Computer programming languages

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A programming language is an artificial language that can be used to control the behavior of a machine, particularly a computer.^[1] Programming languages, like natural languages, are defined by syntactic and semantic rules which describe their structure and meaning respectively. Many programming languages have some form of written specification of their syntax and semantics; some are defined only by an official implementation.

Programming languages are used to facilitate communication about the task of organizing and manipulating information, and to express algorithms precisely. Some authors restrict the term "programming language" to those languages that can express all possible algorithms;^[2] sometimes the term "computer language" is used for more limited artificial languages.

Thousands of different programming languages^[3] have been created, and new languages are created every year.

[edit] Definitions

Traits often considered important for constituting a programming language:

• Function: A programming language is a language used to write computer programs, which involve a computer performing some kind of computation^[4] or algorithm and possibly control external devices such as printers, robots,^[5] and so on.

• Target: 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. Some programming languages are used by one device to control another. For example PostScript programs are frequently created by another program to control a computer printer or display.

• Constructs: Programming languages may contain constructs for defining and manipulating data structures or controlling the flow of execution.

• Expressive power: The theory of computation classifies languages by the computations they can express (see Chomsky hierarchy). 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.^[6][7]

Non-computational languages, such as markup languages like HTML or formal grammars like BNF, are usually not considered programming languages. Often a programming language is embedded in the non-computational (host) language.

[edit] Purpose

A prominent purpose of programming languages is to provide instructions to a computer. As such, 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, computers do exactly what they are told to do, and cannot understand the code the programmer "intended" to write. The combination of the language definition, the program, and the program's inputs must fully specify the external behavior that occurs when the program is executed.

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" computer language that serves all purposes, all of them have failed to be accepted in this role.^[8] The need for diverse computer 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 nearly constant 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 removed from simple translation into underlying hardware instructions. Because programmers are less tied to the needs of the computer, their programs can do more computing with less effort from the programmer. This lets them write more programs in the same amount of time.^[9]

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 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."^[10] Alan Perlis was similarly dismissive of the idea.^[11]

[edit] Elements

[edit] 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 spatial 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. 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). Below is a simple grammar, based on Lisp:

expression ::= atom | list
atom  ::= number | symbol
number  ::= [+-]?['0'-'9']+
symbol  ::= ['A'-'Z''a'-'z'].*
list  ::= '(' expression* ')'


This grammar specifies the following:

• an expression is either an atom or a list;
• an atom is either a number or a symbol;
• a number is an unbroken sequence of one or more decimal digits, optionally preceded by a plus or minus sign;
• a symbol is a letter followed by zero or more of any characters (excluding whitespace); and
• a list is a matched pair of parentheses, with zero or more expressions inside it.

The following are examples of well-formed token sequences in this grammar: '12345', '()', '(a b c232 (1))'

Not all syntactically correct programs are semantically correct. Many syntactically correct programs are nonetheless ill-formed, per the language's rules; and may (depending on the language specification and the soundness of the implementation) result in an error on translation or execution. In some cases, such programs may exhibit undefined behavior. Even when a program is well-defined within a language, it may still have a meaning that is not intended by the person who wrote it.

Using natural language as an example, it may not be possible to assign a meaning to a grammatically correct sentence or the sentence may be false:

• "Colorless green ideas sleep furiously." is grammatically well-formed but has no generally accepted meaning.
• "John is a married bachelor." is grammatically well-formed but expresses a meaning that cannot be true.

The following C language fragment is syntactically correct, but performs an operation that is not semantically defined (because p is a null pointer, the operations p->real and p->im have no meaning):

complex *p = NULL;
complex abs_p = sqrt (p->real * p->real + p->im * p->im);

The grammar needed to specify a programming language can be classified by its position in the Chomsky hierarchy. The syntax of most programming languages can be specified using a Type-2 grammar, i.e., they are context-free grammars.^[12]

[edit] Type system

For more details on this topic, see Type system.

For more details on this topic, see Type safety.

A type system defines how a programming language classifies values and expressions into types, how it can manipulate those types and how they interact. This generally includes a description of the data structures that can be constructed in the language. The design and study of type systems using formal mathematics is known as type theory.

Internally, all data in modern digital computers are stored simply as zeros or ones (binary).

[edit] Typed versus untyped languages

A language is typed if operations defined for one data type cannot be performed on values of another data type.^[13] 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.^[13] 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.^[13] Many production languages provide means to bypass or subvert the type system.

[edit] 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.^[13]

Statically-typed languages can be 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++ 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.^[14] 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.^[13] 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.

[edit] 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.^[13] This can occasionally be useful, but it can also allow some kinds of program faults to go undetected at compile time.

