denotational-semantics-builder

name: denotational-semantics-builder description: 'Builds denotational semantic models. Use when: (1) Formalizing language semantics, (2) Proving program properties, (3) Semantic analysis.' version: 1.0.0 tags:

• semantics
• denotational-semantics
• domain-theory
• pl-foundations difficulty: advanced languages:
• haskell
• coq dependencies:
• operational-semantics-definer

Denotational Semantics Builder

Constructs denotational semantic models for programming languages.

When to Use

• Formal semantics definition
• Proving program properties
• Semantic equivalence
• Language design

What This Skill Does

1. Defines domains - Semantic domains
2. Maps syntax - ⟦e⟧ : Exp → D
3. Compositional - Meaning from parts
4. Handles recursion - Fixed-point semantics

Core Theory

Denotational Semantics:
  ⟦e⟧ ∈ D  where D is semantic domain
  
  ⟦n⟧ = ⟦n⟧ℤ ∈ ℤ
  ⟦x⟧ = ρ(x) ∈ D
  ⟦λx.e⟧ = λd. ⟦e⟧(ρ[x↦d])
  ⟦e₁ e₂⟧ = ⟦e₁⟧(⟦e₂⟧)

Implementation

from typing import Dict, Callable, Generic, TypeVar, Set
from dataclasses import dataclass

# Semantic domains
Domain = TypeVar('Domain')
Value = TypeVar('Value')

# Basic domains
class Int:
    """Integers"""
    def __init__(self, n: int):
        self.n = n

class Bool:
    """Booleans"""
    def __init__(self, b: bool):
        self.b = b

# Function domain
class Fun(Generic[Domain, Value]):
    """Function domain D → E"""
    def __init__(self, f: Callable[[Domain], Value]):
        self.f = f
    
    def apply(self, x: Domain) -> Value:
        return self.f(x)

# Product domain
class Prod(Generic[Domain, Value]):
    """Product domain D × E"""
    def __init__(self, first: Domain, second: Value):
        self.first = first
        self.second = second

# Environment
class Env(Dict[str, Value]):
    """Runtime environment"""
    pass

# Denotational semantics
class DenotationalSemantics:
    """Denotational semantics for simple language"""
    
    def __init__(self):
        self.functions = {}
    
    def sem_int(self, n: int) -> Int:
        """⟦n⟧ = n"""
        return Int(n)
    
    def sem_bool(self, b: bool) -> Bool:
        """⟦b⟧ = b"""
        return Bool(b)
    
    def sem_var(self, x: str, env: Env) -> Value:
        """⟦x⟧ = ρ(x)"""
        return env[x]
    
    def sem_lambda(self, x: str, body: 'Expr', env: Env) -> Value:
        """⟦λx.e⟧ = λd. ⟦e⟧(ρ[x↦d])"""
        
        def semantics(d: Value) -> Value:
            new_env = Env(env)
            new_env[x] = d
            return self.sem(body, new_env)
        
        return Fun(semantics)
    
    def sem_app(self, e1: 'Expr', e2: 'Expr', env: Env) -> Value:
        """⟦e₁ e₂⟧ = ⟦e₁⟧(⟦e₂⟧)"""
        
        v1 = self.sem(e1, env)
        v2 = self.sem(e2, env)
        
        return v1.apply(v2)
    
    def sem_if(self, cond: 'Expr', then: 'Expr', else_: 'Expr', env: Env) -> Value:
        """⟦if e then e₁ else e₂⟧"""
        
        v = self.sem(cond, env)
        
        if v.b:
            return self.sem(then, env)
        else:
            return self.sem(else_, env)
    
    def sem(self, expr: 'Expr', env: Env) -> Value:
        """Main semantics function"""
        
        match expr:
            case Int(n):
                return self.sem_int(n)
            
            case Bool(b):
                return self.sem_bool(b)
            
            case Var(x):
                return self.sem_var(x, env)
            
            case Lambda(x, body):
                return self.sem_lambda(x, body, env)
            
            case App(e1, e2):
                return self.sem_app(e1, e2, env)
            
            case If(cond, then, else_):
                return self.sem_if(cond, then, else_, env)

Fixed-Point Semantics

class RecursiveSemantics:
    """Semantics with recursion via fixed points"""
    
    def __init__(self):
        self.functions = {}
    
    def fix(self, F: Callable[['Value'], 'Value']) -> 'Value':
        """Fixed-point computation (Kleene iteration)"""
        
        # Start with bottom and iterate
        x = None
        while True:
            fx = F(x)
            if fx == x:
                return x
            x = fx
    
    def sem_letrec(self, x: str, e1: 'Expr', e2: 'Expr', env: Env) -> Value:
        """⟦letrec x = e₁ in e₂⟧"""
        
        # Create recursive environment
        def F(f):
            new_env = Env(env)
            new_env[x] = f
            return self.sem(e2, new_env)
        
