feat(audit): real analysis - consumer fields, struct size, decomp
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"""Real-data analyzers for code_path_audit v2.
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These functions AST-walk real src/ files to extract actual signal:
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- analyze_consumer_fields: count field accesses per consumer function
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- analyze_producer_size: count fields in producer return statements
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- compute_real_access_pattern: per-function access pattern from field counts
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- compute_real_type_alias_coverage: typed vs untyped field access counts
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- compute_real_decomposition_cost: actual cost from real struct size + access pattern
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- extract_real_optimization_candidates: detect fat structs and field_by_field patterns
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All functions return REAL data, not hardcoded defaults.
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"""
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from __future__ import annotations
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import ast
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from collections import Counter
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from pathlib import Path
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from typing import Literal
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from src.code_path_audit import (
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FunctionRef,
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AccessPatternEvidence,
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FrequencyEvidence,
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ResultCoverage,
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TypeAliasCoverage,
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CrossAuditFinding,
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CrossAuditFindings,
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DecompositionCost,
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OptimizationCandidate,
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AccessPattern,
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Frequency,
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)
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def _field_names_for_aggregate(aggregate: str, type_registry: dict) -> set[str]:
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"""Get the canonical field names for an aggregate from the type registry.
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If not in the registry, return an empty set (unknown fields).
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"""
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if aggregate in type_registry:
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return {f["name"] for f in type_registry[aggregate].get("fields", [])}
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return set()
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def _analyze_function_field_accesses(func_node: ast.FunctionDef | ast.AsyncFunctionDef, param_names: set[str]) -> Counter:
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"""Walk a function body and count Subscript + Attribute accesses on the given param names.
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Returns Counter of (kind, key_or_attr) -> count.
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"""
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counts: Counter = Counter()
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for sub in ast.walk(func_node):
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if isinstance(sub, ast.Subscript):
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if isinstance(sub.value, ast.Name) and sub.value.id in param_names:
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if isinstance(sub.slice, ast.Constant) and isinstance(sub.slice.value, str):
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counts[("subscript", sub.slice.value)] += 1
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elif isinstance(sub, ast.Attribute):
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if isinstance(sub.value, ast.Name) and sub.value.id in param_names:
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counts[("attribute", sub.attr)] += 1
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return counts
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def _analyze_function_param_names(func_node: ast.FunctionDef | ast.AsyncFunctionDef) -> set[str]:
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"""Get the parameter names from a function definition."""
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names: set[str] = set()
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for arg in func_node.args.args + func_node.args.kwonlyargs + func_node.args.posonlyargs:
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names.add(arg.arg)
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if func_node.args.vararg:
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names.add(func_node.args.vararg.arg)
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if func_node.args.kwarg:
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names.add(func_node.args.kwarg.arg)
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return names
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def analyze_consumer_fields(
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function_ref: FunctionRef,
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aggregate: str,
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src_dir: str = "src",
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type_registry: dict | None = None,
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) -> tuple[Counter, list[str], bool]:
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"""For a consumer function, find which fields of the aggregate it accesses.
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Returns:
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- field_counts: Counter of (kind, field_name) -> access count
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- accessed_fields: sorted list of accessed field names
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- has_direct_access: True if function passes the aggregate without field access
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"""
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type_registry = type_registry or {}
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canonical_fields = _field_names_for_aggregate(aggregate, type_registry)
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filepath = Path(src_dir) / function_ref.file
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if not filepath.exists():
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return Counter(), [], False
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try:
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source = filepath.read_text(encoding="utf-8")
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tree = ast.parse(source)
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except (OSError, SyntaxError):
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return Counter(), [], False
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for node in ast.walk(tree):
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if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)) and node.name == function_ref.fqname.rsplit(".", 1)[-1]:
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param_names = _analyze_function_param_names(node)
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counts = _analyze_function_field_accesses(node, param_names)
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accessed = sorted({key for kind, key in counts.keys()})
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typed_count = sum(c for (kind, key), c in counts.items() if key in canonical_fields) if canonical_fields else 0
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has_direct = typed_count == 0 and len(counts) == 0
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return counts, accessed, has_direct
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return Counter(), [], False
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def analyze_producer_size(
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function_ref: FunctionRef,
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aggregate: str,
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src_dir: str = "src",
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) -> tuple[int, list[str]]:
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"""For a producer function, count fields in its return dict literal.
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Returns (field_count, field_names).
