manual_slop/src/code_path_audit_ssdl.py

"""SSDL analysis for code_path_audit v2.

Translates per-aggregate findings into SSDL (Spec/Sketch Description
Language) sketches + computes "effective codepaths" + suggests
specific defusing techniques per aggregate.

This is the layer that produces real DEDUCTIONS on codebase
organization: not just "this is a fat struct" but "this branch
explosion can be defused by introducing a nil sentinel here".
"""
from __future__ import annotations
import ast
from pathlib import Path
from src.code_path_audit import (
 AggregateProfile,
 FunctionRef,
)


SSDL_PRIMITIVES: dict[str, str] = {
 "I": "Instruction (single unit of computation)",
 "T": "Terminator (returns/exits)",
 "B": "Branch (conditional fork)",
 "M": "Merge (control flow reconverges)",
 "Q": "State Query (reads persistent state)",
 "S": "State Mutation (writes persistent state)",
 "N": "Nil Sentinel (defuses branches)",
}


def _resolve_filepath(fref: FunctionRef, src_dir: str) -> Path | None:
 _p = Path(fref.file)
 filepath = _p if _p.exists() else Path(src_dir) / fref.file
 if not filepath.exists():
  return None
 return filepath


def compute_effective_codepaths(profile: AggregateProfile, src_dir: str = "src") -> int:
 """Compute the effective codepath count for one aggregate.

 Effective codepaths = sum over all consumer functions of
 2^(branch_count_in_function).

 This is the combinatoric explosion metric (Fleury).
 High numbers indicate branch-explosion risk; defusing with
 nil sentinels or immediate-mode caches reduces it to ~1.
 """
 if profile.is_candidate:
  return 0
 total = 0
 for fref in profile.consumers:
  branches = count_branches_in_function(fref, src_dir)
  total += 2 ** branches
 return total


def count_branches_in_function(fref: FunctionRef, src_dir: str = "src") -> int:
 """Count the explicit branch points (if/elif/while/try/for/with) in a function."""
 filepath = _resolve_filepath(fref, src_dir)
 if filepath is None:
  return 0
 try:
  source = filepath.read_text(encoding="utf-8")
  tree = ast.parse(source)
 except (OSError, SyntaxError):
  return 0
 func_name = fref.fqname.rsplit(".", 1)[-1]
 for node in ast.walk(tree):
  if not isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)):
   continue
  if node.name != func_name:
   continue
  count = 0
  for sub in ast.walk(node):
   if isinstance(sub, (ast.If, ast.For, ast.While, ast.With, ast.Try, ast.ExceptHandler)):
    count += 1
   elif isinstance(sub, ast.BoolOp):
    count += len(sub.values) - 1
  return count
 return 0


def detect_nil_check_pattern(fref: FunctionRef, src_dir: str = "src") -> bool:
 """Detect if the function uses `is None` / `== None` / `!= None` checks.

 A nil check is a branch that a nil sentinel could defuse.
 """
 filepath = _resolve_filepath(fref, src_dir)
 if filepath is None:
  return False
 try:
  source = filepath.read_text(encoding="utf-8")
  tree = ast.parse(source)
 except (OSError, SyntaxError):
  return False
 func_name = fref.fqname.rsplit(".", 1)[-1]
 for node in ast.walk(tree):
  if not isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)):
   continue
  if node.name != func_name:
   continue
  for sub in ast.walk(node):
   if not isinstance(sub, ast.Compare):
    continue
   for comparator in sub.comparators:
    if isinstance(comparator, ast.Constant) and comparator.value is None:
     return True
  return False
 return False


def compute_field_access_efficiency(profile: AggregateProfile) -> float:
 """Compute field-access efficiency: ratio of typed accesses to total accesses.

 High efficiency (>0.7) means consumers are using the typed fields directly.
 Low efficiency (<0.3) means consumers are using wildcards or the aggregate
 is being passed through without field use (candidate for immediate-mode).
 """
 if profile.is_candidate:
  return 1.0
 tac = profile.type_alias_coverage
 if tac.total_sites == 0:
  return 0.0
 return tac.typed_sites / tac.total_sites


def suggest_defusing_technique(profile: AggregateProfile, src_dir: str = "src") -> list[dict]:
 """Suggest specific SSDL defusing techniques for this aggregate.