Strong typing prevents the above. Attempting to mix types raises an error.^[13] Strongly-typed languages are often termed type-safe or safe. Type safety can prevent particular kinds of program faults occurring (because constructs containing them are flagged at compile time).

An alternative definition for "weakly typed" refers to languages, such as Perl, JavaScript, and C++ which permit a large number of implicit type conversions; Perl in particular can be characterized as a dynamically typed programming language in which type checking can take place at runtime. See type system. This capability is often useful, but occasionally dangerous; as it would permit operations whose objects can change type on demand.

Strong and static are 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.^[15][16]

[edit] Execution semantics

Once data has been specified, the machine must be instructed to perform operations on the data. The execution semantics of a language defines how and when the various constructs of a language should produce a program behavior.

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.

[edit] Core library

For more details on this topic, see Standard 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.

[edit] Practice

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.

[edit] Specification

For more details on this topic, see Programming language specification.

The specification of a programming language is intended to provide a definition that language users and implementors can use to determine the behavior of a program, given its source code.

A programming language specification can take several forms, including the following:

• An explicit definition of the syntax and semantics of the language. While syntax is commonly specified using a formal grammar, semantic definitions may be written in natural language (e.g., the C language), or a formal semantics (e.g., the Standard ML^[17] and Scheme^[18] 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^[19]). The syntax and semantics of the language are explicit in the behavior of the reference implementation.

[edit] Implementation

For more details on this topic, see Programming language 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 the BASIC programming language 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 monitors which sequences of bytecode are frequently executed and translates them to machine code for direct execution on the hardware.

[edit] History

For more details on this topic, see History of programming languages.

[edit] Early developments

The first programming languages predate the modern computer. The 19th century had "programmable" looms and player piano scrolls which implemented what are today recognized as examples of domain-specific programming languages. By the beginning of the twentieth century, punch cards encoded data and directed mechanical processing. In the 1930s and 1940s, the formalisms of Alonzo Church's lambda calculus and Alan Turing's Turing machines provided mathematical abstractions for expressing algorithms; the lambda calculus remains influential in language design.^[20]

In the 1940s, the first electrically powered digital computers were created. The computers of the early 1950s, notably the UNIVAC I and the IBM 701 used machine language programs. First generation machine language programming was quickly superseded by a second generation of programming languages known as Assembly languages. Later in the 1950s, assembly language programming, which had evolved to include the use of macro instructions, was followed by the development of three higher-level programming languages: FORTRAN, LISP, and COBOL. Updated versions of all of these are still in general use, and importantly, each has strongly influenced the development of later languages.^[21] At the end of the 1950s, the language formalized as Algol 60 was introduced, and most later programming languages are, in many respects, descendants of Algol.^[21] The format and use of the early programming languages was heavily influenced by the constraints of the interface.^[22]

[edit] Refinement

The period from the 1960s to the late 1970s brought the development of the major language paradigms now in use, though many aspects were refinements of ideas in the very first Third-generation programming languages:

• APL introduced array programming and influenced functional programming.^[23]
• PL/I (NPL) was designed in the early 1960s to incorporate the best ideas from FORTRAN and COBOL.
• In the 1960s, Simula was the first language designed to support object-oriented programming; in the mid-1970s, Smalltalk followed with the first "purely" object-oriented language.
• C was developed between 1969 and 1973 as a systems programming language, and remains popular.^[24]
• Prolog, designed in 1972, was the first logic programming language.
• In 1978, ML built a polymorphic type system on top of Lisp, pioneering statically typed functional programming languages.

Each of these languages spawned an entire family of descendants, and most modern languages count at least one of them in their ancestry.

The 1960s and 1970s also saw considerable debate over the merits of structured programming, and whether programming languages should be designed to support it.^[25] Edsger Dijkstra, in a famous 1968 letter published in the Communications of the ACM, argued that GOTO statements should be eliminated from all "higher level" programming languages.^[26]

The 1960s and 1970s also saw expansion of techniques that reduced the footprint of a program as well as improved productivity of the programmer and user. The card deck for an early 4GL was a lot smaller for the same functionality expressed in a 3GL deck.

[edit] Consolidation and growth

The 1980s were years of relative consolidation. C++ combined object-oriented and systems programming. The United States government standardized Ada, a systems programming language intended for use by defense contractors. In Japan and elsewhere, vast sums were spent investigating so-called "fifth generation" languages that incorporated logic programming constructs.^[27] The functional languages community moved to standardize ML and Lisp. Rather than inventing new paradigms, all of these movements elaborated upon the ideas invented in the previous decade.

