CSE Lecture 4: Scanning and parsing 28 Jan Nate Nystrom University of Texas at Arlington

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1 CSE 5317 Lecture 4: Scanning and parsing 28 Jan 2010 Nate Nystrom University of Texas at Arlington

2 Administrivia hcp://groups.google.com/group/uta- cse I will add you to the group soon TA Derek White office hours TBA 2

3 Language definition Syntax defines the structure of a program set of rules defining which symbols are a legally structured program Seman)cs defines the meaning of a program without semanucs, programs are just sequences of characters 3

4 Traditional two-pass compiler source code front end IR back end target code errors errors intermediate representauon (IR) front end maps legal code to IR back end maps IR onto target machine simplify retargeung allow muluple front ends 4

5 Traditional interpreter source code front end IR evaluator output errors errors intermediate representauon (IR) front end maps legal code to IR evaluator interprets program, producing output 5

6 Front end source code scanner tokens parser IR seman6c analysis IR errors errors errors ResponsibiliUes: recognize legal code report errors produce IR preliminary storage map shape the code for the back end Much of the front end construcuon can be automated 6

7 Scanning Pre- process the input before parsing Break stream of bytes into a stream of tokens IF, WHILE, INT, STRING, INT Scanner eliminates whitespace, comments Simplifies the parser deal with ~100 tokens vs characters smaller state machine simpler specifica6on (no need to handle whitespace/comments) uses less memory, faster 7

8 How to specify tokens? Use pa-erns Whitespace WS ::= \t WS WS \t Comments / / <any number of characters> \n Keywords if, while, do 8

9 More patterns IdenUfiers <lecer> <opuonal lecers or digits> Numbers Integer ::= 0 ( 1 to 9 <opuonal digits> ) Float ::= <digits>. <opuonal digits> <opuonal digits>. <digits> 9

10 Regular expressions Can formalize these pacerns as a regular language a (formal) language is just a set of strings different classes: regular, context- free, context- sensiuve, recursively enumerable different language classes have different formal properues closure under union, intersec6on, etc. different language classes can be recognized by different classes of machines regular languages recognized by finite automata c.f. languages recognized by pushdown automata (i.e., FA + stack) r.e. languages recognized by Turing machines NotaUon for defining a regular language is regular expression 10

11 Regular expressions A regular expression r defines a language L(r) over an alphabet Σ RE Descrip*on L(RE) ε empty string {ε} a self {a} r s alterna6on L(r) U L(s) rs concatena6on { ab a in L(r), b in L(s) } = L(r)L(s) r* Kleene closure (zero or more) L(r) U L(rr) U L(rrr) U... the following are non- standard: r? zero or one L(r) U {ε} r+ one or more L(r*) - {ε} r{i,j} repeat from i to j Uk=i..j. L(r k ). or _ any character Σ [xi- xj] characters in range xi to xj {xi,..., xj} 11

12 RE examples Whitespace ( \t )* IdenUfiers LeCer = [a- za- Z] Digit = [0-9] IdenUfier = LeCer ( LeCer Digit )* Numbers Integer ::= 0 ( [1-9] Digit* ) Decimal ::= Integer. Digit* Real = ( Integer Decimal ) ( e E ) (+ - )? Digit+ Can get much more complicated: e e12F 0xdeadbeef ULL 1.toString

13 Recognizing a RE From an RE, can construct a finite automaton or recognizer letter digit letter other digit other accept error IdenUfier = LeCer ( LeCer Digit )* 13

14 Representation of recognizer Two tables: a z A Z 0 9 other value letter letter digit other class letter 1 1 digit 3 1 other 3 2 letter other digit other letter digit accept error 14

15 Code for recognizer To change languages, just change the tables 15

16 NFA A nondetermins)c finite automaton consists of: a finite set of states a start state a set of final (or accepung) states an alphabet set of input symbols a transi)on rela)on that maps a state to the next state given an input symbol or the empty string (ε) 16

17 RE to NFA N N a a N(A) A N A B N(B) B N AB N A N(A) A N(B) B N(A) A 17

18 RE to NFA example a b abb a a b 4 5 b a b a b abb a b b

19 NFA example a b abb a b a b b s 0 s 1 s 2 s 3 Note, s0 has muluple transiuons on a This is what makes the FA nondeterminisuc a b s 0 s 0 s 1 s 0 s 1 s 2 s 2 s 3 19

20 DFA A determinis)c FA is an NFA with at most one transiuon for each state and symbol, and no transiuons on ε 20

21 Subset construction From any NFA, can construct an equivalent DFA a b a b b s 0 s 1 s 2 s 3 a b s 0 s 0 s 1 s 0 s 0 s 1 s 0 s 1 s 0 s 2 s 0 s 2 s 0 s 1 s 0 s 3 s 0 s 3 s 0 s 1 s 0 b b a a b b s 0 s 0 s 1 s 0 s 2 s 0 s 3 a a 21

22 Subset construction Idea is to map create a state in the DFA for each reachable set of states in the NFA Algorithm sketched in the book 22

23 Driving the scanner Can build a single DFA for all input tokens Wrap in an interface like this: interface Scanner { Token nexttoken(); } Parser works by asking for tokens unul end- of- file, building up a parse tree as it goes. 23

24 Limitations Do we need the parser at all? Yes, because regular languages have some limitauons regular languages can t count {a n b n } is not regular regular languages can t do recursion balanced parens are not regular: Exp =>* ( Exp ) 24

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