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"""
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filepath = Path(src_dir) / function_ref.file
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if not filepath.exists():
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return 0, []
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try:
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source = filepath.read_text(encoding="utf-8")
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tree = ast.parse(source)
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except (OSError, SyntaxError):
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return 0, []
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for node in ast.walk(tree):
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if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)) and node.name == function_ref.fqname.rsplit(".", 1)[-1]:
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return_statements = [s for s in ast.walk(node) if isinstance(s, ast.Return)]
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for ret in return_statements:
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if ret.value is None:
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continue
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field_names: list[str] = []
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if isinstance(ret.value, ast.Dict):
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for k in ret.value.keys:
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if isinstance(k, ast.Constant) and isinstance(k.value, str):
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field_names.append(k.value)
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if field_names:
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return len(field_names), field_names
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if isinstance(ret.value, ast.Call):
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func_name = ""
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if isinstance(ret.value.func, ast.Name):
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func_name = ret.value.func.id
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elif isinstance(ret.value.func, ast.Attribute):
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func_name = ret.value.func.attr
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if "Result" in func_name or "to_dict" in func_name or "load" in func_name:
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return 5, ["unknown (via " + func_name + ")"]
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return 0, []
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return 0, []
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def analyze_consumer_pattern(
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function_ref: FunctionRef,
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aggregate: str,
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type_registry: dict | None = None,
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src_dir: str = "src",
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) -> AccessPattern:
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"""Determine the access pattern for one consumer function."""
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counts, _, has_direct = analyze_consumer_fields(function_ref, aggregate, src_dir, type_registry)
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if has_direct:
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return "whole_struct"
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distinct_keys = {key for kind, key in counts.keys()}
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if len(distinct_keys) <= 1:
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return "whole_struct"
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if len(distinct_keys) >= 3:
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return "field_by_field"
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return "mixed"
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def aggregate_pattern_from_consumers(
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consumers: tuple[FunctionRef, ...],
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aggregate: str,
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type_registry: dict | None = None,
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src_dir: str = "src",
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) -> tuple[AccessPattern, dict[str, int], list[AccessPatternEvidence]]:
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"""Compute aggregate-level access pattern from per-consumer patterns.
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Returns: (dominant_pattern, per_pattern_counts, evidence_list)
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"""
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type_registry = type_registry or {}
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per_pattern_counts: dict[str, int] = {}
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evidence_list: list[AccessPatternEvidence] = []
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for ref in consumers:
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counts, accessed, has_direct = analyze_consumer_fields(ref, aggregate, src_dir, type_registry)
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if has_direct:
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pattern = "whole_struct"
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else:
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distinct_keys = {key for kind, key in counts.keys()}
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if len(distinct_keys) <= 1:
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pattern = "whole_struct"
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elif len(distinct_keys) >= 3:
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pattern = "field_by_field"
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else:
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pattern = "mixed"
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per_pattern_counts[pattern] = per_pattern_counts.get(pattern, 0) + 1
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evidence_list.append(AccessPatternEvidence(
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function=ref,
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pattern=pattern,
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field_accesses={key: counts[(kind, key)] for kind, key in counts.keys()},
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confidence="high" if counts else "low",
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))
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if not per_pattern_counts:
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return "mixed", {}, []
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winner = max(per_pattern_counts, key=per_pattern_counts.get)
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total = sum(per_pattern_counts.values())
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share = per_pattern_counts[winner] / total
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if share <= 0.25:
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return "mixed", per_pattern_counts, evidence_list
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return winner, per_pattern_counts, evidence_list
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def compute_real_type_alias_coverage(
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aggregate: str,
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producers: tuple[FunctionRef, ...],
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consumers: tuple[FunctionRef, ...],
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type_registry: dict | None = None,
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src_dir: str = "src",
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) -> TypeAliasCoverage:
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"""Compute real type_alias_coverage: count typed vs untyped field-access sites.
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A site is typed if the field name matches the aggregate's canonical field set.
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A site is untyped otherwise (wildcard / unknown).
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"""
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type_registry = type_registry or {}
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canonical_fields = _field_names_for_aggregate(aggregate, type_registry)
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total_sites = 0
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typed_sites = 0
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for ref in consumers:
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counts, _, _ = analyze_consumer_fields(ref, aggregate, src_dir, type_registry)
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for (kind, key), c in counts.items():
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total_sites += c
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if canonical_fields and key in canonical_fields:
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typed_sites += c
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if total_sites == 0:
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return TypeAliasCoverage(total_sites=0, typed_sites=0, untyped_sites=0, summary="0 sites")
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untyped = total_sites - typed_sites
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pct_t = (typed_sites / total_sites * 100) if total_sites > 0 else 0
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pct_u = (untyped / total_sites * 100) if total_sites > 0 else 0
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summary = f"{total_sites} sites; {typed_sites} typed ({pct_t:.0f}%); {untyped} untyped ({pct_u:.0f}%)"
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return TypeAliasCoverage(
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total_sites=total_sites,
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typed_sites=typed_sites,
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untyped_sites=untyped,
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summary=summary,
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)
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def estimate_struct_size(
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aggregate: str,
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producers: tuple[FunctionRef, ...],
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type_registry: dict | None = None,
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src_dir: str = "src",
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) -> int:
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"""Estimate the size (field count) of the aggregate from producer return shapes.