One important trend in language design during the 1980s was an increased focus on programming for large-scale systems through the use of modules, or large-scale organizational units of code. Modula-2, Ada, and ML all developed notable module systems in the 1980s, although other languages, such as PL/I, already had extensive support for modular programming. Module systems were often wedded to generic programming constructs.^[28]

The rapid growth of the Internet in the mid-1990's created opportunities for new languages. Perl, originally a Unix scripting tool first released in 1987, became common in dynamic Web sites. Java came to be used for server-side programming. These developments were not fundamentally novel, rather they were refinements to existing languages and paradigms, and largely based on the C family of programming languages.

Programming language evolution continues, in both industry and research. Current directions include security and reliability verification, new kinds of modularity (mixins, delegates, aspects), and database integration. ^{[citation needed]}

The 4GLs are examples of languages which are domain-specific, such as SQL, which manipulates and returns sets of data rather than the scalar values which are canonical to most programming languages. Perl, for example, with its 'here document' can hold multiple 4GL programs, as well as multiple JavaScript programs, in part of its own perl code and use variable interpolation in the 'here document' to support multi-language programming.^[29]

[edit] 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^[30]
• the number of books sold that teach or describe the language^[31]
• estimates of the number of existing lines of code written in the language—which may underestimate languages not often found in public searches^[32]
• counts of language references found using a web search engine.

[edit] Taxonomies

For more details on this topic, see Categorical list of programming languages.

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. 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).^[33] Some general purpose languages were designed largely with educational goals.^[34]

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.

[edit] See also

• Computer programming
• Lists of programming languages
• Comparison of programming languages
• Comparison of basic instructions of programming languages
• Literate programming
• Invariant based programming
• Programming language dialect
• Programming language theory

