifying processor instructions that change the state of the computer from its preceding state. Programs are an ordered sequence of instructions for changing the state of the computer in a particular sequence. It is usually written in high-level programming languages that are easier and more efficient for humans to use (closer to natural language) than machine language. High-level languages are compiled or interpreted into machine language object code. Software may also be written in an assembly language, essentially, a mnemonic representation of a machine language using a natural language alphabet. Assembly language must be assembled into object code via an assembler.
Types of software
Practical computer systems divide software systems into three major classes: system software, programming software and application software, although the distinction is arbitrary, and often blurred.
System software
System software provides the basic functions for computer usage and helps run the computer hardware and system. It includes a combination of the following:

• device drivers
• operating systems
• servers
• utilities
• window systems

System software is responsible for managing a variety of independent hardware components, so that they can work together harmoniously. Its purpose is to unburden the application software programmer from the often complex details of the particular computer being used, including such accessories as communications devices, printers, device readers, displays and keyboards, and also to partition the computer's resources such as memory and processor time in a safe and stable manner.

Programming software

Programming software usually provides tools to assist a programmer in writing computer programs, and software using different programming languages in a more convenient way. The tools include:

• compilers
• debuggers
• interpreters
• linkers
• text editors

An Integrated development environment (IDE) is a single application that attempts to manage all these functions.

Application software

System software does not aim at a certain application fields.In contrast,different application software offers different function based on users and the area it served.Application software is developed for some certain purpose,which either can be a certain program or a collection of some programmes,such as a graphic browser or the data base management system. Application software allows end users to accomplish one or more specific (not directly computer development related) tasks. Typical applications include:

• industrial automation
• business software
• video games
• quantum chemistry and solid state physics software
• telecommunications (i.e., the Internet and everything that flows on it)
• databases
• educational software
• Mathematical software
• medical software
• molecular modeling software
• image editing
• spreadsheet
• simulation software
• Word processing
• Decision making software

Application software exists for and has impacted a wide variety of topics.

Software topics

Architecture

Users often see things differently than programmers. People who use modern general purpose computers (as opposed to embedded systems, analog computers and supercomputers) usually see three layers of software performing a variety of tasks: platform, application, and user software.

• Platform software: Platform includes the firmware, device drivers, an operating system, and typically a graphical user interface which, in total, allow a user to interact with the computer and its peripherals (associated equipment). Platform software often comes bundled with the computer. On a PC you will usually have the ability to change the platform software.
• Application software: Application software or Applications are what most people think of when they think of software. Typical examples include office suites and video games. Application software is often purchased separately from computer hardware. Sometimes applications are bundled with the computer, but that does not change the fact that they run as independent applications. Applications are usually independent programs from the operating system, though they are often tailored for specific platforms. Most users think of compilers, databases, and other "system software" as applications.
• User-written software: End-user development tailors systems to meet users' specific needs. User software include spreadsheet templates and word processor templates. Even email filters are a kind of user software. Users create this software themselves and often overlook how important it is. Depending on how competently the user-written software has been integrated into default application packages, many users may not be aware of the distinction between the original packages, and what has been added by co-workers.

Documentation

Most software has software documentation so that the end user can understand the program, what it does, and how to use it. Without clear documentation, software can be hard to use—especially if it is very specialized and relatively complex like Photoshop or AutoCAD.
Developer documentation may also exist, either with the code as comments and/or as separate files, detailing how the programs works and can be modified.

Library

An executable is almost always not sufficiently complete for direct execution. Software libraries include collections of functions and functionality that may be embedded in other applications. Operating systems include many standard Software libraries, and applications are often distributed with their own libraries.

Standard

Since software can be designed using many different programming languages and in many different operating systems and operating environments, software standard is needed so that different software can understand and exchange information between each other. For instance, an email sent from a Microsoft Outlook should be readable from Yahoo! Mail and vice versa.

 Execution

Computer software has to be "loaded" into the computer's storage (such as the hard drive or memory). Once the software has loaded, the computer is able to execute the software. This involves passing instructions from the application software, through the system software, to the hardware which ultimately receives the instruction as machine code. Each instruction causes the computer to carry out an operation – moving data, carrying out a computation, or altering the control flow of instructions.
Data movement is typically from one place in memory to another. Sometimes it involves moving data between memory and registers which enable high-speed data access in the CPU. Moving data, especially large amounts of it, can be costly. So, this is sometimes avoided by using "pointers" to data instead. Computations include simple operations such as incrementing the value of a variable data element. More complex computations may involve many operations and data elements together.

 Quality and reliability

Software quality is very important, especially for commercial and system software like Microsoft Office, Microsoft Windows and Linux. If software is faulty (buggy), it can delete a person's work, crash the computer and do other unexpected things. Faults and errors are called "bugs." Many bugs are discovered and eliminated (debugged) through software testing. However, software testing rarely – if ever – eliminates every bug; some programmers say that "every program has at least one more bug" (Lubarsky's Law). All major software companies, such as Microsoft, Novell and Sun Microsystems, have their own software testing departments with the specific goal of just testing. Software can be tested through unit testing, regression testing and other methods, which are done manually, or most commonly, automatically, since the amount of code to be tested can be quite large. For instance, NASA has extremely rigorous software testing procedures for many operating systems and communication functions. Many NASA based operations interact and identify each other through command programs called software. This enables many people who work at NASA to check and evaluate functional systems overall. Programs containing command software enable hardware engineering and system operations to function much easier together.

