Niklaus Emil Wirth is a Swiss computer scientist. He has designed several programming languages, including Pascal, pioneered several classic topics in software engineering. In 1984 he won the Turing Award recognized as the highest distinction in computer science, for developing a sequence of innovative computer languages. Wirth was born in Winterthur, Switzerland, in 1934. In 1959 he earned a degree in Electronics Engineering from the Swiss Federal Institute of Technology Zürich. In 1960 he earned an M. Sc. from Université Laval, Canada. In 1963 he was awarded a Ph. D. in Electrical Engineering and Computer Science from the University of California, supervised by the computer designer pioneer Harry Huskey. From 1963 to 1967 he served as assistant professor of Computer Science at Stanford University and again at the University of Zurich. In 1968 he became Professor of Informatics at ETH Zürich, taking two one-year sabbaticals at Xerox PARC in California. Wirth retired in 1999. In 2004, he was made a Fellow of the Computer History Museum "for seminal work in programming languages and algorithms, including Euler, Algol-W, Pascal and Oberon."
Wirth was the chief designer of the programming languages Euler, Algol W, Modula, Modula-2, Oberon-2, Oberon-07. He was a major part of the design and implementation team for the Lilith and Oberon operating systems, for the Lola digital hardware design and simulation system, he received the Association for Computing Machinery Turing Award for the development of these languages in 1984 and in 1994 he was inducted as a Fellow of the ACM. His book, written jointly with Kathleen Jensen, The Pascal User Manual and Report, served as the basis of many language implementation efforts in the 1970s and 1980s in the United States and across Europe, his article Program Development by Stepwise Refinement, about the teaching of programming, is considered to be a classic text in software engineering. In 1975 he wrote the book Algorithms + Data Structures = Programs. Major revisions of this book with the new title Algorithms + Data Structures were published in 1985 and 2004; the examples in the first edition were written in Pascal.
These were replaced in the editions with examples written in Modula-2 and Oberon respectively. His textbook, Systematic Programming: An Introduction, was considered a good source for students who wanted to do more than just coding. Regarded as a challenging text to work through, it was sought as imperative reading for those interested in numerical mathematics. In 1992 he published the full documentation of the Oberon OS.. A second book was intended as a programmer's guide. In 1995, he popularized the adage now known as Wirth's law, which states that software is getting slower more than hardware becomes faster. In his 1995 paper A Plea for Lean Software he attributes it to Martin Reiser. Asteroid 21655 Niklauswirth Extended Backus–Naur Form Wirth syntax notation Bucky bit Wirth–Weber precedence relationship List of pioneers in computer science Biography at ETH Zürich. Personal home page at ETH Zürich. Niklaus Wirth at DBLP Bibliography Server Niklaus E. Wirth at ACM. Wirth, Niklaus. "Program Development by Stepwise Refinement".
Communications of the ACM. 14: 221–7. Doi:10.1145/362575.362577. Wirth, N.. "On the Design of Programming Languages". Proc. IFIP Congress 74: 386–393. Turing Award Lecture, 1984 Pascal and its Successors paper by Niklaus Wirth – includes short biography. A Few Words with Niklaus Wirth The School of Niklaus Wirth: The Art of Simplicity, by László Böszörményi, Jürg Gutknecht, Gustav Pomberger. Dpunkt.verlag / Morgan Kaufmann Publishers, 2000. ISBN 3-932588-85-1 / ISBN 1-55860-723-4; the book Compiler Construction The book Algorithms and Data Structures The book Project Oberon – The Design of an Operating System and Compiler. The book about the Oberon language and Operating System is now available as a PDF file; the PDF file has an additional appendix Ten Years After: From Objects to Components. Project Oberon 2013
In computer science, control flow is the order in which individual statements, instructions or function calls of an imperative program are executed or evaluated. The emphasis on explicit control flow distinguishes an imperative programming language from a declarative programming language. Within an imperative programming language, a control flow statement is a statement, the execution of which results in a choice being made as to which of two or more paths to follow. For non-strict functional languages and language constructs exist to achieve the same result, but they are not termed control flow statements. A set of statements is in turn structured as a block, which in addition to grouping defines a lexical scope. Interrupts and signals are low-level mechanisms that can alter the flow of control in a way similar to a subroutine, but occur as a response to some external stimulus or event, rather than execution of an in-line control flow statement. At the level of machine language or assembly language, control flow instructions work by altering the program counter.
