A number of computer scientists have argued for the distinction of three separate paradigms in computer science.
Peter Wegner argued that those paradigms are science, technology, and mathematics.
[16] Peter Denning's working group argued that they are theory, abstraction (modeling), and design.
[17] Amnon H. Eden described them as the "rationalist paradigm" (which treats computer science as branch of mathematics, which is prevalent in theoretical computer science, and mainly employs
deductive reasoning), the "technocratic paradigm" (which might be found in
engineeringapproaches, most prominently in software engineering), and the "scientific paradigm" (which approaches computer-related artifacts from the empirical perspective of
natural sciences, identifiable in some branches of artificial intelligence).
[18][edit]Name of the field
The term "computer science" was first coined by the
numerical analyst George Forsythe in 1961.
[19] Despite its name, a significant amount of computer science does not involve the study of computers themselves. Because of this, several alternative names have been proposed. Certain departments of major universities prefer the term
computing science, to emphasize precisely that difference. Danish scientist
Peter Naur suggested the term
datalogy, to reflect the fact that the scientific discipline revolves around data and data treatment, while not necessarily involving computers. The first scientific institution to use the term was the Department of Datalogy at the University of Copenhagen, founded in 1969, with Peter Naur being the first professor in datalogy. The term is used mainly in the Scandinavian countries. Also, in the early days of computing, a number of terms for the practitioners of the field of computing were suggested in the
Communications of the ACM –
turingineer,
turologist,
flow-charts-man,
applied meta-mathematician, and
applied epistemologist.
[20] Three months later in the same journal,
comptologist was suggested, followed next year by
hypologist.
[21] The term
computics has also been suggested.
[22] In Europe, terms derived from contracted translations of the expression "automatic information" (e.g. "informazione automatica" in Italian) or "information and mathematics" are often used, e.g.
informatique (French),
Informatik (German),
informatica (Italy),
informática (Spain, Portugal) or
informatika (
Slavic languages) are also used and have also been adopted in the UK (as in
the School of Informatics of the University of Edinburgh).
[23]A folkloric quotation, often attributed to—but almost certainly not first formulated by—
Edsger Dijkstra, states that "computer science is no more about computers than astronomy is about telescopes."
[note 1] The design and deployment of computers and computer systems is generally considered the province of disciplines other than computer science. For example, the study of
computer hardware is usually considered part of
computer engineering, while the study of commercial
computer systems and their deployment is often called
information technology or
information systems. However, there has been much cross-fertilization of ideas between the various computer-related disciplines. Computer science research also often intersects other disciplines, such as
philosophy,
cognitive science,
linguistics,
mathematics,
physics,
statistics, and
logic.
Computer science is considered by some to have a much closer relationship with mathematics than many scientific disciplines, with some observers saying that computing is a mathematical science.
[6] Early computer science was strongly influenced by the work of mathematicians such as
Kurt Gödel and
Alan Turing, and there continues to be a useful interchange of ideas between the two fields in areas such as
mathematical logic,
category theory,
domain theory, and
algebra.
The relationship between computer science and
software engineering is a contentious issue, which is further muddied by
disputes over what the term "software engineering" means, and how computer science is defined.
[24] David Parnas, taking a cue from the relationship between other engineering and science disciplines, has claimed that the principal focus of computer science is studying the properties of computation in general, while the principal focus of software engineering is the design of specific computations to achieve practical goals, making the two separate but complementary disciplines.
[25]The academic, political, and funding aspects of computer science tend to depend on whether a department formed with a mathematical emphasis or with an engineering emphasis. Computer science departments with a mathematics emphasis and with a numerical orientation consider alignment with
computational science. Both types of departments tend to make efforts to bridge the field educationally if not across all research.
[edit]Areas of computer science
As a discipline, computer science spans a range of topics from theoretical studies of algorithms and the limits of computation to the practical issues of implementing computing systems in hardware and software.
