The scanner performs lexical analysis of a certain program (in our case, the Simple program). It reads the source program as a sequence of characters and recognizes 'larger' textual units called tokens. For example, if the source programs contains the characters VAR ics142: INTEGER; // variable declaration the scanner would produce the tokens VAR ID(ics142) COLON ID(INTEGER) SEMICOLON to be processed in later phases of the compiler.
Note that the scanner discards white space and comments between the tokens, i.e. They are 'filtered' and not passed on to later phases. Examples of nontokens are tabs, line feeds, carriage returns, etc.
FLEX (Fast LEXical analyzer generator) is a tool for generating scanners. Zebra designer pro v2 download. In stead of writing a scanner from scratch, you only need to identify the vocabulary of a certain language (e.g. Simple), write a specification of patterns using regular expressions (e.g. DIGIT 0-9), and FLEX will construct a scanner for you. FLEX is generally used in the manner depicted here: First, FLEX reads a specification of a scanner either from an input file.lex, or from standard input, and it generates as output a C source file lex.yy.c. Then, lex.yy.c is compiled and linked with the '-lfl' library to produce an executable a.out.
Finally, a.out analyzes its input stream and transforms it into a sequence of tokens.lex is in the form of pairs of regular expressions and C code. (, ) lex.yy.c defines a routine yylex that uses the specification to recognize tokens. A.out is actually the scanner!
A compiler compiler assists you in generating compilers. Using the programs lex and yacc, you can generate your own compiler. While the program assists you in that, its not quite easy to use though. Lex does all the lexical analyzing, it analyzes the source code and divides it into tokens. You can define these tokens in a file which you will feed into lex. Yacc does the second part - bringing meaning to your tokens.
In Yacc you define a grammar for your language and rules which are applied on your grammar. This is as well done in some file you feed into yacc. Now yacc combines the output of lex and your grammar and rule definitions and generates a c source file which you then can compile - resulting in your own compiler.
There is a nice tutorial about this if you are interested: Greetings, Kork. YACC compiler assists in the next phase of the compiler. It creats a parser which will be the output to form a suitable inclusion in the next phase. YACC is available as a command/ utility on the UNIX system & has been used to help implement hundreds of compilers.
First a file say parse.y containing a YACC specification for an expression is prepared. The UNIX system command YACC parse.y transforms the file parse.y into C program called y.tab.c which is a representation with other C programs that users may have prepared. Y.tab.c is run through C compiler along with other C programs that have prepared. Y.tab.c runs through C compiler & produces object program a.out that performs the translation specified by original YACC program. YACC source program has also 3 parts that can be expressed as follows: declaration%% translation rules%% C-Programs.:).
The LEX & YACC Page The Lex & Yacc Page The asteroid to kill this dinosaur is still in orbit. Lex Manual Page ON THIS PAGE A compiler or interptreter for a programminning language is often decomposed into two parts:. Read the source program and discover its structure. Process this structure, e.g. To generate the target program. Lex and Yacc can generate program fragments that solve the first task. The task of discovering the source structure again is decomposed into subtasks:.
Split the source file into tokens ( Lex). Find the hierarchical structure of the program ( Yacc). Lex - A Lexical Analyzer Generator M.
Schmidt Lex helps write programs whose control flow is directed by instances of regular expressions in the input stream. It is well suited for editor-script type transformations and for segmenting input in preparation for a parsing routine. Lex source is a table of regular expressions and corresponding program fragments. The table is translated to a program which reads an input stream, copying it to an output stream and partitioning the input into strings which match the given expressions. As each such string is recognized the corresponding program fragment is executed. The recognition of the expressions is performed by a deterministic finite automaton generated by Lex. The program fragments written by the user are executed in the order in which the corresponding regular expressions occur in the input stream.
Yacc: Yet Another Compiler-Compiler Stephen C. Johnson Computer program input generally has some structure; in fact, every computer program that does input can be thought of as defining an ``input language' which it accepts. An input language may be as complex as a programming language, or as simple as a sequence of numbers. Unfortunately, usual input facilities are limited, difficult to use, and often are lax about checking their inputs for validity. Yacc provides a general tool for describing the input to a computer program. The Yacc user specifies the structures of his input, together with code to be invoked as each such structure is recognized.