For some central processing units, the only control flow instructions available are conditional or unconditional branch instructions termed jumps. The kinds of control flow statements supported by different languages vary, but can be categorized by their effect: Continuation at a different statement Executing a set of statements only if some condition is met Executing a set of statements zero or more times, until some condition is met Executing a set of distant statements, after which the flow of control returns Stopping the program, preventing any further execution A label is an explicit name or number assigned to a fixed position within the source code, which may be referenced by control flow statements appearing elsewhere in the source code. A label marks a position within source code, has no other effect. Line numbers are an alternative to a named label, that are whole numbers placed at the start of each line of text in the source code. Languages which use these impose the constraint that the line numbers must increase in value in each following line, but may not require that they be consecutive.
For example, in BASIC: In other languages such as C and Ada, a label is an identifier appearing at the start of a line and followed by a colon. For example, in C: The language ALGOL 60 allowed both whole numbers and identifiers as labels, but few if any other ALGOL variants allowed whole numbers. Early Fortran compilers only allowed whole numbers as labels. Beginning with Fortran-90, alphanumeric labels have been allowed; the goto statement is the most basic form of unconditional transfer of control. Although the keyword may either be in upper or lower case depending on the language, it is written as: goto label The effect of a goto statement is to cause the next statement to be executed to be the statement appearing at the indicated label. Goto statements have been considered harmful by many computer scientists, notably Dijkstra; the terminology for subroutines varies. In the 1950s, computer memories were small by current standards so subroutines were used to reduce program size. A piece of code was written once and used many times from various other places in a program.
Today, subroutines are more used to help make a program more structured, e.g. by isolating some algorithm or hiding some data access method. If many programmers are working on one program, subroutines are one kind of modularity that can help divide the work. In structured programming, the ordered sequencing of successive commands is considered one of the basic control structures, used as a building block for programs alongside iteration and choice. In May 1966, Böhm and Jacopini published an article in Communications of the ACM which showed that any program with gotos could be transformed into a goto-free form involving only choice and loops with duplicated code and/or the addition of Boolean variables. Authors showed that choice can be replaced by loops; that such minimalism is possible does not mean that it is desirable. What Böhm and Jacopini's article showed was that all programs could be goto-free. Other research showed that control structures with one entry and one exit were much easier to understand than any other form because they could be used anywhere as a statement without disrupting the control flow.
In other words, they were composable. Some academics took a purist approach to the Böhm-Jacopini result and argued that instructions like break and return from the middle of loops are bad practice as they are not needed in the Böhm-Jacopini proof, th
In computing, a process is the instance of a computer program, being executed. It contains its activity. Depending on the operating system, a process may be made up of multiple threads of execution that execute instructions concurrently. While a computer program is a passive collection of instructions, a process is the actual execution of those instructions. Several processes may be associated with the same program. Multitasking is a method to allow multiple processes to other system resources; each CPU executes a single task at a time. However, multitasking allows each processor to switch between tasks that are being executed without having to wait for each task to finish. Depending on the operating system implementation, switches could be performed when tasks perform input/output operations, when a task indicates that it can be switched, or on hardware interrupts. A common form of multitasking is time-sharing. Time-sharing is a method to allow high responsiveness for interactive user applications.
In time-sharing systems, context switches are performed which makes it seem like multiple processes are being executed on the same processor. This seeming execution of multiple processes is called concurrency. For security and reliability, most modern operating systems prevent direct communication between independent processes, providing mediated and controlled inter-process communication functionality. In general, a computer system process consists of the following resources: An image of the executable machine code associated with a program. Memory. Operating system descriptors of resources that are allocated to the process, such as file descriptors or handles, data sources and sinks. Security attributes, such as the process' set of permissions. Processor state, such as the content of registers and physical memory addressing; the state is stored in computer registers when the process is executing, in memory otherwise. The operating system holds most of this information about active processes in data structures called process control blocks.