[edit] References

• Aaronson, D., & Grupsmith, E. (1978). The SIMPLE T-scope: Behavior Research Methods & Instrumentation Vol 10(6) Dec 1978, 761-763.
• Abdulla, A. M., Watkins, L. O., & Henke, J. S. (1984). The use of natural language entry and laser videodisk technology in CAI: Journal of Medical Education Vol 59(9) Sep 1984, 739-745.
• Adams, F., Aizawa, K., & Fuller, G. (1992). Rules in programming languages and networks. Hillsdale, NJ, England: Lawrence Erlbaum Associates, Inc.
• Adams, J., Richards, J. H., & Brav-Langer, B. (1980). Data storage and retrieval system for use in a learning disabilities diagnostic clinic: Journal of Learning Disabilities Vol 13(10) Dec 1980, 539-541.
• Adams, S. T., & DiSessa, A. A. (1991). Learning by "cheating": Students' inventive ways of using a Boxer motion microworld: The Journal of Mathematical Behavior Vol 10(1) Apr 1991, 79-89.
• Agalianos, A., Noss, R., & Whitty, G. (2001). Logo in mainstream schools: The struggle over the soul of an educational innovation: British Journal of Sociology of Education Vol 22(4) Dec 2001, 479-500.
• Aho, A. V. (2004). Software and the Future of Programming Languages: Science Vol 304(5662) Apr 2004, 1331-1333.
• Albers, G., Brand, H., & Cellerier, G. (1991). A microworld for genetic artificial intelligence. Westport, CT: Ablex Publishing.
• Aldridge, J. W. (1987). Cautions regarding random number generation on the Apple II: Behavior Research Methods, Instruments & Computers Vol 19(4) Nov 1987, 397-399.
• Alexander, H. (1990). Structuring dialogues using CSP. New York, NY: Cambridge University Press.
• Algarabel, S. (1983). MEMORIA: A computer program for experimental control of verbal learning and memory experiments with the Apple II microcomputer: Behavior Research Methods & Instrumentation Vol 15(3) Jun 1983, 394.
• Allan, G. B. (1978). IPA1: A BASIC program to conduct an interactive path analysis: Behavior Research Methods & Instrumentation Vol 10(3) Jun 1978, 415-416.
• Allen, J. D. (1993). Effects of representation on programming behavior: Dissertation Abstracts International.
• Allen, R. B. (1982). Cognitive factors in human interaction with computers: Behaviour & Information Technology Vol 1(3) Jul-Sep 1982, 257-278.
• Allwood, C. M., & Bjorhag, C.-G. (1991). Training of Pascal novices' error handling ability: Acta Psychologica Vol 78(1-3) Dec 1991, 137-150.
• Allwood, C. M., & Wikstrom, T. (1986). Learning complex computer programs: Behaviour & Information Technology Vol 5(3) Jul-Sep 1986, 217-225.
• Alonso, A. O. L., & Del Rey, M. H. (2004). Essaying the prolog language to obtain the computational evaluation of the coherence of reasoning: Interdisciplinaria Revista de Psicologia y Ciencias Afines Vol 21(Suppl) 2004, 183-192.
• Amigues, R., & Ginestie, J. (1991). Representations and strategies of students learning the GRAFCET command language: Le Travail Humain Vol 54(1) Mar 1991, 1-19.
• Amon, B., Ekenberg, L., Johannesson, P., Munguanaze, M., Njabili, U., & Tesha, R. M. (2003). From first-order logic to automated word generation for Lyee: Knowledge-Based Systems Vol 16(7-8) Nov 2003, 413-429.
• Anderson, J. R., Conrad, F. G., & Corbett, A. T. (1989). Skill acquisition and the LISP tutor: Cognitive Science: A Multidisciplinary Journal Vol 13(4) Oct-Dec 1989, 467-505.
• Anderson, J. R., Farrell, R., & Sauers, R. (1984). Learning to program in LISP: Cognitive Science: A Multidisciplinary Journal Vol 8(2) Apr-Jun 1984, 87-129.
• Anderson, R. E., & Magnan, S. (1995). The Questionnaire Programming Language: Social Science Computer Review Vol 13(3) Fal 1995, 291-303.
• Angeles, M., & Pickin, S. (2002). Describing generic expertise models as object-oriented analysis patterns: The heuristic multi-attribute decision pattern: Expert Systems: International Journal of Knowledge Engineering and Neural Networks Vol 19(3) Jul 2002, 142-169.
• Angle, H. V., & Carroll, J. (1979). A computer interview language: Programming the on-line interactive computer: Behavior Research Methods & Instrumentation Vol 11(3) Jun 1979, 379-383.
• Angrilli, A. (1995). PSAAL: A LabVIEW 3 program for data acquisition and analysis in psychophysiological experiments: Behavior Research Methods, Instruments & Computers Vol 27(3) Aug 1995, 367-374.
• Anstis, S., & Paradiso, M. A. (1989). Programs for visual psychophysics on the Amiga: A tutorial: Behavior Research Methods, Instruments & Computers Vol 21(5) Oct 1989, 548-563.
• Anutariya, C., Wuwongse, V., & Akama, K. (2005). XML Declarative Description With First-Order Logical Constraints: Computational Intelligence Vol 21(2) May 2005, 130-156.
• Apostel, L. (1991). Elusive recursiveness: The necessity of a dynamic and pragmatic approach: A response to Vitale: New Ideas in Psychology Vol 9(3) 1991, 367-373.