License

The software's license gives the user the right to use the software in the licensed environment. Some software comes with the license when purchased off the shelf, or an OEM license when bundled with hardware. Other software comes with a free software license, granting the recipient the rights to modify and redistribute the software. Software can also be in the form of freeware or shareware.

Patents

Software can be patented; however, software patents can be controversial in the software industry with many people holding different views about it. The controversy over software patents is that a specific algorithm or technique that the software has may not be duplicated by others and is considered an intellectual property and copyright infringement depending on the severity.

Design and implementation

Main articles: Software development, Computer programming, and Software engineering

Design and implementation of software varies depending on the complexity of the software. For instance, design and creation of Microsoft Word software will take much more time than designing and developing Microsoft Notepad because of the difference in functionalities in each one.
Software is usually designed and created (coded/written/programmed) in integrated development environments (IDE) like Eclipse, Emacs and Microsoft Visual Studio that can simplify the process and compile the program. As noted in different section, software is usually created on top of existing software and the application programming interface (API) that the underlying software provides like GTK+, JavaBeans or Swing. Libraries (APIs) are categorized for different purposes. For instance, JavaBeans library is used for designing enterprise applications, Windows Forms library is used for designing graphical user interface (GUI) applications like Microsoft Word, and Windows Communication Foundation is used for designing web services. Underlying computer programming concepts like quicksort, hashtable, array, and binary tree can be useful to creating software. When a program is designed, it relies on the API. For instance, if a user is designing a Microsoft Windows desktop application, he/she might use the .NET Windows Forms library to design the desktop application and call its APIs like Form1.Close() and Form1.Show() to close or open the application and write the additional operations him/herself that it need to have. Without these APIs, the programmer needs to write these APIs him/herself. Companies like Sun Microsystems, Novell, and Microsoft provide their own APIs so that many applications are written using their software libraries that usually have numerous APIs in them.
Computer software has special economic characteristics that make its design, creation, and distribution different from most other economic goods. A person who creates software is called a programmer, software engineer, software developer, or code monkey, terms that all have a similar meaning.

Industry and organizations

A great variety of software companies and programmers in the world comprise a software industry . Software can be quite a profitable industry: Bill Gates, the founder of Microsoft was the richest person in the world in 2009 largely by selling the Microsoft Windows and Microsoft Office software products. The same goes for Larry Ellison, largely through his Oracle database software. Through time the software industry has become increasingly specialized.
Non-profit software organizations include the Free Software Foundation, GNU Project and Mozilla Foundation. Software standard organizations like the W3C, IETF develop software standards so that most software can interoperate through standards such as XML, HTML, HTTP or FTP.
Other well-known large software companies include Novell, SAP, Symantec, Adobe Systems, and Corel, while small companies often provide innovation.

Input - Output Devices

Input/output device, also known as computer peripheral, any of various devices (including sensors) used to enter information and instructions into a computer for storage or processing and to deliver the processed data to a human operator or, in some cases, a machine controlled by the computer. Such devices make up the peripheral equipment of modern digital computer systems.

An input device converts incoming data and instructions into a pattern of electrical signals in binary code that are comprehensible to a digital computer. An output device reverses the process, translating the digitized signals into a form intelligible to the user. At one time punched-card and paper-tape readers were extensively used for inputting, but these have now been supplanted by more efficient devices.

Input devices include typewriter-like keyboards; hand-held devices such as the mouse, trackball, joystick, and special pen with pressure-sensitive pad; and microphones. They also include sensors that provide information about their environment—temperature, pressure, and so forth—to a computer. Another direct-entry mechanism is the optical laser scanner (e.g., scanners used with point-of-sale terminals in retail stores) that can read bar-coded data or optical character fonts. Output equipment includes video display terminals (either cathode-ray tubes or liquid crystal displays), ink-jet and laser printers, loudspeakers, and devices such as flow valves that control machinery, often in response to computer processing of sensor input data. Some devices, such as video display terminals, may provide both input and output. Other examples are devices that enable the transmission and reception of data between computers—e.g., modems and network interfaces. Most auxiliary storage devices—as, for example, magnetic tape, magnetic disk drives, and certain types of optical compact discs—also double as input/output devices (see computer memory).

Various standards for connecting peripherals to computers exist. For example, integrated drive electronics (IDE) and enhanced integrated drive electronics (EIDE) are common interfaces, or buses, for magnetic disk drives. A bus (also known as a port) can be either serial or parallel, depending on whether the data path carries one bit at a time (serial) or many at once (parallel). Serial connections, which use relatively few wires, are generally simpler and slower than parallel connections. Universal serial bus (USB) is a common serial bus. A common example of a parallel bus is the small computer systems interface, or SCSI, bus.