Any subset of the resources at least the processor state, may be associated with each of the process' threads in operating systems that support threads or child processes. The operating system keeps its processes separate and allocates the resources they need, so that they are less to interfere with each other and cause system failures; the operating system may provide mechanisms for inter-process communication to enable processes to interact in safe and predictable ways. A multitasking operating system may just switch between processes to give the appearance of many processes executing though in fact only one process can be executing at any one time on a single CPU, it is usual to associate a single process with a main program, child processes with any spin-off, parallel processes, which behave like asynchronous subroutines. A process is said to own resources. However, in multiprocessing systems many processes may run off of, or share, the same reentrant program at the same location in memory, but each process is said to own its own image of the program.
Processes are called "tasks" in embedded operating systems. The sense of "process" is "something that takes up time", as opposed to "memory", "something that takes up space"; the above description applies to both processes managed by an operating system, processes as defined by process calculi. If a process requests something for which it must wait, it will be blocked; when the process is in the blocked state, it is eligible for swapping to disk, but this is transparent in a virtual memory system, where regions of a process's memory may be on disk and not in main memory at any time. Note that portions of active processes/tasks are eligible for swapping to disk, if the portions have not been used recently. Not all parts of an executing program and its data have to be in physical memory for the associated process to be active. An operating system kernel that allows multitasking needs processes to have certain states. Names for these states are not standardised. First, the process is "created" by being loaded from a secondary storage device into main memory.
After that the process scheduler assigns it the "waiting" state. While the process is "waiting", it waits for the scheduler to do a so-called context switch and load the process into the processor; the process state becomes "running", the processor executes the process instructions. If a process needs to wait for a resource, it is assigned the "blocked" state; the process state is changed back to "waiting". Once the process finishes execution, or is terminated by the operating system, it is no longer needed; the process is removed or is moved to the "terminated" state. When removed, it just waits to be removed from main
A regular expression, regex or regexp is a sequence of characters that define a search pattern. This pattern is used by string searching algorithms for "find" or "find and replace" operations on strings, or for input validation, it is a technique developed in formal language theory. The concept arose in the 1950s when the American mathematician Stephen Cole Kleene formalized the description of a regular language; the concept came into common use with Unix text-processing utilities. Since the 1980s, different syntaxes for writing regular expressions exist, one being the POSIX standard and another used, being the Perl syntax. Regular expressions are used in search engines and replace dialogs of word processors and text editors, in text processing utilities such as sed and AWK and in lexical analysis. Many programming languages provide regex capabilities, built-in or via libraries; the phrase regular expressions, regexes, is used to mean the specific, standard textual syntax for representing patterns for matching text.
Each character in a regular expression is either a metacharacter, having a special meaning, or a regular character that has a literal meaning. For example, in the regex a. A is a literal character which matches just'a', while'.' is a meta character that matches every character except a newline. Therefore, this regex matches, for example,'a', or'ax', or'a0'. Together and literal characters can be used to identify text of a given pattern, or process a number of instances of it. Pattern matches may vary from a precise equality to a general similarity, as controlled by the metacharacters. For example. Is a general pattern, is less general and a is a precise pattern; the metacharacter syntax is designed to represent prescribed targets in a concise and flexible way to direct the automation of text processing of a variety of input data, in a form easy to type using a standard ASCII keyboard. A simple case of a regular expression in this syntax is to locate a word spelled two different ways in a text editor, the regular expression serialie matches both "serialise" and "serialize".