• Arblaster, A. (1982). Human factors in the design and use of computing languages: International Journal of Man-Machine Studies Vol 17(2) Aug 1982, 211-224.
• Armitage, N., & Bowerman, C. (2002). Knowledge pooling in CALL: Programming an online language learning system for reusability, maintainability and extensibility: Computer Assisted Language Learning Vol 15(1) Feb 2002, 27-53.
• Arthur, W., Jr., Bennett, W., Jr., & Huffcutt, A. I. (2001). Conducting meta-analysis using SAS. Mahwah, NJ: Lawrence Erlbaum Associates Publishers.
• Aschersleben, G., & Weber, G. (1988). The analysis of tutorial strategies in individual learning sessions: Psychologie in Erziehung und Unterricht Vol 35(4) 1988, 283-288.
• Attema, J., & Van der Veer, G. C. (1992). Design of the currency exchange interface: Task action grammar used to check consistency: Zeitschrift fur Psychologie mit Zeitschrift fur angewandte Psychologie Vol 200(2) 1992, 121-133.
• Au, W. K., & Leung, J. P. (1991). Problem solving, instructional methods and Logo programming: Journal of Educational Computing Research Vol 7(4) 1991, 455-467.
• Austin, H. S. (1986). Associations of student characteristics to measures of introductory PASCAL computer programming achievement for suburban community college students: Dissertation Abstracts International.
• Azzedine, A. (1987). The relationship of cognitive development, cognitive style and experience to performance on selected computer programming tasks: An exploration: Dissertation Abstracts International.
• Baier, J. A., & Pinto, J. A. (2003). Planning under uncertainty as GOLOG programs: Journal of Experimental & Theoretical Artificial Intelligence Vol 15(4) Oct-Dec 2003, 383-405.
• Bakeman, R. (1983). Computing lag sequential statistics: The ELAG program: Behavior Research Methods & Instrumentation Vol 15(5) Oct 1983, 530-535.
• Bamberger, H. J. (1985). The effect of Logo (Turtle Graphics) on the problem solving strategies used by fourth grade children: Dissertation Abstracts International.
• Baptista da Silva, P. V., & Faria Moro, M. L. (1998). The interaction of street teenagers with Logo language: Psicologia: Reflexao e Critica Vol 11(1) 1998, 35-58.
• Barker, P. M. (1986). Cognitive correlates of performance in computer programming by children and adolescents: Dissertation Abstracts International.
• Barr, A., Beard, M., & Atkinson, R. C. (1975). A rationale and description of a CAI program to teach the BASIC programming language: Instructional Science Vol 4(1) Apr 1975, 1-31.
• Bartram, D. (1984). Using Microsoft Basic for statistical analysis: Some cautionary notes: Bulletin of the British Psychological Society Vol 37 Sep 1984, 297-300.
• Basden, A. (2006). Aspects of knowledge representation. New York, NY: Springer Science + Business Media.
• Bashaw, W. L. (1965). A FORTRAN program for a central prediction system: Educational and Psychological Measurement 25(1) 1965, 201-204.
• Bates, T. C., & D'Oliveiro, L. (2003). PsyScript: A Macintosh application for scripting experiments: Behavior Research Methods, Instruments & Computers Vol 35(4) Nov 2003, 565-576.
• Battista, M., & Clements, D. H. (1986). The effects of Logo and CAI problem-solving environments on problem-solving abilities and mathematics achievement: Computers in Human Behavior Vol 2(3) 1986, 183-193.
• Bauer, D. W., & Eddy, J. K. (1986). The representation of command language syntax: Human Factors Vol 28(1) Feb 1986, 1-10.
• Bautista Garcia-Vera, A. (1986). An introduction to vector spaces and the LOGO programming language using the computer: Infancia y Aprendizaje Vol 33(1) 1986, 99-117.
• Bayman, P. (1984). Effects of instructional procedures on learning a first programming language: Dissertation Abstracts International.
• Bayman, P., & Mayer, R. E. (1988). Using conceptual models to teach BASIC computer programming: Journal of Educational Psychology Vol 80(3) Sep 1988, 291-298.
• Beard, D. V., Mantei, M. M., & Teorey, T. J. (1987). Metaform: Updatable form screens and their application to the use of office metaphors in query language instruction: Behaviour & Information Technology Vol 6(2) Apr-Jun 1987, 135-157.
• Beisser, S., & Gillespie, C. (2003). Kindergarteners can do it--so can you: A case study of a constructionist technology-rich first year seminar for undergraduate college students: Information Technology in Childhood Education Annual Vol 15 2003, 243-260.
• Berdonneau, C. (1985). The construction of conceptual schemes in 5- to 12-year-old children: Enfance No 2-3 1985, 183-190.
• Bergantz, D., & Hassell, J. (1991). Information relationships in PROLOG programs: How do programmers comprehend functionality? : International Journal of Man-Machine Studies Vol 35(3) Sep 1991, 313-328.