[26][27] CSAB, formerly called
Computing Sciences Accreditation Board – which is made up of representatives of the
Association for Computing Machinery (ACM), and the
IEEE Computer Society (IEEE-CS)
[28] – identifies four areas that it considers crucial to the discipline of computer science:
theory of computation,
algorithms and data structures,
programming methodology and languages, and
computer elements and architecture. In addition to these four areas, CSAB also identifies fields such as software engineering, artificial intelligence, computer networking and communication, database systems, parallel computation, distributed computation, computer-human interaction, computer graphics, operating systems, and numerical and symbolic computation as being important areas of computer science.
[26][edit]Theoretical computer science
The broader field of
theoretical computer science encompasses both the classical theory of computation and a wide range of other topics that focus on the more abstract, logical, and mathematical aspects of computing.
[edit]Theory of computation
According to
Peter J. Denning, the fundamental question underlying computer science is,
"What can be (efficiently) automated?"[6] The study of the
theory of computation is focused on answering fundamental questions about what can be computed and what amount of resources are required to perform those computations. In an effort to answer the first question,
computability theoryexamines which computational problems are solvable on various theoretical
models of computation. The second question is addressed by
computational complexity theory, which studies the time and space costs associated with different approaches to solving a multitude of computational problems.
[edit]Information and coding theory
[edit]Algorithms and data structures
[edit]Programming language theory
Programming language theory (PLT) is a branch of computer science that deals with the design, implementation, analysis, characterization, and classification of
programming languages and their individual
features. It falls within the discipline of computer science, both depending on and affecting
mathematics,
software engineering and
linguistics. It is an active research area, with numerous dedicated academic journals.
[edit]Formal methods
Main article:
Formal methodsFormal methods are a particular kind of
mathematically based technique for the
specification, development and
verification of
software and
hardware systems. The use of formal methods for software and hardware design is motivated by the expectation that, as in other engineering disciplines, performing appropriate mathematical analysis can contribute to the reliability and robustness of a design. However, the high cost of using formal methods means that they are usually only used in the development of high-integrity and
life-critical systems, where
safety or
security is of utmost importance. Formal methods are best described as the application of a fairly broad variety of
theoretical computer science fundamentals, in particular
logic calculi,
formal languages,
automata theory, and
program semantics, but also
type systems and
algebraic data types to problems in software and hardware specification and verification.
[edit]Concurrent, parallel and distributed systems
Concurrency is a property of systems in which several computations are executing simultaneously, and potentially interacting with each other. A number of mathematical models have been developed for general concurrent computation including
Petri nets,
process calculi and the
Parallel Random Access Machine model. A distributed system extends the idea of concurrency onto multiple computers connected through a network. Computers within the same distributed system have their own private memory, and information is often exchanged amongst themselves to achieve a common goal.
[edit]Databases and information retrieval
A database is intended to organize, store, and retrieve large amounts of data easily. Digital databases are managed using database management systems to store, create, maintain, and search data, through
database models and
query languages.
[edit]Applied computer science
[edit]Artificial intelligence
This branch of computer science aims to or is required to synthesise goal-orientated processes such as problem-solving, decision-making, environmental adaptation, learning and communication which are found in humans and animals. From its origins in
cybernetics and in the
Dartmouth Conference (1956), artificial intelligence (AI) research has been necessarily cross-disciplinary, drawing on areas of expertise such as
applied mathematics,
symbolic logic,
semiotics,
electrical engineering,
philosophy of mind,
neurophysiology, and
social intelligence. AI is associated in the popular mind with
robotic development, but the main field of practical application has been as an embedded component in areas of
software development which require computational understanding and modeling such as finance and economics, data mining and the physical sciences. The starting-point in the late 1940s was
Alan Turing's question "Can computers think?", and the question remains effectively unanswered although the "
Turing Test" is still used to assess computer output on the scale of human intelligence. But the automation of evaluative and predictive tasks has been increasingly successful as a substitute for human monitoring and intervention in domains of computer application involving complex real-world data.