Wildcards achieve this, but are more limited in what they can pattern, as they have fewer metacharacters and a simple language-base. The usual context of wildcard characters is in globbing similar names in a list of files, whereas regexes are employed in applications that pattern-match text strings in general. For example, the regex ^+|+$ matches excess whitespace at the beginning or end of a line. An advanced regular expression that matches any numeral is??. A regex processor translates a regular expression in the above syntax into an internal representation which can be executed and matched against a string representing the text being searched in. One possible approach is the Thompson's construction algorithm to construct a nondeterministic finite automaton, made deterministic and the resulting deterministic finite automaton is run on the target text string to recognize substrings that match the regular expression; the picture shows the NFA scheme N obtained from the regular expression s*, where s denotes a simpler regular expression in turn, recursively translated to the NFA N.
Regular expressions originated in 1951, when mathematician Stephen Cole Kleene described regular languages using his mathematical notation called regular sets. These arose in theoretical computer science, in the subfields of automata theory and the description and classification of formal languages. Other early implementations of pattern matching include the SNOBOL language, which did not use regular expressions, but instead its own pattern matching constructs. Regular expressions entered popular use from 1968 in two uses: pattern matching in a text editor and lexical analysis in a compiler. Among the first appearances of regular expressions in program form was when Ken Thompson built Kleene's notation into the editor QED as a means to match patterns in text files. For speed, Thompson implemented regular expression matching by just-in-time compilation to IBM 7094 code on the Compatible Time-Sharing System, an important early example of JIT compilation, he added this capability to the Unix editor ed, which led to the popular search tool grep's use of regular expressions.
Around the same time when Thompson developed QED, a group of researchers including Douglas T. Ross implemented a tool based on regular expressions, used for lexical analysis in compiler design. Many variations of these original forms of regular expressions were used in Unix programs at Bell Labs in the 1970s, including vi, sed, AWK, expr, in other programs such as Emacs. Regexes were subsequently adopted by a wide range of programs, with these early forms standardized in the POSIX.2 standard in 1992. In the 1980s the more complicated regexes arose in Perl, which derived from a regex library written by Henry Spencer, who wrote an implementation of Advanced Regular Expressions for Tcl; the Tcl library is a hybrid NFA/DFA implementation with improved performance characteristics. Software projects that have adopted Spencer's Tcl regular expression implementation include PostgreSQL. Perl expanded on Spencer's original library
Data structure diagram
Data structure diagram is a diagram of the conceptual data model which documents the entities and their relationships, as well as the constraints that connect to them. The basic graphic notation elements of DSDs are boxes; the arrow symbol represents relationships. Data structure diagrams are most useful for documenting complex data entities. Data Structure Diagram is a diagram type, used to depict the structure of data elements in the data dictionary; the data structure diagram is a graphical alternative to the composition specifications within such data dictionary entries. The data structure diagrams is a predecessor of the entity-relationship model. In DSDs, attributes are specified inside the entity boxes rather than outside of them, while relationships are drawn as boxes composed of attributes which specify the constraints that bind entities together. DSDs differ from the E-R model in that the E-R model focuses on the relationships between different entities, whereas DSDs focus on the relationships of the elements within an entity.
There are several styles for representing data structure diagrams, with the notable difference in the manner of defining cardinality. The choices are between arrow heads, inverted arrow heads, or numerical representation of the cardinality. A Bachman diagram is a certain type of data structure diagram, is used to design the data with a network or relational "logical" model, separating the data model from the way the data is stored in the system; the model is named after database pioneer Charles Bachman, used in computer software design. In a relational model, a relation is the cohesion of attributes that are and not transitive functional dependent of every key in that relation; the coupling between the relations is based on accordant attributes. For every relation, a rectangle has to be drawn and every coupling is illustrated by a line that connects the relations. On the edge of each line, arrows indicate the cardinality. We have 1-to-1 and n-to-n; the latter must be replaced by two 1-to-n couplings.
Data flow diagram Entity-relationship diagram Unified Modeling Language Charles W. Bachman. Data structure diagrams. Data Base, 1969, 1:4–10. Tom DeMarco. Structured Analysis and System Specification. ISBN 0-13-854380-1. Prentice Hall. 11 May 1979. Edward Yourdon. Modern Structured Analysis. ISBN 0-13-598624-9. Prentice Hall. 1 August 1988.
