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A Survey of Rewriting Strategies in Program Transformation SystemsPowerPoint Presentation

A Survey of Rewriting Strategies in Program Transformation Systems

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A Survey of Rewriting Strategies in Program Transformation Systems

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A Survey of Rewriting Strategies in Program Transformation Systems

Part II

Author: Eelco Visser

Speaker: Shih-hsi Liu

Section 5 Systems

- Program Transformation Paradigms

Outline Systems

- Interactive Program Transformation
- Syntactic Abstractions in Intentional Programming
- Simple Tree Parsing
- Tree Parsing with Dynamic Programming
- Term Rewriting
- Term Rewriting with Strategy Annotations
- Functional Rewriting
- Rewriting with Traversal Functions
- Controlling Rewriting by Reflection
- Sequences of Canonical Forms
- Non-deterministic Sequential Strategies
- Generic Traversal Strategies

Interactive Program Transformation - Draco Systems

- First support the transformation of high-level domain specific programs to executable code
- Transformation rules for optimizations and refinement
- Application controlled by user via an interactive process.
- User select domain, instance (region in AST) and locale (node in AST)
- APPLY, SUGGEST, TRANSFORM

Syntactic Abstractions in Intentional Programming Systems

- Program represented by source tree (source graph)
- User enter, modify, and compiler source code directly (no need to work on program code stored as text format)
- Reduction: transforming source tree into lower-level tree
- R-code intention: basic construct translated to some form of machine code by code generator
- The order of reductions is implicit -> not a general program transformation system
- Treated as a tool for defining and eliminating new syntactic abstractions
- E.g. while (x<5) TEST : if (x<5)
++x { ++x

goto TEST

}

Simple Tree Parsing – SORCERER Systems

- Tree parser generator for the ANTLR language processing system
- Generates top-down tree parser that may execute action at any point during a tree walk directing with syntactic and semantic context information (predicate)
- Work on intermediate representation (IR) expression tree
- Goal: find structure in a tree by covering the tree with pattern (top-down manner via LL(1)-based parsing strategy)
- Transformation rule embedded in grammar productions

Simple Tree Parsing – SORCERER Systems(2)

- E.g. exp: # (PLUS exp exp) | INT //tree pattern
- Tree translation to text output:
exp: # (PLUS exp <<printf(“+”);>> exp)

| i: INT <<printf(“”%d”,i);>>

- Tree transformation: by reconstructing trees and returning them as result
exp: !# (PLUS l:exp r:exp) //! is semantic predicate

<< #exp = #(PLUS r l);>>

| INT

Simple Tree Parsing – SORCERER Systems(3)

- Semantic predicate (test structure)
exp: <<root->down!=NULL && root->down->right!=NULL>>?

#(MINUS exp exp) // Binary exp (subtract)

| #(MINUS exp); //Unary exp (negate)

- Syntactic predicate
exp: (#MINUS ..))? #(MINUS exp exp)

| #(MINUS exp);

Tree Parsing with Dynamic Programming - BURG Systems

- BURG: program that generates a fast tree parser using BURS (Bottom-Up Rewrite System) technology
- Use dynamic programming to compute all possible parses in one traversal when a tree grammar is ambiguous
- Goal: Find the lowest cost covering of IR expression tree
- Two traversals of the tree :
- one bottom-up traversal to label each node with a state that encode all optimal match (matching RHS to tree pattern)

- second top-down traversal that uses the states to select and emit code (select machine instructions driven by the goal nonterminal for the root of the tree

Tree Parsing with Dynamic Programming – BURG (2) Systems

- Grammar : set of rules
- Rule: n->t (c)
- LHS: n=nonterminal
- RHS: t=tree pattern
- C: Cost
- Find least-cost parse by DP

Tree Parsing with Dynamic Programming – BURG (3) Systems

- BURG specification
#1 goal -> reg (0) #5 reg ->Plus(reg,reg) (2)

#2 reg -> Reg (0) #6 addr -> reg (0)

#3 reg -> Int (1) #7 addr -> Int (0)

#4 reg -> Fetch(addr) (2) #8 addr -> Plus(reg,Int) (0)

- E.g. Fetch(Fetch(Plus(Reg,Int)))
- 4(4(6(5(2,3)))) => cost =7
- 4(4(8(2))) =>cost=4

Term Rewriting Systems

- Supported by OBJ, ASF+SDF, ELAN etc.
- Rewrite rule: t1->t2 (transformation of a term matching pattern t1 to the instantiation of t2)
- Use De Morgan rule and propositional logic rule
- Rewrite engine employ different strategies:
- innermost: all subterms of a term are normalized before rules are applied to term itself

- outermost: subterms closest to the root of term are rewritten first

Term Rewriting (2) Systems

- Complete normalization is not adequate for program transformation
- Rewrite system for programming languages will often be non-terminating and non-confluent
- (Section 4.2 transformation strategies:
Confluent and terminating => a unique normal form for every program)