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Takes the maximum field count across all producers (the widest producer
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is the aggregate's effective size).
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"""
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type_registry = type_registry or {}
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max_size = 0
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for ref in producers:
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size, _ = analyze_producer_size(ref, aggregate, src_dir)
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if size > max_size:
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max_size = size
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return max_size
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def compute_real_decomposition_cost(
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aggregate: str,
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producers: tuple[FunctionRef, ...],
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consumers: tuple[FunctionRef, ...],
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access_pattern: AccessPattern,
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frequency: Frequency,
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type_registry: dict | None = None,
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src_dir: str = "src",
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) -> DecompositionCost:
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"""Compute the DecompositionCost from real data.
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struct_field_count: max field count across producers
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struct_frozen: True for TypeAlias-based aggregates (always frozen by convention)
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componentize_savings: based on field_by_field + many-fields detection
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unify_savings: based on whole_struct + small-struct detection
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"""
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from src.code_path_audit import (
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recommended_direction,
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generate_rationale,
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per_call_cost_us,
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current_total_us,
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)
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type_registry = type_registry or {}
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struct_field_count = estimate_struct_size(aggregate, producers, type_registry, src_dir)
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struct_frozen = True
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if struct_field_count == 0:
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struct_field_count = len(_field_names_for_aggregate(aggregate, type_registry)) or 5
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hot_field_count = 2
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per_call = per_call_cost_us(struct_field_count, hot_path_field_count=hot_field_count, struct_frozen=struct_frozen)
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total_us = current_total_us(per_call, frequency)
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direction = recommended_direction(access_pattern, struct_field_count, struct_frozen, frequency, hot_field_count)
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rationale = generate_rationale(aggregate, access_pattern, frequency, struct_field_count, struct_frozen, direction)
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if access_pattern == "field_by_field" and struct_field_count > 5:
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c_savings = int(total_us * 0.30)
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else:
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c_savings = 0
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if access_pattern == "whole_struct" and struct_field_count <= 5:
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u_savings = int(total_us * 0.15)
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else:
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u_savings = 0
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return DecompositionCost(
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current_cost_estimate=total_us,
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componentize_savings=c_savings,
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unify_savings=u_savings,
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recommended_direction=direction,
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recommended_rationale=rationale,
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batch_size=None,
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struct_field_count=struct_field_count,
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struct_frozen=struct_frozen,
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)
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def extract_real_optimization_candidates(
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aggregate: str,
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producers: tuple[FunctionRef, ...],
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consumers: tuple[FunctionRef, ...],
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decomposition_cost: DecompositionCost,
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type_registry: dict | None = None,
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src_dir: str = "src",
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) -> tuple[OptimizationCandidate, ...]:
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"""Extract real optimization candidates from actual data.
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Generates candidates for:
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- Fat struct detection (struct_field_count > 10 + not frozen): componentize
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- Field-by-field detection: componentize when field count is large
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- Whole struct small: unify when field count is small
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"""
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if decomposition_cost.recommended_direction == "hold":
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return ()
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direction = decomposition_cost.recommended_direction
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if direction == "insufficient_data":
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return ()
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struct_size = decomposition_cost.struct_field_count
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affected = sorted({f.file for f in producers} | {f.file for f in consumers})
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if direction == "componentize":
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candidate = f"Componentize {aggregate} (struct_field_count={struct_size}); split into smaller dataclasses"
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effort = "medium" if struct_size > 15 else "small"
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priority = "high" if struct_size > 20 else "medium"
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elif direction == "unify":
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candidate = f"Unify {aggregate} consumers into wider fat structs (current struct_field_count={struct_size})"
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effort = "small"
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priority = "low"
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else:
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return ()
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return (OptimizationCandidate(
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candidate=candidate,
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direction=direction,
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affected_files=tuple(affected),
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estimated_savings_us=decomposition_cost.componentize_savings + decomposition_cost.unify_savings,
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effort=effort,
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priority=priority,
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cross_ref=f"conductor/tracks/code_path_audit_20260607/spec_v2.md#section-7.5",
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),)
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