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Short Code (for UNIVAC II) Albert B. Tonik, [2] J. R. Logan Short Code (for UNIVAC I) 1952 A-0: Grace Hopper: Short Code 1952 Glennie Autocode: Alick Glennie after Alan Turing: CPC Coding scheme 1952 Operator programming Alexey Andreevich Lyapunov with the participation Kateryna Yushchenko: MESM: 1952 Editing Generator Milly Koss SORT/MERGE ...
A call graph generated for a simple computer program in Python. A call graph (also known as a call multigraph [1] [2]) is a control-flow graph, [3] which represents calling relationships between subroutines in a computer program.
For example, in the source code of a computer program, the string ... Lexers are often generated by a lexer generator, analogous to parser generators, ...
Adapt the source of the code generator to the new machine. Execute the adapted source using the interpreter with the code generator source as input. This will generate the machine code for the code generator. The difficult part of coding the optimization routines is done using the high-level language instead of the assembly language of the target.
Iterators are a 1980's version of the 1950's concept of generators. A generator is a subroutine which is called like a function, but returns a "new" value every time it is called (i.e., it is emphatically not a mathematical function). The "random number generator" is prototypic of a generator.
In the case of programming languages, a parser is a component of a compiler or interpreter, which parses the source code of a computer programming language to create some form of internal representation; the parser is a key step in the compiler frontend.
RPG II is a very early and popular version of the IBM RPG programming language.. It was developed in the late 1960s and designed to work on the smallest IBM systems of the time such as the IBM 1130, IBM System/3, System/32, System/34, System/36.
A random seed (or seed state, or just seed) is a number (or vector) used to initialize a pseudorandom number generator.. A pseudorandom number generator's number sequence is completely determined by the seed: thus, if a pseudorandom number generator is later reinitialized with the same seed, it will produce the same sequence of numbers.
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