- e.g: DAOL+DAOR DOAL+DOAR

Term Rewriting (3) Systems

Signature

sorts Prop

constructors

False : Prop

True : Prop

Atom : String->Prop

Not : Prop -> Prop

And : Prop * Prop -> Prop

Or : Prop * Prop -> Prop

(continued)

Term Rewriting (4) Systems

rules

DAOL : And (Or(x,y),z) -> Or(And(x,z),And(y,z))

DAOR : And(z,Or(x,y)) -> Or(And(z,x),And(z,y))

DOAL : Or(And(x,y),z)) -> And(Or(x,z),Or(y,z))

DOAR : Or(z,And(x,y)) -> And(Or(z,x),Or(z,y))

DN : Not(Not(x)) ->x

DMA : Not(And(x,y)) -> Or(Not(x),Not(y))

DMO : Not(Or(x,y)) -> And(Not(x),Not(y))

Term Rewriting with Strategy Annotations (1) Systems

- Problem of Term Rewriting: term with infinite reduction paths cannot be resolved by removing unnecessary rules
- E.g. pure innermost rewriting strategy a term Fac(3)
rules

Fac : Fac(x) -> If (Eq(x,0),1,Mul(x,Fac(Sub(x,1)))

IfT : If (True,x,y) -> x

IfF : If (False,x,y) ->y

IfE : If (p,x,x) ->x

- Fac(3) never terminates, since IfT, IfF always evaluate after arguments of If (never know the results of Eq(x,0)

Term Rewriting with Strategy Annotations (2) – Just-in-time

- delay the evaluation of arguments, but guarantee the term reached after evaluation is a normal form with respect to the rewrite system
- E.g. 1 innermost strategy:
strat(c) = [1,2,3,…,R1,R2,R3]

All its arguments should be evaluated first anf then the rules Ri

- E.g. 2 Solution for Term Rewriting
stract(If) = [1,IfT,IfF,2,3,IfE]

Only the first argument should be evaluated before IfT and IfF

- a permutation of argument positions and rules in which rules are applied as early as possible

Term Rewriting with Strategy Annotations (3) – E-Strategy Just-in-time

- Problem of Just-in-time: cannot handle non-normal forms for some terms (e.g. Inf(n) for some n)
- Solution: not all arguments need to be evaluated
- Strategy annotation: list of argument positions and root position (0)
- annotation declares the order of evaluations of term at root

- Root position 0 indicates evaluation of term at root

- E.g. strat(1 0): evaluate first argument of Cons and then Cons itself (root)

Term Rewriting with Strategy Annotations (4) – Laziness Just-in-time

- No reduction should be performed for subterms of that argument, unless needed for matching.
- E.g. Lazy(Cons,2) delays the second argument evaluation

Functional Rewriting Just-in-time

- Solution to problem of control over application of rewrite rules: introduce additional constructors (functions) that achieve normalization under a restricted set of rules
- Use Disjunctive normal form(DNF)
- Innermost normalization strategy: recursively traverse terms

Functional Rewriting (2) Just-in-time

- Cons:
- functional programming style of rewriting

- overhead in the form of traversal rules for each constructor in the signature, intertwining of rules and function definitions

=> make reuse of rules impossible

=> leads to specifications harder to understand

Functional Rewriting (3) Just-in-time

rules

DNF1 : dnf(True) -> True

DNF2 : dnf(False) -> False

DNF3 : dnf(Atom(x) -> Atom(x)

DNF4 : dnf(Not(x)) -> not(dnf(x))

DNF5 : dnf(And(x,y)) -> and(dnf(x),dnf(y))

DNF6 : dnf(Or(x,y)) -> Or(dnf(x),dnf(y))

AND1 : and(Or(x,y),z) -> Or(and(x,z),and(y,z))

AND2 : and(z,Or(x,y)) -> Or(and(z,x),and(z,y))

AND3 : and(x,y) -> And(x,y) (default)

NOT1 : not(Not(x)) -> x

NOT2 : not(And(x,y)) -> Or(not(x),not(y))

NOT3 : not(Or(x,y)) -> and(not(x),not(y))

NOT4 : not(x) -> Not(x) (default)

Continue … Just-in-time

Speaker: Fei Cao

5.8 Rewriting with Traversal Functions Just-in-time

- Traversal Rules vs. Normal Rules
- Overhead
- Requires new generator

- Traversal Function
- Run-time support by rewriting engine
- Still overhead with smart constructor is used

Rewriting with Traversal Functions in ASF+SDF Just-in-time

- Strategy
- Top-down
- Bottom-up

- Transformation vs. Accumulation
- Parameterization

signature Just-in-time

constructors

dnf : Prop -> Prop {traversal(trafo,bottom-up)}

and : Prop * Prop -> Prop

not : Prop -> Prop

rules

DNF4 : dnf(Not(x)) -> not(x)

DNF5 : dnf(And(x,y)) -> and(x,y)

AND1 : and(Or(x,y),z) -> Or(and(x,z),and(y,z))

AND2 : and(z,Or(x,y)) -> Or(and(z,x),and(z,y))

AND3 : and(x,y) -> And(x,y) (default)

NOT1 : not(Not(x)) -> x

NOT2 : not(And(x,y)) -> Or(not(x),not(y))