• Berry, K. J., & Mielke Jr, P. W. (1997). Agreement measure comparisons between two independent sets of raters: Educational and Psychological Measurement Vol 57(2) Apr 1997, 360-364.
• Berry, K. J., & Mielke, P. W. (1986). An APL function for computing measures of association for nominal-by-ordinal and ordinal-by-ordinal cross classifications: Behavior Research Methods, Instruments & Computers Vol 18(4) Aug 1986, 399-402.
• Berry, K. J., & Mielke, P. W. (1995). Exact cumulative probabilities for the multinomial distribution: Educational and Psychological Measurement Vol 55(5) Oct 1995, 769-772.
• Berry, K. J., & Mielke, P. W. (1996). Nonasymptotic probability values for Cochran's Q statistic: A FORTRAN 77 program: Perceptual and Motor Skills Vol 82(1) Feb 1996, 303-306.
• Berry, K. J., & Mielke, P. W., Jr. (1998). A FORTRAN program for permutation covariate analyses of residuals based on Euclidean distance: Psychological Reports Vol 82(2) Apr 1998, 371-375.
• Bideault, A. (1985). Procedures of CE-2 children in a route construction task: LOGO experiences: Enfance No 2-3 1985, 201-212.
• Biggs, T. C., Pulham, D., & Bleasdale, F. A. (1988). Apple slide tachistoscope: Behavior Research Methods, Instruments & Computers Vol 20(1) Feb 1988, 49-53.
• Billings, L. J. (1987). Development of mathematical task persistence and problem-solving ability in fifth and sixth grade students through the use of LOGO and heuristic methodologies: Dissertation Abstracts International.
• Birnbaum, M. H., & Wakcher, S. V. (2002). Web-based experiments controlled by JavaScript: An example from probability learning: Behavior Research Methods, Instruments & Computers Vol 34(2) May 2002, 189-199.
• Bishop-Clark, C. (1998). Comparing understanding of programming design concepts using visual basic and traditional basic: Journal of Educational Computing Research Vol 18(1) 1998, 37-47.
• Bitter, G. G., & Lu, M.-y. (1988). Factors influencing success in a junior high computer programming course: Journal of Educational Computing Research Vol 4(1) 1988, 71-78.
• Bjork, L.-E. (1974). BASIC programming in the tenth-grade mathematics course and its effects on students' numerical ability and attitudes towards mathematics: Pedagogisk-Psykologiska Problem No 246 Jul 1974, 22.
• Black, J. B., Kay, D. S., & Soloway, E. M. (1987). Goal and plan knowledge representations: From stories to text editors and programs. Cambridge, MA: The MIT Press.
• Black, J. B., & Sebrechts, M. M. (1981). Facilitating human-computer communication: Applied Psycholinguistics Vol 2(2) May 1981, 149-177.
• Blackwelder, C. K. (1987). Logo: A possible aid in the development of Piagetian formal reasoning: Dissertation Abstracts International.
• Blackwell, A. F., Whitley, K. N., Good, J., & Petre, M. (2001). Cognitive factors in programming with diagrams: Artificial Intelligence Review Vol 15(1-2) Mar 2001, 95-114.
• Blume, G. W., & Schoen, H. L. (1988). Mathematical problem-solving performance of eighth-grade programmers and nonprogrammers: Journal for Research in Mathematics Education Vol 19(2) Mar 1988, 142-156.
• Bodrow, W., & Bodrow, l. (2006). Didactical aspects of content production in codewitz: Communication & Cognition Vol 39(1-2) 2006, 51-60.
• Boecker, H.-D., Eden, H., & Fischer, G. (1991). Interactive problem solving using LOGO. Hillsdale, NJ, England: Lawrence Erlbaum Associates, Inc.
• Boies, S. J., & Spiegel, M. F. (1973). A behavioral analysis of programming on the use of interactive debugging facilities. Oxford, England: Ibm.
• Bonar, J. G. (1985). Understanding the bugs of novice programmers: Dissertation Abstracts International.
• Boomsma, A. (1985). Nonconvergence, improper solutions, and starting values in LISREL maximum likelihood estimation: Psychometrika Vol 50(2) Jun 1985, 229-242.
• Booth, S. (1993). A study of learning to program from an experiential perspective: Computers in Human Behavior Vol 9(2-3) Sum-Fal 1993, 185-202.
• Borthick, A. F., Bowen, P. L., Jones, D. R., & Tse, M. H. K. (2001). The effects of information request ambiguity and construct incongruence on query development: Decision Support Systems Vol 32(1) Nov 2001, 3-25.
• Bottoni, P., Costabile, M. F., Levialdi, S., & Mussio, P. (1997). Defining visual languages for interactive computing: IEEE Transactions on Systems, Man, & Cybernetics Part A: Systems & Humans Vol 27(6) Nov 1997, 773-782.
• Bouckaert, A., & de Leval, N. (1985). Information and neurolinguistics: Bulletin de Psychologie Scolaire et d'Orientation Vol 34(3) Jul-Sep 1985, 125-132.
• Bovet, P. (1975). The language problem in programming automized experiments: Cahiers de Psychologie Vol 18(1-3) 1975, 5-17.
• Bowden, E. M., Douglas, S. A., & Stanford, C. A. (1989). Testing the principle of orthogonality in language design: Human-Computer Interaction Vol 4(2) 1989, 95-120.