CPU Primary and Secondary Storage

The  CPU  contains  circuits  that  control  and  execute instructions by using some type of MEMORY. Memory is referred to by size, such as 16K, 32K, 64K, and so on. The "K" represents the value of 1,000. Therefore 16,000 is 16K. Semiconductor  memory  consists  of  hundreds  of thousands of tiny electronic circuits etched on a silicon chip. Each of these electronic circuits is called a BIT CELL and can be in either an OFF or ON state to represent a 0 or 1 bit. This state depends on whether or not current is flowing in that cell. Another name used for semiconductor memory chips is integrated circuits (ICs).  Developments  in  technology  have  led  to large-scale  integration  (LSI)  that  allows  more  and  more circuits to be squeezed onto the same silicon chip. Some of the advantages of semiconductor storage are  fast  internal  processing  speeds,  high  reliability,  low power consumption, high density (many circuits), and low cost. However, there is a drawback to this type of storage. It may be VOLATILE, which means it requires a constant power source. When the power for your system fails and you have no backup power supply, all of the stored data is lost. Primary  Storage Two classifications of primary storage with which you  should  become  familiar  are  read-only  memory (ROM) and random-access memory (RAM). READ-ONLY MEMORY (ROM).—In computers, it is useful to have instructions that are used often, permanently stored inside the computer. ROM enables us to do this without loosing the programs and data when  the  computer  is  powered  down.  Only  the computer manufacturer can provide these programs in ROM; once done, you cannot change it. Consequently, you cannot put any of your own data or programs in ROM. Many complex functions, such as translators for high-level  languages,  and  operating  systems  are  placed in  ROM  memory. Since these instructions are hardwired, they can be performed quickly and accurately. Another advantage of  ROM  is  that  your  imaging  facility  can  order programs tailored for its specific needs and have them installed permanently in ROM. Such programs are called microprograms or firmware. RANDOM-ACCESS  MEMORY  (RAM).—RAM is another type of memory found inside computers. It
may be compared to a chalkboard on which you canscribble down notes, read them, and erase them whenfinished.  In  the  computer,  RAM  is  the  workingmemory.  Data  can  be  read  (retrieved)  or  written(stored) in RAM by providing the computer with anaddress location where the data is stored or where youwant  it  to  be  stored.  When  the  data  is  no  longerrequited, you may simply write over it. Thus you canuse the storage location again for something else.Secondary  StorageSecondary  storage,  or  auxiliary  storage,  is  memoryexternal to the main body of the computer (CPU) whereprograms and data can be stored for future use. Whenthe computer is ready to use these programs, the data isread  into  primary  storage.  Secondary  storage  mediaextends the storage capabilities of the computer system.Secondary storage is required for two reasons. First, theworking memory of the CPU is limited in size andcannot  always  hold  the  amount  of  data  required.Second, data and programs in secondary programs donot disappear when the power is turned off. Secondarystorage  is  nonvolatile  memory.  This  information  is  lostonly when you erase it. Magnetic disks are the mostcommon type of secondary storage. They may be eitherfloppy disks or hard disks (hard drives).PERIPHERAL DEVICESPeripheral devices include all the input and outputdevices used with a computer system. When thesedevices are under control of the CPU, they are said tobe   on   line.   When   they   perform   their   functionindependently, not under direct control of the CPU, theyare said to be off line. The following peripheral devicesare used commonly for input and output. Those thatperform only input are marked (I), those that performonly output are marked (O), and those that perform bothinput and output are marked (I/O).Optical Character Reader (I)An optical character reader reads printed data(characters) and translates it to machine code.Keyboard  (I)The keyboard is used by a computer operator tocommunicate with a computer system.

Processor Memory Unit

The processor memory unit is the interface between the processor and the caches. Currently, instruction caches are not simulated and are assumed to be perfect. RSIM also does not currently support virtual memory. A processor's accesses to its private data space are currently considered to be cache hits in all multiprocessor simulations and in uniprocessor simulations configured for this purpose. However, contention at all processor and cache resources and all memory ordering constraints are modeled for private accesses in all cases.
The most important responsibility of the processor memory unit is to insure that memory instructions occur in the correct order. There are three types of ordering constraints that must be upheld:

1. Constraints to guarantee precise exceptions
2. Constraints to satisfy uniprocessor data dependences
3. Constraints due to the multiprocessor memory consistency model

Constraints for precise exceptions
The RSIM memory system supports non-blocking loads and stores. To maintain precise exceptions, a store cannot issue before it is ready to be graduated; namely, it must be one of the instructions to graduate in the next cycle and all previous instructions must have completed successfully. A store can be allowed to graduate, as it does not need to maintain a space in the active list for any later dependences. However, if it is not able to issue to the cache before graduating, it must hold a slot in the memory unit until it is actually sent to the cache. The store can leave the memory unit as soon as it has issued, unless the multiprocessor memory constraints require the store to remain in the unit.
Loads always wait until completion before leaving the memory unit or graduating, as loads must write a destination register value. Prefetches can leave the memory unit as soon as they are issued to the cache, as these instructions have no destination register value. Furthermore, there are no additional constraints on the graduation of prefetches.


