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Parsing. Programming Language Concepts Lecture 6. Prepared by Manuel E. Bermúdez, Ph.D. Associate Professor University of Florida. Context-Free Grammars.

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parsing

Parsing

Programming Language Concepts

Lecture 6

Prepared by

Manuel E. Bermúdez, Ph.D.

Associate Professor

University of Florida

context free grammars
Context-Free Grammars
  • Definition: A context-free grammar (CFG) is a quadrupleG = (, , P, S),where all productions are of the formA →, for A   and   (u )*.
  • Re-writing using grammar rules:
    • βAγ => βγif A → (derivation).
string derivations
String Derivations
  • Left-most derivation: At each step, the left-most nonterminal is re-written.
  • Right-most derivation: At each step, the right-most nonterminal is re-written.
derivation trees
Derivation Trees

Derivation trees:

Describe re-writes, independently of the order (left-most or right-most).

  • Each tree branch matches a production rule in the grammar.
derivation trees1
Derivation Trees

Notes:

  • Leaves are terminals.
  • Bottom contour is the sentence.
  • Left recursion causes left branching.
  • Right recursion causes right branching.
goal of parsing
Goal of Parsing
  • Examine input string, determine whether it\'s legal.
  • Equivalent to building derivation tree.
  • Added benefit: tree embodies syntactic structure of input.
  • Therefore, tree should be unique.
ambiguous grammars
Ambiguous Grammars
  • Definition: A CFG is ambiguous if there exist two different right-most (or left-most, but not both) derivations for some sentence z.
  • (Equivalent) Definition: A CFG is ambiguous if there exist two different derivation trees for some sentence z.
ambiguous grammars1
Ambiguous Grammars

Classic ambiguities:

  • Simultaneous left/right recursion:

E → E + E

→ i

  • Dangling else problem:

S → if E then S

→ if E then S else S

operator precedence and associativity
Operator Precedence and Associativity
  • Let’s build a CFG for expressions consisting of:
    • elementary identifier i.
    • +and - (binary ops) have lowest precedence, and are left associative .
    • * and / (binary ops) have middle precedence, and are right associative.
    • + and - (unary ops) have highest precedence, and are right associative.
corresponding grammar for expressions
Corresponding Grammar for Expressions

E → E + TE consists of T\'s,

→ E - Tseparated by –’s and +\'s

→ T(lowest precedence).

T → F * TT consists of F\'s,

→ F / Tseparated by *\'s and /\'s

→ F(next precedence).

F → - FF consists of a single P,

→ + Fpreceded by +\'s and -\'s.

→ P(next precedence).

P → \'(\' E \')\'P consists of a parenthesized E,

→ i or a single i(highest precedence).

operator precedence and associativity1
Operator Precedence and Associativity
  • Operator precedence:
    • The lower in the grammar, the higher the precedence.
  • Operator Associativity:
    • Tie breaker for precedence.
    • Left recursion in the grammar means
      • left associativity of the operator,
      • left branching in the tree.
    • Right recursion in the grammar means
      • right associativity of the operator,
      • right branching in the tree.
building derivation trees
Building Derivation Trees

Sample Input :

- + i - i * ( i + i ) / i + i

(Human) derivation tree construction:

  • Bottom-up.
  • On each pass, scan entire expression, process operators with highest precedence (parentheses are highest).
  • Lowest precedence operators are last, at the top of tree.
abstract syntax trees
Abstract Syntax Trees
  • AST is a condensed version of the derivation tree.
  • No noise (intermediate nodes).
  • String-to-tree transduction grammar:
    • rules of the form A → ω => \'s\'.
  • Build \'s\' tree node, with one child per tree from each nonterminal in ω.
example
Example

E → E + T => +

→ E - T => -

→ T

T → F * T => *

→ F / T => /

→ F

F → - F => neg

→ + F => +

→ P

P → \'(\' E \')\'

→ i => i

string to tree transduction
String-to-Tree Transduction
  • We transduce from vocabulary of input symbols, to vocabulary of tree node names.
  • Could eliminate construction of unary + node, anticipating semantics.

F → - F => neg

→ + F // no more unary +node

→ P

the game of syntactic dominoes
The Game of Syntactic Dominoes
  • The grammar:

E → E+T T → P*T P → (E)

→ T → P →i

  • The playing pieces: An arbitrary supply of each piece (one per grammar rule).
  • The game board:
    • Start domino at the top.
    • Bottom dominoes are the "input."
the game of syntactic dominoes1
The Game of Syntactic Dominoes
  • Game rules:
    • Add game pieces to the board.
    • Match the flat parts and the symbols.
    • Lines are infinitely elastic.
  • Object of the game:
    • Connect start domino with the input dominoes.
    • Leave no unmatched flat parts.
parsing strategies
Parsing Strategies
  • Same as for the game of syntactic dominoes.
    • “Top-down” parsing: start at the start symbol, work toward the input string.
    • “Bottom-up” parsing: start at the input string, work towards the goal symbol.
  • In either strategy, can process the input left-to-right  or right-to-left 
top down parsing
Top-Down Parsing
  • Attempt a left-most derivation, by predicting the re-write that will match the remaining input.
  • Use a string (a stack, really) from which the input can be derived.
top down parsing1
Top-Down Parsing

Start with S on the stack.

At every step, two alternatives:

  •  (the stack) begins with a terminal t. Match t against the first input symbol.
  •  begins with a nonterminal A. Consult an OPF (Omniscient Parsing Function) to determine which production for A would lead to a match with the first symbol of the input.

The OPF does the “predicting” in such a predictive parser.

classical top down parsing algorithm
Classical Top-Down Parsing Algorithm

Push (Stack, S);

while not Empty (Stack) do

if Top(Stack) 

then if Top(Stack) = Head(input)

then input := tail(input)

Pop(Stack)

else error (Stack, input)

else P:= OPF (Stack, input)

Push (Pop(Stack), RHS(P))

od

top down parsing2
Top-Down Parsing
  • Most parsing methods impose bounds on the amount of stack lookback and input lookahead. For programming languages, a common choice is (1,1).
  • We must define OPF (A,t), where A is the top element of the stack, and t is the first symbol on the input.
  • Storage requirements: O(n2), where n is the size of the grammar vocabulary

(a few hundred).

ll 1 grammars
LL(1) Grammars

Definition:

A CFG G is LL(1) (Left-to-right, Left-most, one-symbol lookahead)

iff for all A, and for allA→, A→,   ,

Select (A → ) ∩ Select (A → ) = 

  • Previous example: Grammar is not LL(1).
  • More later on why, and what do to about it.
example1
Example:

S → A {b,}

A → bAd {b}

→ {d, }

Disjoint!

Grammar is LL(1)!

(At most) one production per entry.

parsing1

Parsing

Programming Language Concepts

Lecture 6

Prepared by

Manuel E. Bermúdez, Ph.D.

Associate Professor

University of Florida

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