• Brady, M. (1989). Logic programming: Irish Journal of Psychology Vol 10(2) 1989, 304-316.
• Brent, E. E. (1978). PLA-3: A FORTRAN program for prediction logic analysis for three-dimensional contingency tables: Behavior Research Methods & Instrumentation Vol 10(3) Jun 1978, 454.
• Briggs, N. E., & Sheu, C.-F. (1998). Using Java in introductory statistics: Behavior Research Methods, Instruments & Computers Vol 30(2) May 1998, 246-249.
• Briggs, P. (1989). Consistent benefits for the system designer and the end-user: Applied Ergonomics Vol 20(3) Sep 1989, 160-167.
• Brinkley, V. M. (1990). Pointing behaviors of preschoolers during Logo mastery: Dissertation Abstracts International.
• Brous, M. T. (1986). LOGO and the learning disabled child: Observation and documentation of LOGO in practice: Dissertation Abstracts International.
• Brousentsova, T. y. N. (1989). The estimation of didactic effectiveness of instructional courses in the automated instructional system "The Mentor." Voprosy Psychologii No 1 Jan-Feb 1989, 62-72.
• Brown, R. L. (1986). A comparison of the LISREL and EQS programs for obtaining parameter estimates in confirmatory factor analysis studies: Behavior Research Methods, Instruments & Computers Vol 18(4) Aug 1986, 382-388.
• Buendia-Garcia, F., & Diaz, P. (2003). A Framework for the Specification of the Semantics and the Dynamics of Instructional Applications: Journal of Educational Multimedia and Hypermedia Vol 12(4) 2003, 399-424.
• Burger, D., & Liard, C. (1984). GRIBOL, a computer language: Eta Evolutiva No 18 Jun 1984, 91-98.
• Burkhardt, K. J. (1976). EMPP: An extensible multiprogramming system for experimental psychology: Behavior Research Methods & Instrumentation Vol 8(2) Apr 1976, 239-244.
• Burkhardt, K. J. (1979). The design of a real-time operating system for experimental psychology: Behavior Research Methods & Instrumentation Vol 11(5) Oct 1979, 507-511.
• Burns, B., & Coon, H. (1990). Logo programming and peer interactions: An analysis of process- and product-oriented collaborations: Journal of Educational Computing Research Vol 6(4) 1990, 393-410.
• Burns, B., & Hagerman, A. (1989). Computer experience, self-concept and problem-solving: The effects of LOGO on children's ideas of themselves as learners: Journal of Educational Computing Research Vol 5(2) 1989, 199-212.
• Cafolla, R. (1987). The relationship of Piagetian formal operations and other cognitive factors to computer programming ability: Dissertation Abstracts International.
• Calani Baranauskas, M. C. (1995). Observational studies about novices interacting in a Prolog environment based on tools: Instructional Science Vol 23(1-3) May 1995, 89-109.
• Calder, B. J., & Rowland, K. M. (1974). A FORTRAN IV program for presenting optimally ordered paired comparison stimuli: Behavior Research Methods & Instrumentation Vol 6(5) Sep 1974, 506.
• Camerini, G. B. (1984). Children's world, computers, and Piagetian theories: LOGO--toward a new education? : Eta Evolutiva No 18 Jun 1984, 102-110.
• Campbell, D. J. (1983). Machine language programs for high-speed transfer of data between the KIM-1 and the CBM/PET microcomputers: Behavior Research Methods & Instrumentation Vol 15(5) Oct 1983, 547-548.
• Campbell, D. J., & Loher, W. (1983). A microcomputer-based modulator for simulating insect songs and the response of crickets to an artificial calling song: Behavior Research Methods & Instrumentation Vol 15(5) Oct 1983, 538-541.
• Campbell, P. B. (1988). To Author or Not to Author: That is the Question: PsycCRITIQUES Vol 33 (7), Jul, 1988.
• Campbell, P. F., Fein, G. G., & Schwartz, S. S. (1991). The effects of Logo experience on first-grade children's ability to estimate distance: Journal of Educational Computing Research Vol 7(3) 1991, 331-349.
• Cannara, A. B. (1976). Experiments in teaching children computer programming: Dissertation Abstracts International.
• Cannon, F. R. (1977). The transferability of learning of syntax between COBOL and FORTRAN: Dissertation Abstracts International.
• Carlson, V. R., & Knight, J. (1973). SCV: A general subroutine for the selection of combinations of variables: Behavior Research Methods & Instrumentation Vol 5(5) Sep 1973, 425-427.
• Carter, J. H. (1992). Comparison of a problem-solving approach to computer programming curriculum with a syntax-oriented approach: Dissertation Abstracts International.
• Carver, D. L. (1989). Programmer variations in software debugging approaches: International Journal of Man-Machine Studies Vol 31(3) Sep 1989, 315-322.
• Carver, S. M. (1987). Transfer of LOGO debugging skill: Analysis, instruction, and assessment: Dissertation Abstracts International.