Constraints for uniprocessor data depedences
These constraints require that a processor's conflicting loads and stores (to the same address) appear to execute in program order. The precise exception constraint ensures that this condition holds for two stores and for a load followed by a store. For a store followed by a load, the processor may need to maintains this data dependence by enforcing additional constraints on the execution of the load. If the load has generated its address, the state of the store address determines whether or not the load can issue. Specifically, the prior store must be in one of the following three categories:

1. address is known, non-conflicting
2. address is known, conflicting
3. address is unknown

In the first case, there is no data dependence from the store to the load. As a result, the load can issue to the cache in all configuration options, as long as the multiprocessor ordering constraints allow the load to proceed.
In the second case, the processor knows that there is a data dependence from the store to the load. If the store matches the load address exactly, the load can forward its return value from the value of the store in the memory unit without ever having to issue to cache, if the multiprocessor ordering constraints allow this. If the load address and the store address only partially overlap, the load may have to stall until the store has completed at the caches; such a stall is called a partial overlap, and is discussed further.
In the third case, however, the load may or may not have a data dependence on the previous store. The behavior of the RSIM memory unit in this situation depends on the configuration options. In the default RSIM configuration, the load is allowed to issue to the cache, if allowed by the multiprocessor ordering constraints. When the load data returns from the cache, the load will be allowed to complete unless there is still a prior store with an unknown or conflicting address. If a prior store is now known to have a conflicting address, the load must either attempt to reissue or forward a value from the store as appropriate. If a prior store still has an unknown address, the load remains in the memory unit, but clears the busy bit of its destination register, allowing further instructions to use the value of the load. However, if a prior store is later disambiguated and is found to conflict with a later completed load, the load is marked with a soft exception, which flushes the value of that load and all subsequent instructions. Soft-exception handling is discussed further.
There are two less aggressive variations provided on this default policy for handling the third case. The first scheme is similar to the default policy; however, the busy bit of the load is not cleared until all prior stores have completed. Thus, if a prior store is later found to have a conflicting address, the instruction must only be forced to reissue, rather than to take a soft exception. However, later instructions cannot use the value of the load until all prior stores have been disambiguated.
The second memory unit variation stalls the issue of the load altogether whenever a prior store has an unknown address.

Constraints for multiprocessor memory consistency model.
RSIM supports memory systems three types of multiprocessor memory consistency protocols:

• Relaxed memory ordering (RMO)  and release consistency (RC) 
• Sequential consistency (SC) 
• Processor consistency (PC)  and total store ordering (TSO) 

Each of these memory models is supported with a straightforward implementation and optimized implementations. We first describe the straightforward implementation and then the more optimized implementations for each of these models.
The relaxed memory ordering (RMO) model is based on the memory barrier (or fence) instructions, called MEMBARs, in the SPARC V9 ISA . Multiprocessor ordering constraints are imposed only with respect to these fence instructions. A SPARC V9 MEMBAR can specify one or more of several ordering options. An example of a commonly used class of MEMBAR is a LoadStore MEMBAR, which orders all loads (by program order) before the MEMBAR with respect to all stores following the MEMBAR (by program order). Other common forms of MEMBAR instructions include StoreStore, LoadLoad, and combinations of the above formed by bitwise or (e.g. LoadLoad|LoadStore). Instructions that are ordered by the above MEMBAR instructions must appear to execute in program order. Additionally, RSIM supports the MemIssue class of MEMBAR, which forces all previous memory accesses to have been globally performed before any later instructions can be initiated; this precludes the use of the optimized consistency implementations described below.
Release consistency is implemented using RMO with LoadLoad|LoadStore fences after acquire operations and LoadStore|StoreStore fences before release operations.
In the sequential consistency (SC) memory model, all operations must appear to execute in strictly serial order. The straightforward implementation of SC enforces this constraint by actually serializing all memory instructions; i.e. a load or store is issued to the cache only after the previous memory instruction by program order is globally performed . Further, stores in SC maintain their entries in the memory unit until they have globally performed to facilitate maintaining multiprocessor memory ordering dependences from stores to later instructions. Unless RSIM is invoked with the store buffering command line option , stores in SC do not graduate until they have globally performed. Forwarding of values from stores to loads inside the memory unit is not allowed in the straightforward implementation of sequential consistency. MEMBARs are ignored in the sequential consistency model.
The processor consistency (PC) and total-store ordering (TSO) implementations are identical in RSIM. With these models, stores are handled just as in sequential consistency with store buffering. Loads are ordered with respect to other loads, but are not prevented from issuing, leaving the memory unit, or graduating if only stores are ahead of them in the memory unit. Processor consistency and total store ordering also do not impose any multiprocessor constraints on forwarding values from stores to loads inside the memory unit, or on loads issuing past stores that have not yet disambiguated. MEMBARs are ignored under the processor consistency and total store ordering models.
Beyond the above straightforward implementations, the processor memory unit in RSIM also supports optimized implementations of memory consistency constraints. These implementations use two techniques to improve the performance of consistency implementations: hardware-controlled non-binding prefetching from the active list and speculative load execution .
In the straightforward implementations of memory consistency models, a load or store is prevented from issuing into the memory system whenever it has an outstanding consistency constraint from a prior instruction that has not yet been completed at the memory system. Hardware-controlled non-binding prefetching from the active list allows loads or stores in the active list that are blocked for consistency constraints to be prefetched into the processor's cache. As a result, the access is likely to expose less latency when it is issued to the caches after its consistency constraints have been met. This technique also allows exclusive prefetching of stores that have not yet reached the head of the active list (and which are thus prevented from issuing by the precise exception constraints).
Speculative load execution allows the processor not only to prefetch the cache lines for loads blocked for consistency constraints into the cache, but also to use the values in these prefetched lines. Values used in this fashion are correct as long as they are not overwritten by another processor before the load instruction completes its consistency constraints. The processor detects potential violations by monitoring coherence actions due to sharing or replacement at the cache. As in the MIPS R10000, a soft exception is marked on any speculative load for which such a coherence action occurs ; this soft exception will force the load to reissue and will flush subsequent instructions. The soft exception mechanism used on violations is the same as the mechanism used in the case of aggressive speculation of loads beyond stores with unknown addresses. Speculative load execution can be used in conjunction with hardware-controlled non-binding prefetching.