Edsger W. Dijkstra
Edsger Wybe Dijkstra was a Dutch systems scientist, software engineer, science essayist, pioneer in computing science. A theoretical physicist by training, he worked as a programmer at the Mathematisch Centrum from 1952 to 1962. A university professor for much of his life, Dijkstra held the Schlumberger Centennial Chair in Computer Sciences at the University of Texas at Austin from 1984 until his retirement in 1999, he was a professor of mathematics at the Eindhoven University of Technology and a research fellow at the Burroughs Corporation. One of the most influential figures of computing science's founding generation, Dijkstra helped shape the new discipline from both an engineering and a theoretical perspective, his fundamental contributions cover diverse areas of computing science, including compiler construction, operating systems, distributed systems and concurrent programming, programming paradigm and methodology, programming language research, program design, program development, program verification, software engineering principles, graph algorithms, philosophical foundations of computer programming and computer science.
Many of his papers are the source of new research areas. Several concepts and problems that are now standard in computer science were first identified by Dijkstra or bear names coined by him; as a foremost opponent of the mechanizing view of computing science, he refuted the use of the concepts of'computer science' and'software engineering' as umbrella terms for academic disciplines. Until the mid-1960s computer programming was considered more an art than a scientific discipline. In Harlan Mills's words, "programming was regarded as a private, puzzle-solving activity of writing computer instructions to work as a program". In the late 1960s, computer programming was in a state of crisis. Dijkstra was one of a small group of academics and industrial programmers who advocated a new programming style to improve the quality of programs. Dijkstra, who had a background in mathematics and physics, was one of the driving forces behind the acceptance of computer programming as a scientific discipline, he coined the phrase "structured programming" and during the 1970s this became the new programming orthodoxy.
His ideas about structured programming helped lay the foundations for the birth and development of the professional discipline of software engineering, enabling programmers to organize and manage complex software projects. As Bertrand Meyer noted, "The revolution in views of programming started by Dijkstra's iconoclasm led to a movement known as structured programming, which advocated a systematic, rational approach to program construction. Structured programming is the basis for all, done since in programming methodology, including object-oriented programming."The academic study of concurrent computing started in the 1960s, with Dijkstra credited with being the first paper in this field and solving the mutual exclusion problem. He was one of the early pioneers of the research on principles of distributed computing, his foundational work on concurrency, mutual exclusion, finding shortest paths in graphs, fault-tolerance, self-stabilization, among many other contributions comprises many of the pillars upon which the field of distributed computing is built.
Shortly before his death in 2002, he received the ACM PODC Influential-Paper Award in distributed computing for his work on self-stabilization of program computation. This annual award was renamed the Dijkstra Prize the following year, in his honor; as the prize, sponsored jointly by the ACM Symposium on Principles of Distributed Computing and the EATCS International Symposium on Distributed Computing, recognizes that "No other individual has had a larger influence on research in principles of distributed computing". Edsger W. Dijkstra was born in Rotterdam, his father was a chemist, president of the Dutch Chemical Society. His mother was a mathematician, but never had a formal job. Dijkstra had considered a career in law and had hoped to represent the Netherlands in the United Nations. However, after graduating from school in 1948, at his parents' suggestion he studied mathematics and physics and theoretical physics at the University of Leiden. In the early 1950s, electronic computers were a novelty.
Dijkstra stumbled on his career quite by accident, through his supervisor, Professor A. Haantjes, he met Adriaan van Wijngaarden, the director of the Computation Department at the Mathematical Center in Amsterdam, who offered Dijkstra a job. For some time Dijkstra remained committed to physics, working on it in Leiden three days out of each week. With increasing exposure to computing, his focus began to shift; as he recalled: After having programmed for some three years, I had a discussion with A. van Wijngaarden, my boss at the Mathematical Center in Amsterdam, a discussion for which I shall remain grateful to him as long as I live. The point was that I was supposed to study theoretical physics at the University of Leiden and as I found the two activities harder and harder to combine, I had to make up my mind, either to stop programming and become a real, respectable theoretical physicist, or to carry my study of physics to a formal completion only, with a minimum of effort, to become.....