Yacc turns such a specification into a subroutine that han- dles the input process; frequently, it is convenient and appropriate to have most of the flow of control in the user's application handled by this subroutine. Flex, A fast scanner generator Vern Paxson flex is a tool for generating scanners: programs which recognized lexical patterns in text. Flex reads the given input files, or its standard input if no file names are given, for a description of a scanner to generate.
The description is in the form of pairs of regular expressions and C code, called rules. Flex generates as output a C source file, `lex.yy.c', which defines a routine `yylex'. This file is compiled and linked with the `-lfl' library to produce an executable.
When the executable is run, it analyzes its input for occurrences of the regular expressions. Whenever it finds one, it executes the corresponding C code. Bison, The YACC-compatible Parser Generator Charles Donnelly and Richard Stallman Bison is a general-purpose parser generator that converts a grammar description for an LALR(1) context-free grammar into a C program to parse that grammar. Once you are proficient with Bison, you may use it to develop a wide range of language parsers, from those used in simple desk calculators to complex programming languages. Bison is upward compatible with Yacc: all properly-written Yacc grammars ought to work with Bison with no change.
Anyone familiar with Yacc should be able to use Bison with little trouble. Other tools for compiler writers:.
Lex Source Code
John R. Levine, Tony Mason, Doug Brown Paperback - 366 pages 2nd/updated edition (October 1992) O'Reilly & Associates ISBN: Alfred V.
Aho, Ravi Sethi, Jeffrey D. Ullman Addison-Wesley Pub Co ISBN: Andrew W. Appel, Maia Ginsburg Hardcover - 560 pages Rev expand edition (January 1998) Cambridge University Press ISBN: 052158390X.
'Lexer' redirects here. For people with this name, see.
In, lexical analysis, lexing or tokenization is the process of converting a sequence of characters (such as in a computer program or web page) into a sequence of tokens ( with an assigned and thus identified meaning). A program that performs lexical analysis may be termed a lexer, tokenizer, or scanner, though scanner is also a term for the first stage of a lexer. A lexer is generally combined with a, which together analyze the, web pages, and so forth. Contents. Applications A lexer forms the first phase of a in modern processing. Analysis generally occurs in one pass. In older languages such as, the initial stage was instead, which performed and removed whitespace and (and had scannerless parsers, with no separate lexer).
These steps are now done as part of the lexer. Lexers and parsers are most often used for compilers, but can be used for other computer language tools, such as. Lexing can be divided into two stages: the scanning, which segments the input string into syntactic units called lexemes and categorizes these into token classes; and the evaluating, which converts lexemes into processed values. Lexers are generally quite simple, with most of the complexity deferred to the parser or phases, and can often be generated by a, notably or derivatives. However, lexers can sometimes include some complexity, such as processing to make input easier and simplify the parser, and may be written partly or fully by hand, either to support more features or for performance. Lexeme A lexeme is a sequence of characters in the source program that matches the pattern for a token and is identified by the lexical analyzer as an instance of that token. Some authors term this a 'token', using 'token' interchangeably to represent the string being tokenized, and the token data structure resulting from putting this string through the process.
The word lexeme in computer science is defined differently than in linguistics. A lexeme in computer science roughly corresponds to what might be termed a in linguistics (the term in computer science has a different meaning than in linguistics), although in some cases it may be more similar to a.
Token A lexical token or simply token is a pair consisting of a token name and an optional token value. The token name is a category of lexical unit.
Common token names are. identifiers: names the programmer chooses;. keywords: names already in the programming language;.
separators (also known as punctuators): punctuation characters and paired-delimiters;. operators: symbols that operate on arguments and produce results;. literals: numeric, logical, textual, reference literals;. comments: line, block. Examples of token values Token name Sample token values identifier x, color, UP keyword if, while, return separator , (,; operator +. ( sentence ( word The ) ( word quick ) ( word brown ) ( word fox ) ( word jumps ) ( word over ) ( word the ) ( word lazy ) ( word dog )) When a token class represents more than one possible lexeme, the lexer often saves enough information to reproduce the original lexeme, so that it can be used in.
The parser typically retrieves this information from the lexer and stores it in the. This is necessary in order to avoid information loss in the case of numbers and identifiers. Tokens are identified based on the specific rules of the lexer.