Boolean Algebra and Logic Circuits

Mathematical rules are based on the defining limits we place on the particular numerical quantities dealt with. When we say that 1 + 1 = 2 or 3 + 4 = 7, we are implying the use of integer quantities: the same types of numbers we all learned to count in elementary education. What most people assume to be self-evident rules of arithmetic -- valid at all times and for all purposes -- actually depend on what we define a number to be.
For instance, when calculating quantities in AC circuits, we find that the "real" number quantities which served us so well in DC circuit analysis are inadequate for the task of representing AC quantities. We know that voltages add when connected in series, but we also know that it is possible to connect a 3-volt AC source in series with a 4-volt AC source and end up with 5 volts total voltage (3 + 4 = 5)! Does this mean the inviolable and self-evident rules of arithmetic have been violated? No, it just means that the rules of "real" numbers do not apply to the kinds of quantities encountered in AC circuits, where every variable has both a magnitude and a phase. Consequently, we must use a different kind of numerical quantity, or object, for AC circuits (complex numbers, rather than real numbers), and along with this different system of numbers comes a different set of rules telling us how they relate to one another.
An expression such as "3 + 4 = 5" is nonsense within the scope and definition of real numbers, but it fits nicely within the scope and definition of complex numbers (think of a right triangle with opposite and adjacent sides of 3 and 4, with a hypotenuse of 5). Because complex numbers are two-dimensional, they are able to "add" with one another trigonometrically as single-dimension "real" numbers cannot.
Logic is much like mathematics in this respect: the so-called "Laws" of logic depend on how we define what a proposition is. The Greek philosopher Aristotle founded a system of logic based on only two types of propositions: true and false. His bivalent (two-mode) definition of truth led to the four foundational laws of logic: the Law of Identity (A is A); the Law of Non-contradiction (A is not non-A); the Law of the Excluded Middle (either A or non-A); and the Law of Rational Inference. These so-called Laws function within the scope of logic where a proposition is limited to one of two possible values, but may not apply in cases where propositions can hold values other than "true" or "false." In fact, much work has been done and continues to be done on "multivalued," or fuzzy logic, where propositions may be true or false to a limited degree. In such a system of logic, "Laws" such as the Law of the Excluded Middle simply do not apply, because they are founded on the assumption of bivalence. Likewise, many premises which would violate the Law of Non-contradiction in Aristotelian logic have validity in "fuzzy" logic. Again, the defining limits of propositional values determine the Laws describing their functions and relations.
The English mathematician George Boole (1815-1864) sought to give symbolic form to Aristotle's system of logic. Boole wrote a treatise on the subject in 1854, titled An Investigation of the Laws of Thought, on Which Are Founded the Mathematical Theories of Logic and Probabilities, which codified several rules of relationship between mathematical quantities limited to one of two possible values: true or false, 1 or 0. His mathematical system became known as Boolean algebra.
All arithmetic operations performed with Boolean quantities have but one of two possible outcomes: either 1 or 0. There is no such thing as "2" or "-1" or "1/2" in the Boolean world. It is a world in which all other possibilities are invalid by fiat. As one might guess, this is not the kind of math you want to use when balancing a checkbook or calculating current through a resistor. However, Claude Shannon of MIT fame recognized how Boolean algebra could be applied to on-and-off circuits, where all signals are characterized as either "high" (1) or "low" (0). His 1938 thesis, titled A Symbolic Analysis of Relay and Switching Circuits, put Boole's theoretical work to use in a way Boole never could have imagined, giving us a powerful mathematical tool for designing and analyzing digital circuits.
In this chapter, you will find a lot of similarities between Boolean algebra and "normal" algebra, the kind of algebra involving so-called real numbers. Just bear in mind that the system of numbers defining Boolean algebra is severely limited in terms of scope, and that there can only be one of two possible values for any Boolean variable: 1 or 0. Consequently, the "Laws" of Boolean algebra often differ from the "Laws" of real-number algebra, making possible such statements as 1 + 1 = 1, which would normally be considered absurd. Once you comprehend the premise of all quantities in Boolean algebra being limited to the two possibilities of 1 and 0, and the general philosophical principle of Laws depending on quantitative definitions, the "nonsense" of Boolean algebra disappears.
It should be clearly understood that Boolean numbers are not the same as binary numbers. Whereas Boolean numbers represent an entirely different system of mathematics from real numbers, binary is nothing more than an alternative notation for real numbers. The two are often confused because both Boolean math and binary notation use the same two ciphers: 1 and 0. The difference is that Boolean quantities are restricted to a single bit (either 1 or 0), whereas binary numbers may be composed of many bits adding up in place-weighted form to a value of any finite size. The binary number 10011₂ ("nineteen") has no more place in the Boolean world than the decimal number 2₁₀ ("two") or the octal number 32₈ ("twenty-six").