Yes what? A programmer? But was that a respectable profession? For after all, what was
COBOL is a compiled English-like computer programming language designed for business use. It is procedural and, since 2002, object-oriented. COBOL is used in business and administrative systems for companies and governments. COBOL is still used in legacy applications deployed on mainframe computers, such as large-scale batch and transaction processing jobs, but due to its declining popularity and the retirement of experienced COBOL programmers, programs are being migrated to new platforms, rewritten in modern languages or replaced with software packages. Most programming in COBOL is now purely to maintain existing applications. COBOL was designed in 1959 by CODASYL and was based on previous programming language design work by Grace Hopper referred to as "the mother of COBOL", it was created as part of a US Department of Defense effort to create a portable programming language for data processing. It was seen as a stopgap, but the Department of Defense promptly forced computer manufacturers to provide it, resulting in its widespread adoption.
It has since been revised four times. Expansions include support for object-oriented programming; the current standard is ISO/IEC 1989:2014. COBOL statements have an English-like syntax, designed to be self-documenting and readable. However, it uses over 300 reserved words. In contrast with modern, succinct syntax like y = x. COBOL code is split into four divisions containing a rigid hierarchy of sections and sentences. Lacking a large standard library, the standard specifies 43 statements, 87 functions and just one class. Academic computer scientists were uninterested in business applications when COBOL was created and were not involved in its design. COBOL has been criticized throughout its life, for its verbosity, design process, poor support for structured programming; these weaknesses result in monolithic and, though intended to be English-like, not comprehensible and verbose programs. In the late 1950s, computer users and manufacturers were becoming concerned about the rising cost of programming.
A 1959 survey had found that in any data processing installation, the programming cost US$800,000 on average and that translating programs to run on new hardware would cost $600,000. At a time when new programming languages were proliferating at an ever-increasing rate, the same survey suggested that if a common business-oriented language were used, conversion would be far cheaper and faster. On 8 April 1959, Mary K. Hawes, a computer scientist at Burroughs Corporation, called a meeting of representatives from academia, computer users, manufacturers at the University of Pennsylvania to organize a formal meeting on common business languages. Representatives included Grace Hopper, inventor of the English-like data processing language FLOW-MATIC, Jean Sammet and Saul Gorn. At the April meeting, the group asked the Department of Defense to sponsor an effort to create a common business language; the delegation impressed Charles A. Phillips, director of the Data System Research Staff at the DoD, who thought that they "thoroughly understood" the DoD's problems.
The DoD operated 225 computers, had a further 175 on order and had spent over $200 million on implementing programs to run on them. Portable programs would save time, reduce costs and ease modernization. Phillips tasked the delegation with drafting the agenda. On 28 and 29 May 1959, a meeting was held at the Pentagon to discuss the creation of a common programming language for business, it was chaired by Phillips. The Department of Defense was concerned about whether it could run the same data processing programs on different computers. FORTRAN, the only mainstream language at the time, lacked the features needed to write such programs. Representatives enthusiastically described a language that could work in a wide variety of environments, from banking and insurance to utilities and inventory control, they agreed unanimously that more people should be able to program and that the new language should not be restricted by the limitations of contemporary technology. A majority agreed that the language should make maximal use of English, be capable of change, be machine-independent and be easy to use at the expense of power.
The meeting resulted in the creation of a steering committee and short-, intermediate- and long-range committees. The short-range committee was given to September to produce specifications for an interim language, which would be improved upon by the other committees, their official mission, was to identify the strengths and weaknesses of existing programming languages and did not explicitly direct them to create a new language. The deadline was met with disbelief by the short-range committee. One member, Betty Holberton, described the three-month deadline as "gross optimism" and doubted that the language would be a stopgap; the steering committee met on 4 June and agreed to name the entire activity as the Committee on Data Systems Languages, or CODASYL, to form an executive committee. The short-range committee was made up of members representing six computer manufacturers and three government agencies; the six computer manufacturers were Burroughs Corporation, IBM, Minneapolis-Honeywell (Honeywell