Some methods used to identify tokens include:, specific sequences of characters termed a, specific separating characters called, and explicit definition by a dictionary. Special characters, including punctuation characters, are commonly used by lexers to identify tokens because of their natural use in written and programming languages. Tokens are often categorized by character content or by context within the data stream. Categories are defined by the rules of the lexer. Categories often involve grammar elements of the language used in the data stream. Programming languages often categorize tokens as identifiers, operators, grouping symbols,.
Written languages commonly categorize tokens as nouns, verbs, adjectives, or punctuation. Categories are used for post-processing of the tokens either by the parser or by other functions in the program. A lexical analyzer generally does nothing with combinations of tokens, a task left for a. For example, a typical lexical analyzer recognizes parentheses as tokens, but does nothing to ensure that each '(' is matched with a ')'. When a lexer feeds tokens to the parser, the representation used is typically an enumerated list of number representations.
For example, 'Identifier' is represented with 0, 'Assignment operator' with 1, 'Addition operator' with 2, etc. Tokens are defined often by, which are understood by a lexical analyzer generator such as. The lexical analyzer (generated automatically by a tool like lex, or hand-crafted) reads in a stream of characters, identifies the in the stream, and categorizes them into tokens. This is termed tokenizing. If the lexer finds an invalid token, it will report an error.
Following tokenizing is. From there, the interpreted data may be loaded into data structures for general use, interpretation,. Scanner The first stage, the scanner, is usually based on a (FSM). It has encoded within it information on the possible sequences of characters that can be contained within any of the tokens it handles (individual instances of these character sequences are termed ). For example, an integer token may contain any sequence of characters. In many cases, the first non-whitespace character can be used to deduce the kind of token that follows and subsequent input characters are then processed one at a time until reaching a character that is not in the set of characters acceptable for that token (this is termed the, or longest match, rule). In some languages, the lexeme creation rules are more complex and may involve over previously read characters.
For example, in C, one 'L' character is not enough to distinguish between an identifier that begins with 'L' and a wide-character string literal. Evaluator A, however, is only a string of characters known to be of a certain kind (e.g., a string literal, a sequence of letters). In order to construct a token, the lexical analyzer needs a second stage, the evaluator, which goes over the characters of the lexeme to produce a value. The lexeme's type combined with its value is what properly constitutes a token, which can be given to a parser. Some tokens such as parentheses do not really have values, and so the evaluator function for these can return nothing: only the type is needed. Similarly, sometimes evaluators can suppress a lexeme entirely, concealing it from the parser, which is useful for whitespace and comments. The evaluators for identifiers are usually simple (literally representing the identifier), but may include some.
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The evaluators for may pass the string on (deferring evaluation to the semantic analysis phase), or may perform evaluation themselves, which can be involved for different bases or floating point numbers. For a simple quoted string literal, the evaluator needs to remove only the quotes, but the evaluator for an incorporates a lexer, which unescapes the escape sequences. For example, in the source code of a computer program, the string networthfuture = ( assets - liabilities ); might be converted into the following lexical token stream; whitespace is suppressed and special characters have no value: IDENTIFIER networthfuture EQUALS OPENPARENTHESIS IDENTIFIER assets MINUS IDENTIFIER liabilities CLOSEPARENTHESIS SEMICOLON Though it is possible and sometimes necessary, due to licensing restrictions of existing parsers or if the list of tokens is small, to write a lexer by hand, lexers are often generated by automated tools. These tools generally accept regular expressions that describe the tokens allowed in the input stream.
Each regular expression is associated with a in the lexical grammar of the programming language that evaluates the lexemes matching the regular expression. These tools may generate source code that can be compiled and executed or construct a for a (which is plugged into template code for compiling and executing). Regular expressions compactly represent patterns that the characters in lexemes might follow. For example, for an -based language, an IDENTIFIER token might be any English alphabetic character or an underscore, followed by any number of instances of ASCII alphanumeric characters and/or underscores.
This could be represented compactly by the string a-zA-Za-zA-Z0-9. This means 'any character a-z, A-Z or , followed by 0 or more of a-z, A-Zor 0-9'. Regular expressions and the finite-state machines they generate are not powerful enough to handle recursive patterns, such as ' n opening parentheses, followed by a statement, followed by n closing parentheses.' They are unable to keep count, and verify that n is the same on both sides, unless a finite set of permissible values exists for n. It takes a full parser to recognize such patterns in their full generality. A parser can push parentheses on a stack and then try to pop them off and see if the stack is empty at the end (see example in the book).