Let us begin our exploration of Boolean algebra by adding numbers together:

The first three sums make perfect sense to anyone familiar with elementary addition. The last sum, though, is quite possibly responsible for more confusion than any other single statement in digital electronics, because it seems to run contrary to the basic principles of mathematics. Well, it does contradict principles of addition for real numbers, but not for Boolean numbers. Remember that in the world of Boolean algebra, there are only two possible values for any quantity and for any arithmetic operation: 1 or 0. There is no such thing as "2" within the scope of Boolean values. Since the sum "1 + 1" certainly isn't 0, it must be 1 by process of elimination.
It does not matter how many or few terms we add together, either. Consider the following sums:

Take a close look at the two-term sums in the first set of equations. Does that pattern look familiar to you? It should! It is the same pattern of 1's and 0's as seen in the truth table for an OR gate. In other words, Boolean addition corresponds to the logical function of an "OR" gate, as well as to parallel switch contacts:







There is no such thing as subtraction in the realm of Boolean mathematics. Subtraction implies the existence of negative numbers: 5 - 3 is the same thing as 5 + (-3), and in Boolean algebra negative quantities are forbidden. There is no such thing as division in Boolean mathematics, either, since division is really nothing more than compounded subtraction, in the same way that multiplication is compounded addition.
Multiplication is valid in Boolean algebra, and thankfully it is the same as in real-number algebra: anything multiplied by 0 is 0, and anything multiplied by 1 remains unchanged:

This set of equations should also look familiar to you: it is the same pattern found in the truth table for an AND gate. In other words, Boolean multiplication corresponds to the logical function of an "AND" gate, as well as to series switch contacts:







Like "normal" algebra, Boolean algebra uses alphabetical letters to denote variables. Unlike "normal" algebra, though, Boolean variables are always CAPITAL letters, never lower-case. Because they are allowed to possess only one of two possible values, either 1 or 0, each and every variable has a complement: the opposite of its value. For example, if variable "A" has a value of 0, then the complement of A has a value of 1. Boolean notation uses a bar above the variable character to denote complementation, like this:

In written form, the complement of "A" denoted as "A-not" or "A-bar". Sometimes a "prime" symbol is used to represent complementation. For example, A' would be the complement of A, much the same as using a prime symbol to denote differentiation in calculus rather than the fractional notation d/dt. Usually, though, the "bar" symbol finds more widespread use than the "prime" symbol, for reasons that will become more apparent later in this chapter.
Boolean complementation finds equivalency in the form of the NOT gate, or a normally-closed switch or relay contact:



The basic definition of Boolean quantities has led to the simple rules of addition and multiplication, and has excluded both subtraction and division as valid arithmetic operations. We have a symbology for denoting Boolean variables, and their complements. In the next section we will proceed to develop Boolean identities.

• REVIEW:
• Boolean addition is equivalent to the OR logic function, as well as parallel switch contacts.
• Boolean multiplication is equivalent to the AND logic function, as well as series switch contacts.
• Boolean complementation is equivalent to the NOT logic function, as well as normally-closed relay contacts.

In mathematics, an identity is a statement true for all possible values of its variable or variables. The algebraic identity of x + 0 = x tells us that anything (x) added to zero equals the original "anything," no matter what value that "anything" (x) may be. Like ordinary algebra, Boolean algebra has its own unique identities based on the bivalent states of Boolean variables.
The first Boolean identity is that the sum of anything and zero is the same as the original "anything." This identity is no different from its real-number algebraic equivalent:

No matter what the value of A, the output will always be the same: when A=1, the output will also be 1; when A=0, the output will also be 0.
The next identity is most definitely different from any seen in normal algebra. Here we discover that the sum of anything and one is one:

No matter what the value of A, the sum of A and 1 will always be 1. In a sense, the "1" signal overrides the effect of A on the logic circuit, leaving the output fixed at a logic level of 1.
Next, we examine the effect of adding A and A together, which is the same as connecting both inputs of an OR gate to each other and activating them with the same signal:

In real-number algebra, the sum of two identical variables is twice the original variable's value (x + x = 2x), but remember that there is no concept of "2" in the world of Boolean math, only 1 and 0, so we cannot say that A + A = 2A. Thus, when we add a Boolean quantity to itself, the sum is equal to the original quantity: 0 + 0 = 0, and 1 + 1 = 1.
Introducing the uniquely Boolean concept of complementation into an additive identity, we find an interesting effect. Since there must be one "1" value between any variable and its complement, and since the sum of any Boolean quantity and 1 is 1, the sum of a variable and its complement must be 1:

Just as there are four Boolean additive identities (A+0, A+1, A+A, and A+A'), so there are also four multiplicative identities: Ax0, Ax1, AxA, and AxA'. Of these, the first two are no different from their equivalent expressions in regular algebra:



The third multiplicative identity expresses the result of a Boolean quantity multiplied by itself. In normal algebra, the product of a variable and itself is the square of that variable (3 x 3 = 3² = 9). However, the concept of "square" implies a quantity of 2, which has no meaning in Boolean algebra, so we cannot say that A x A = A². Instead, we find that the product of a Boolean quantity and itself is the original quantity, since 0 x 0 = 0 and 1 x 1 = 1:

The fourth multiplicative identity has no equivalent in regular algebra because it uses the complement of a variable, a concept unique to Boolean mathematics. Since there must be one "0" value between any variable and its complement, and since the product of any Boolean quantity and 0 is 0, the product of a variable and its complement must be 0:

To summarize, then, we have four basic Boolean identities for addition and four for multiplication:

Another identity having to do with complementation is that of the double complement: a variable inverted twice. Complementing a variable twice (or any even number of times) results in the original Boolean value. This is analogous to negating (multiplying by -1) in real-number algebra: an even number of negations cancel to leave the original value:

Another type of mathematical identity, called a "property" or a "law," describes how differing variables relate to each other in a system of numbers. One of these properties is known as the commutative property, and it applies equally to addition and multiplication. In essence, the commutative property tells us we can reverse the order of variables that are either added together or multiplied together without changing the truth of the expression:



Along with the commutative properties of addition and multiplication, we have the associative property, again applying equally well to addition and multiplication. This property tells us we can associate groups of added or multiplied variables together with parentheses without altering the truth of the equations.



Lastly, we have the distributive property, illustrating how to expand a Boolean expression formed by the product of a sum, and in reverse shows us how terms may be factored out of Boolean sums-of-products:

To summarize, here are the three basic properties: commutative, associative, and distributive.


Let's begin with a semiconductor gate circuit in need of simplification. The "A," "B," and "C" input signals are assumed to be provided from switches, sensors, or perhaps other gate circuits. Where these signals originate is of no concern in the task of gate reduction.

Our first step in simplification must be to write a Boolean expression for this circuit. This task is easily performed step by step if we start by writing sub-expressions at the output of each gate, corresponding to the respective input signals for each gate. Remember that OR gates are equivalent to Boolean addition, while AND gates are equivalent to Boolean multiplication. For example, I'll write sub-expressions at the outputs of the first three gates:

. . . then another sub-expression for the next gate:

Finally, the output ("Q") is seen to be equal to the expression AB + BC(B + C):

Now that we have a Boolean expression to work with, we need to apply the rules of Boolean algebra to reduce the expression to its simplest form (simplest defined as requiring the fewest gates to implement):

The final expression, B(A + C), is much simpler than the original, yet performs the same function. If you would like to verify this, you may generate a truth table for both expressions and determine Q's status (the circuits' output) for all eight logic-state combinations of A, B, and C, for both circuits. The two truth tables should be identical.
Now, we must generate a schematic diagram from this Boolean expression. To do this, evaluate the expression, following proper mathematical order of operations (multiplication before addition, operations inside parentheses before anything else), and draw gates for each step. Remember again that OR gates are equivalent to Boolean addition, while AND gates are equivalent to Boolean multiplication. In this case, we would begin with the sub-expression "A + C", which is an OR gate:

The next step in evaluating the expression "B(A + C)" is to multiply (AND gate) the signal B by the output of the previous gate (A + C):

Obviously, this circuit is much simpler than the original, having only two logic gates instead of five. Such component reduction results in higher operating speed (less delay time from input signal transition to output signal transition), less power consumption, less cost, and greater reliability.
Electromechanical relay circuits, typically being slower, consuming more electrical power to operate, costing more, and having a shorter average life than their semiconductor counterparts, benefit dramatically from Boolean simplification. Let's consider an example circuit:

As before, our first step in reducing this circuit to its simplest form must be to develop a Boolean expression from the schematic. The easiest way I've found to do this is to follow the same steps I'd normally follow to reduce a series-parallel resistor network to a single, total resistance. For example, examine the following resistor network with its resistors arranged in the same connection pattern as the relay contacts in the former circuit, and corresponding total resistance formula:

Remember that parallel contacts are equivalent to Boolean addition, while series contacts are equivalent to Boolean multiplication. Write a Boolean expression for this relay contact circuit, following the same order of precedence that you would follow in reducing a series-parallel resistor network to a total resistance. It may be helpful to write a Boolean sub-expression to the left of each ladder "rung," to help organize your expression-writing:

Now that we have a Boolean expression to work with, we need to apply the rules of Boolean algebra to reduce the expression to its simplest form (simplest defined as requiring the fewest relay contacts to implement):

The more mathematically inclined should be able to see that the two steps employing the rule "A + AB = A" may be combined into a single step, the rule being expandable to: "A + AB + AC + AD + . . . = A"

As you can see, the reduced circuit is much simpler than the original, yet performs the same logical function:

• REVIEW:
• To convert a gate circuit to a Boolean expression, label each gate output with a Boolean sub-expression corresponding to the gates' input signals, until a final expression is reached at the last gate.
• To convert a Boolean expression to a gate circuit, evaluate the expression using standard order of operations: multiplication before addition, and operations within parentheses before anything else.
• To convert a ladder logic circuit to a Boolean expression, label each rung with a Boolean sub-expression corresponding to the contacts' input signals, until a final expression is reached at the last coil or light. To determine proper order of evaluation, treat the contacts as though they were resistors, and as if you were determining total resistance of the series-parallel network formed by them. In other words, look for contacts that are either directly in series or directly in parallel with each other first, then "collapse" them into equivalent Boolean sub-expressions before proceeding to other contacts.
• To convert a Boolean expression to a ladder logic circuit, evaluate the expression using standard order of operations: multiplication before addition, and operations within parentheses before anything else.