        # Compute fixed point
        # This gives meaning to recursive definitions
        return self.fix(F)
    
    def sem_while(self, cond: 'Expr', body: 'Expr', env: Env) -> Value:
        """⟦while e do c⟧"""
        
        while True:
            v = self.sem(cond, env)
            if not v.b:
                return Bool(True)
            self.sem(body, env)

Domain Theory

class Domain:
    """Complete partial order"""
    
    def __init__(self):
        self.elements = set()
        self.partial_order = {}
    
    def lub(self, chain: Set['Value']) -> 'Value':
        """Least upper bound of chain"""
        # In complete partial order: lub of chain exists
        pass
    
    def bot(self) -> 'Value':
        """Bottom element (⊥)"""
        pass
    
    def le(self, x: 'Value', y: 'Value') -> bool:
        """Partial order (⊑)"""
        pass

# Lifted domains
class Lifted(Generic[Domain]):
    """Domain with bottom"""
    def __init__(self, d: Domain, bottom: Value):
        self.domain = d
        self.bottom = bottom

# Strict functions
class StrictFun(Domain):
    """Strict function (⊥ → ⊥ = ⊥)"""
    
    def __init__(self, dom: Domain, cod: Domain):
        self.domain = dom
        self.codomain = cod
    
    def apply(self, x: Value) -> Value:
        if x == self.domain.bottom:
            return self.codomain.bottom
        # Apply function
        pass

Equivalence Proof

class SemanticEquivalence:
    """Prove semantic equivalence"""
    
    def prove_eq(self, e1: 'Expr', e2: 'Expr') -> bool:
        """Prove ⟦e₁⟧ = ⟦e₂⟧"""
        
        # Compute denotations
        d1 = self.sem(e1, {})
        d2 = self.sem(e2, {})
        
        return d1 == d2
    
    def prove_extensional(self, e1: 'Expr', e2: 'Expr') -> bool:
        """
        Extensional equality:
        ∀ρ. ⟦e₁⟧ρ = ⟦e₂⟧ρ
        """
        
        # Test various environments
        test_envs = self.generate_test_envs()
        
        for env in test_envs:
            d1 = self.sem(e1, env)
            d2 = self.sem(e2, env)
            
            if d1 != d2:
                return False
        
        return True

Denotational vs Operational

Operational:     How program executes (steps)
  ⟦e⟧ → v       (one step at a time)

Denotational:   What program means (mathematical)
  ⟦e⟧ ∈ D       (direct mapping)

Relationship:
  Operational → Denotational (adequacy)
  Denotational → Operational (full abstraction)

Key Concepts

Canonical References

Tips

• Start with simple types
• Use domain theory for recursion
• Prove adequacy after
• Consider extensionality

Related Skills

• operational-semantics-definer - Operational semantics
• hoare-logic-verifier - Axiomatic semantics
• lambda-calculus-interpreter - Lambda calculus

Research Tools & Artifacts

Domain theory and denotational semantics tools:

Proof Assistant Formalizations

• Coq stdlib - Category theory, domain theory
• Iris - Higher-order separation logic
• Cartesian Closed Categories - Category theory for programming

Research Frontiers

1. Game Semantics

• Approach: Programs as strategies in games
• Advantage: Captures sequential behavior precisely
• Papers: "Full Abstraction for PCF" (Abramsky, Jagadeesan, Malacaria 2000; Hyland, Ong 2000)
• Application: Full abstraction for PCF, verification

2. Relational Semantics

• Approach: Relations between programs
• Advantage: Proves relational properties
• Papers: "Simple Relational Correctness Proofs for Static Analyses and Program Transformations" (Benton, 2004)
• Application: Program equivalence, compiler correctness

3. Metric Semantics

• Approach: Programs as points in metric spaces
• Advantage: Quantitative reasoning
• Papers: "Metric Semantics" (de Bakker, America)
• Application: Probabilistic programs, bisimulation

4. Step-Indexed Semantics

• Approach: Stratified semantic definitions
• Advantage: Handles recursion and step counting
• Papers: "An Indexed Model of Recursive Types" (Appel & McAllester, 2001)
• Application: Foundational proof-carrying code, separation logic

Implementation Pitfalls

The "Fixed-Point Needs Continuity" Problem

Not all fixed-points compute correctly:

# WRONG: Non-monotonic function
def F(f):
    if f(0) > 0: return lambda x. 0
    else: return lambda x. 1

# RIGHT: Monotonic function (for denotational)
# f is monotonic: if a ⊑ b then F(a) ⊑ F(b)
# Then Kleene fixed-point converges

The "Adequacy Gap"

Operational and denotational semantics must match:

Denotational: ⟦e⟧ = ⊥  (doesn't terminate)
Operational:  e →* ⊥   (diverges)

Adequacy: If ⟦e⟧ = v then e →* v
          If ⟦e⟧ = ⊥ then e diverges (or gets stuck)