NOT3 : not(Or(x,y)) -> and(not(x),not(y))

NOT4 : not(x) -> Not(x) (default)

Figure 11: Disjunctive Normal Form with traversal function (Version 1)

signature Just-in-time

constructors

dnf : Prop -> Prop {traversal(trafo,bottom-up)}

rules

AND1 : dnf(And(Or(x,y),z) -> dnf(Or(And(x,z)),And(y,z))

AND2 : dnf(And(z,Or(x,y)) -> dnf(Or(And(z,x)),And(z,y))

NOT1 : dnf(Not(Not(x)) -> x

NOT2 : dnf(Not(And(x,y)) -> dnf(Or(Not(x),Not(y)))

NOT3 : dnf(Not(Or(x,y)) -> dnf(And(Not(x),Not(y)))

Figure 12: Disjunctive Normal Form with traversal function (Version 2)

Rewriting with Traversal Functions Just-in-time

- Limitations
- No separation of rules from strategies
- Rules un-reusable
- Even hard to distinguish the two things

- Limited Range of Traversal
- Top-down traversal stops when rule applies

- Abstraction restricted

- No separation of rules from strategies

5.9 Controlling Rewriting by Reflection Just-in-time

- Using Reflection to support specification generation
- Maude

5.10 Sequences of Canonical Forms Just-in-time

- TAMPR
- Numerical domain
- Stepwise
- Pre-processing
- Exhaustively apply rewrite rules
- Post-processing

( Just-in-timex2 + 2x + 1)y2 + (x2 - 9)y - (20x2 + 18x - 18)

sum-of-monomonials;

x-commuted-to-right;

like-powers-collected;

x-factored-out

(y2 + y - 20)x2 + (2y2 - 18)x + (y2 - 9y + 18)

5.11 Non-Deterministic Sequential Strategies Just-in-time

- ELAN
- User-definable Strategy
- Unlabeled rewrite rules—fixed innermost strategy
- Labeled rules-- user defined strategy

- Strategy Operator
- id
- fail
- dk, dc
- first
- iterate, repeat
- normalize

- User-definable Strategy

[Delete] (E U {s=s} ; R) => (E ; R) end Just-in-time

[Compose] (E ; R U {s->t}) => (E ; R U {s->u}) if reduce(t->u) end

[Simplify] (E U {s=t} ; R) => (E U {s=u} ; R) if reduce(t->u) end

[Orient] (E U {s=t) ; R) => (E ; R U {s->t}) if s > t end

[Collapse] (E ; R U {s->t}) => (E U {u=t} ; R) if reduce(s->u) end

[Deduce] (E ; R) => (E U {s=t} ; R) if s=t in CP(R) end

completion =>

repeat*(repeat*(repeat*(Collapse);

repeat*(Compose) ;

repeat*(Simplify) ;

repeat*(Delete) ;

repeat*(Orient)) ;

Deduce)

5.12 Generic Traversal Strategies Just-in-time

- Stratego
- Generic traversal strategies based on one-level traversal operators

- Sequential Programming
- identity
- failure
- non-determinitic
- deterministic
- recursive closure
- test
- negation

strategies Just-in-time

disj-nf =

innermost(DAOL + DAOR + DN + DMA + DMO)

conj-nf =

innermost(DOAL + DOAR + DN + DMA + DMO)

T =

T1 + T2 + T3 + T4 + T5 + T6 + T7 + T8 + T9 + T10 +

T11 + T12 + T13 + T14 + T15 + T16 + T17 + T18 + T19

eval =

bottomup(repeat(T))

desugar =

topdown(try(DefI + DefE))

impl-nf =

topdown(repeat(DefN + DefA2 + DefO1 + DefE))

Figure 14: Various transformations on propositional formulae.

strategies Just-in-time

try(s) = s <+ id

repeat(s) = rec x(try(s; x))

while(c, s) = rec x(try(c; s; x))

do-while(s, c) = rec x(s; try(c; x))

while-not(c, s) = rec x(c <+ s; x)

for(i, c, s) = i; while-not(c, s)

Figure 15: Generic iteration strategies.

Generic Traversal Strategies Just-in-time

- Term Traversal
- Congruent operator
- C(t1,…,tn) C(s1,,,sn)

- Congruent operator
- Match, Building and Variable Binding
- Match: ?t binding variable to the subject term
- Build: !t replace the bounded subject term with a new bunded term in t.
L = {x1,...,xn: ?l; where(s); !r}

Generic Traversal Strategies Just-in-time

- Generic Strategies
- Strategy library
- Language independent , parameterizable operations
- overhead

Scoped Dynamic Rewrite Rules Just-in-time

- The issue:
- Rule is context-free but transformation problems are often context sensitive

- Solution
- Extend traversal so as to provide context data to transformation rule
- Accumulation in ASF+SDF traversal function
- Cons: control flow tangled with data

- Use contextual rules
- Scoped dynamic rewrite rules
- Under control of a normal strategy
- Range of rule controled by scope

- Extend traversal so as to provide context data to transformation rule