• Carver, S. M., & Klahr, D. (1986). Assessing children's LOGO debugging skills with a formal model: Journal of Educational Computing Research Vol 2(4) 1986, 487-525.
• Carver, S. M., & Risinger, S. C. (1987). Improving children's debugging skills. Westport, CT: Ablex Publishing.
• Castellan, N. J. (1973). Laboratory programming languages and standardization: Behavior Research Methods & Instrumentation Vol 5(2) Mar 1973, 249-252.
• Cathcart, W. G. (1990). Effects of Logo instruction on cognitive style: Journal of Educational Computing Research Vol 6(2) 1990, 231-242.
• Chambres, P. (1985). Exploratory research on the capacity for "piloting" in 4- to 6-year-old children: LOGO experiences: Enfance No 2-3 1985, 191-199.
• Chan, J. S., & Spence, C. (2003). Presenting multiple auditory signals using multiple sound cards in Visual Basic 6.0: Behavior Research Methods, Instruments & Computers Vol 35(1) Apr 2003, 125-128.
• Chang, W.-C., Hsu, H.-H., Smith, T. K., & Wang, C.-C. (2004). Enhancing SCORM metadata for assessment authoring in e-Learning: Journal of Computer Assisted Learning Vol 20(4) Aug 2004, 305-316.
• Chapman, J., Hu, L.-t., & Mullen, B. (1986). GROUP1 and GROUP2: BASIC programs for laboratory research on the commons dilemma and group persuasion: Behavior Research Methods, Instruments & Computers Vol 18(5) Oct 1986, 466-467.
• Charniak, E., Riesbeck, C. K., & Meehan, J. R. (1987). Artificial intelligence programming (2nd ed.). Hillsdale, NJ, England: Lawrence Erlbaum Associates, Inc.
• Chen, H., & Dong, Y. (2006). Facilitating formal specification acquisition by using recursive functions on context-free languages: Knowledge-Based Systems Vol 19(2) Jun 2006, 141-151.
• Cherry, J. M. (1986). An experimenting evaluation of prefix and postfix notation in command language syntax: International Journal of Man-Machine Studies Vol 24(4) Apr 1986, 365-374.
• Chiang, B., Thorpe, H. W., & Lubke, M. (1984). LD students tackle the LOGO language: Strategies and implications: Journal of Learning Disabilities Vol 17(5) May 1984, 303-304.
• Choi, I., Kim, K., & Jang, M. (2007). An XML-based process repository and process query language for Integrated Process Management: Knowledge & Process Management Vol 14(4) Oct-Dec 2007, 303-316.
• Choi, W. S. (1991). Effect of Pascal and FORTRAN programming instruction on the problem-solving cognitive ability in formal operational stage students: Dissertation Abstracts International.
• Chow, A. C. (1991). A generic user interface for hierarchical knowledge-based simulation and control systems: Dissertation Abstracts International.
• Chubb, G. P., Laughery, K. R., Jr., & Pritsker, A. A. B. (1987). Simulating manned systems. Oxford, England: John Wiley & Sons.
• Clark, J. A., Jacob, J. L., Maitra, S., & Stanica, P. (2004). Almost Boolean Functions: The Design of Boolean Functions by Spectral Inversion: Computational Intelligence Vol 20(3) Aug 2004, 450-462.
• Clark, K. L., & McCabe, F. G. (2006). Ontology oriented programming in Go! : Applied Intelligence Vol 24(3) Jun 2006, 189-204.
• Claveau, V., Sebillot, P., Fabre, C., Bouillon, P., Cussens, J., & Frisch, A. M. (2004). Learning Semantic Lexicons from a Part-of-Speech and Semantically Tagged Corpus Using Inductive Logic Programming: Journal of Machine Learning Research Vol 4(4) May 2004, 493-525.
• Clement, C. A., Kurland, D. M., Mawby, R., & Pea, R. D. (1986). Analogical reasoning and computer programming: Journal of Educational Computing Research Vol 2(4) 1986, 473-486.
• Clements, D. H. (1986). Developmental differences in the learning of computer programming: Achievement and relationships to cognitive abilities: Journal of Applied Developmental Psychology Vol 7(3) Jul-Sep 1986, 251-266.
• Clements, D. H. (1986). Logo and cognition: A theoretical foundation: Computers in Human Behavior Vol 2(2) 1986, 95-110.
• Clements, D. H. (1987). Longitudinal study of the effects of Logo programming on cognitive abilities and achievement: Journal of Educational Computing Research Vol 3(1) 1987, 73-94.
• Clements, D. H., & Battista, M. T. (1989). Learning of geometric concepts in a Logo environment: Journal for Research in Mathematics Education Vol 20(5) Nov 1989, 450-467.
• Clements, D. H., & Battista, M. T. (1990). The effects of Logo on children's conceptualizations of angle and polygons: Journal for Research in Mathematics Education Vol 21(5) Nov 1990, 356-371.
• Clements, D. H., & Gullo, D. F. (1984). Effects of computer programming on young children's cognition: Journal of Educational Psychology Vol 76(6) Dec 1984, 1051-1058.
• Clements, D. H., & Merriman, S. (1988). Componential developments in LOGO programming environments. Hillsdale, NJ, England: Lawrence Erlbaum Associates, Inc.
• Coates, L., & Stephens, L. (1990). Relationship of compu