Computer Codes

In computer science, source code is any collection of statements or declarations written in some human-readable computer programming language. Source code is the means most often used by programmers to specify the actions to be performed by a computer.
The source code which constitutes a program is usually held in one or more text files, sometimes stored in databases as stored procedures and may also appear as code snippets printed in books or other media. A large collection of source code files may be organized into a directory tree, in which case it may also be known as a source tree.
A computer program's source code is the collection of files needed to convert from human-readable form to some kind of computer-executable form. The source code may be converted into an executable file by a compiler, or executed on the fly from the human readable form with the aid of an interpreter.
The annual working conference Source Code Analysis and Manipulation defines source code as

"For the purpose of clarity ‘source code’ is taken to mean any fully executable description of a software system. It is therefore so construed as to include machine code, very high level languages and executable graphical representations of systems. "

The code base of a programming project is the larger collection of all the source code of all the computer programs which make up the project.

Organization
The source code for a particular piece of software may be contained in a single file or many files. Though the practice is uncommon, a program's source code can be written in different programming languages.For example, a program written primarily in the C programming language, might have portions written in assembly language for optimization purposes. It is also possible for some components of a piece of software to be written and compiled separately, in an arbitrary programming language, and later integrated into the software using a technique called library linking. This is the case in some languages, such as Java: each class is compiled separately into a file and linked by the interpreter at runtime.
Yet another method is to make the main program an interpreter for a programming language, either designed specifically for the application in question or general-purpose, and then write the bulk of the actual user functionality as macros or other forms of add-ins in this language, an approach taken for example by the GNU Emacs text editor.
Moderately complex software customarily requires the compilation or assembly of several, sometimes dozens or even hundreds, of different source code files. In these cases, instructions for compilations, such as a Makefile, are included with the source code. These describe the relationships among the source code files, and contain information about how they are to be compiled.
The revision control system is another tool frequently used by developers for source code maintenance.

Purposes

Source code is primarily used as input to the process that produces an executable program (i.e., it is compiled or interpreted). It is also used as a method of communicating algorithms between people (e.g., code snippets in books).
Programmers often find it helpful to review existing source code to learn about programming techniques. The sharing of source code between developers is frequently cited as a contributing factor to the maturation of their programming skills. Some people consider source code an expressive artistic medium.
Porting software to other computer platforms is usually prohibitively difficult without source code. Without the source code for a particular piece of software, portability is generally computationally expensive. Possible porting options include binary translation and emulation of the original platform.
Decompilation of an executable program can be used to generate source code, either in assembly code or in a high level language.
Programmers frequently adapt source code from one piece of software to use in other projects, a concept known as software reusability.

 Licensing

Software, and its accompanying source code, typically falls within one of two licensing paradigms: free software and proprietary software.
Generally speaking, software is free if the source code is free to use, distribute, modify and study, and proprietary if the source code is kept secret, or is privately owned and restricted. Note that "free" refers to freedom, not price. Under many licenses it is acceptable to charge for "free software". The first free software license to be published and to explicitly grant these freedoms was the GNU General Public License in 1989. The GNU GPL was originally intended to be used with the GNU operating system. The GNU GPL was later adopted by other non-GNU software projects such as the Linux kernel.
For proprietary software, the provisions of the various copyright laws, trade secrecy and patents are used to keep the source code closed. Additionally, many pieces of retail software come with an end-user license agreement (EULA) which typically prohibits decompilation, reverse engineering, analysis, modification, or circumventing of copy protection. Types of source code protection — beyond traditional compilation to object code — include code encryption, code obfuscation or code morphing.

Legal issues in the United States

In a 2003 court case in the United States, it was ruled that source code should be considered a constitutionally protected form of free speech. Proponents of free speech argued that because source code conveys information to programmers, is written in a language, and can be used to share humour and other artistic pursuits, it is a protected form of communication.
One of the first court cases regarding the nature of source code as free speech involved University of California mathematics professor Dan Bernstein, who had published on the internet the source code for an encryption program that he created. At the time, encryption algorithms were classified as munitions by the United States government; exporting encryption to other countries was considered an issue of national security, and had to be approved by the State Department. The Electronic Frontier Foundation sued the U.S. government on Bernstein's behalf; the court ruled that source code was free speech, protected by the First Amendment.

Quality

The way a program is written can have important consequences for its maintainers. Coding conventions, which stress readability and some language-specific conventions, are aimed at the maintenance of the software source code, which involves debugging and updating. Other priorities, such as the speed of the programs execution, or the ability to compile the program for multiple architectures, often make code readability a less important consideration, since code quality depends entirely on its purpose.