Obstacles Typically, tokenization occurs at the word level. However, it is sometimes difficult to define what is meant by a 'word'. Often a tokenizer relies on simple heuristics, for example:. Punctuation and whitespace may or may not be included in the resulting list of tokens. All contiguous strings of alphabetic characters are part of one token; likewise with numbers.
Tokens are separated by characters, such as a space or line break, or by punctuation characters. In languages that use inter-word spaces (such as most that use the Latin alphabet, and most programming languages), this approach is fairly straightforward. However, even here there are many edge cases such as, and larger constructs such as (which for some purposes may count as single tokens). A classic example is 'New York-based', which a naive tokenizer may break at the space even though the better break is (arguably) at the hyphen. Tokenization is particularly difficult for languages written in which exhibit no word boundaries such as, or., such as Korean, also make tokenization tasks complicated. Some ways to address the more difficult problems include developing more complex heuristics, querying a table of common special-cases, or fitting the tokens to a that identifies collocations in a later processing step.
Software. includes rule based and statistical tokenizers which support many languages. is an API over HTTP that can cut Mandarin and Japanese sentences at word boundary. English is supported as well. (Commercial product, with freemium access) uses Advanced Probabilistic Concept Modelling to determine the weight that the term holds in the specified text indexes. The tool and its compiler is designed to generate code for fast lexical analysers based on a formal description of the lexical syntax.
It is generally considered insufficient for applications with a complex set of lexical rules and severe performance requirements. For example, the (GCC) uses hand-written lexers. Lexer generator.
See also: Lexers are often generated by a lexer generator, analogous to, and such tools often come together. The most established is, paired with the parser generator, and the free equivalents /bison.
These generators are a form of, taking in a lexical specification – generally regular expressions with some markup – and emitting a lexer. These tools yield very fast development, which is very important in early development, both to get a working lexer and because a language specification may change often. Further, they often provide advanced features, such as pre- and post-conditions which are hard to program by hand. However, an automatically generated lexer may lack flexibility, and thus may require some manual modification, or an all-manually written lexer. Lexer performance is a concern, and optimizing is worthwhile, more so in stable languages where the lexer is run very often (such as C or HTML).
Lex/flex-generated lexers are reasonably fast, but improvements of two to three times are possible using more tuned generators. Hand-written lexers are sometimes used, but modern lexer generators produce faster lexers than most hand-coded ones. The lex/flex family of generators uses a table-driven approach which is much less efficient than the directly coded approach.
– With the latter approach the generator produces an engine that directly jumps to follow-up states via goto statements. Tools like re2c have proven to produce engines that are between two and three times faster than flex produced engines. It is in general difficult to hand-write analyzers that perform better than engines generated by these latter tools. List of lexer generators. page 111, 'Compilers Principles, Techniques, & Tools, 2nd Ed.'
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'RE2C: A more versatile scanner generator'. ACM Letters on Programming Languages and Systems. 2 (1–4): 70–84., re2c manual.,.,:., '. ', golang-nuts, Rob 'Commander' Pike, 12/10/09.,:.,. Compiling with C# and Java, Pat Terry, 2005,. Algorithms + Data Structures = Programs, Niklaus Wirth, 1975,.
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Concepts of programming languages (Seventh edition) pp. 177. Boston: Pearson/Addison-Wesley. External links. Yang, W.; Tsay, Chey-Woei; Chan, Jien-Tsai (2002). Computer Languages, Systems and Structures. Elsevier Science.
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Developer Works., an analysis.
Lex can perform simple transformations by itself but its main purpose is to facilitate lexical analysis, the processing of character sequences such as to produce symbol sequences called s for use as input to other programs such as s. Lex can be used with a parser generator to perform lexical analysis. It is easy, for example, to interface Lex and, an program that generates code for the parser in the programming language. Lex is proprietary but versions based on the original code are available as open source.
These include a streamlined version called, an acronym for 'fast lexical analyzer generator,' as well as components of Open and Plan 9.
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