 Returns a list of {technique, location, current_state, recommended_change,
 effective_codepaths_before, effective_codepaths_after}.
 """
 suggestions: list[dict] = []
 if profile.is_candidate:
  return suggestions
 nil_check_count = sum(1 for f in profile.consumers if detect_nil_check_pattern(f, src_dir))
 effective = compute_effective_codepaths(profile, src_dir)
 efficiency = compute_field_access_efficiency(profile)
 branch_count = sum(count_branches_in_function(f, src_dir) for f in profile.consumers)

 if nil_check_count > 0:
  suggestions.append({
   "technique": "Nil Sentinel `[N]`",
   "location": f"{nil_check_count} consumer function{'s' if nil_check_count != 1 else ''} have `is None` / `== None` checks",
   "current_state": f"{nil_check_count} nil-check branches contribute to branch explosion",
   "recommended_change": "Introduce a module-level `NIL_<AGGREGATE>` sentinel whose field accesses return safe defaults. Replace None checks with the sentinel. Collapses 2^branch_count into ~1.",
   "effective_codepaths_before": effective,
   "effective_codepaths_after": max(1, effective - nil_check_count * 2),
  })

 if efficiency < 0.3:
  suggestions.append({
   "technique": "Immediate-Mode Cache `[Q:key] -> [I:FetchCached] -> [T]`",
   "location": f"{profile.name} consumers access {profile.type_alias_coverage.total_sites} sites, only {profile.type_alias_coverage.typed_sites} typed ({efficiency*100:.0f}%)",
   "current_state": "Many consumers use wildcard or defensive access patterns",
   "recommended_change": f"Introduce a `{profile.name.lower()}_cache` keyed lookup. Consumers request by key, get cached value, no field-existence checks. Reduces {profile.type_alias_coverage.total_sites} field-check branches to 1 cache lookup.",
   "effective_codepaths_before": effective,
   "effective_codepaths_after": max(1, profile.type_alias_coverage.total_sites),
  })

 if branch_count > 20:
  suggestions.append({
   "technique": "Generational Handles `[I:ResolveHandle] -> [B:Gen matches?] -> [N|safe]`",
   "location": f"{profile.name} consumers have {branch_count} explicit branch points total",
   "current_state": f"Branch explosion: {branch_count} branches = {effective} effective codepaths",
   "recommended_change": "Wrap the aggregate in a generational handle (index + generation). Validation is one comparison; mismatch returns the nil sentinel. Reduces N lifetime branches to 1 handle validation + sentinel return.",
   "effective_codepaths_before": effective,
   "effective_codepaths_after": len(profile.consumers),
  })

 return suggestions


def render_ssdl_sketch(profile: AggregateProfile, src_dir: str = "src") -> str:
 """Render an SSDL sketch of one aggregate's access pattern.

 The sketch shows:
 - Producers (queries that fetch the aggregate)
 - Consumers (instruction sequences that read the aggregate)
 - Branch points (B)
 - Defusing opportunities (N)
 - Effective codepaths metric
 """
 if profile.is_candidate:
  return f"## SSDL Sketch for {profile.name}\n\n_(placeholder; candidate aggregate)_\n"
 lines: list[str] = [f"## SSDL Sketch for `{profile.name}`", ""]
 lines.append("```")
 lines.append(f"[Q:{profile.name} entry-point] -> [Q:PCG lookup]")
 nil_check_funcs = [f for f in profile.consumers if detect_nil_check_pattern(f, src_dir)]
 branches_total = 0
 for i, fref in enumerate(profile.consumers):
  b = count_branches_in_function(fref, src_dir)
  branches_total += b
  is_nil = fref in nil_check_funcs
  nil_marker = "[B:is None?]" if is_nil else "[B:check]"
  nil_defuse = "[N:safe]" if is_nil else ""
  short_name = fref.fqname.rsplit(".", 1)[-1]
  lines.append(f"  -> [{i+1}: {short_name}] {nil_marker} (branches={b}) {nil_defuse}")
 lines.append("  -> [T:done]")
 lines.append("```")
 lines.append("")
 effective = compute_effective_codepaths(profile, src_dir)
 lines.append(f"**Effective codepaths:** {effective} (sum of 2^branches across {len(profile.consumers)} consumers)")
 lines.append(f"**Total branch points:** {branches_total}")
 lines.append(f"**Nil-check functions:** {len(nil_check_funcs)}")
 lines.append("")
 suggestions = suggest_defusing_technique(profile, src_dir)
 if suggestions:
  lines.append("**Defusing opportunities:**")
  lines.append("")
  for s in suggestions:
   lines.append(f"- **{s['technique']}**: {s['recommended_change']}")
   lines.append(f"  - Effective codepaths: {s['effective_codepaths_before']} -> {s['effective_codepaths_after']}")
 else:
  lines.append("**No SSDL defusing opportunities detected** (the aggregate is already well-structured for data-oriented access).")
 lines.append("")
 return "\n".join(lines)


def render_ssdl_rollup(profiles: tuple[AggregateProfile, ...], src_dir: str = "src") -> str:
 """Render the SSDL rollup (all aggregates + their defusing opportunities)."""
 lines: list[str] = ["# SSDL Analysis Rollup", ""]
 lines.append("Per-aggregate analysis: effective codepaths, branch points, defusing opportunities.")
 lines.append("")
 real_profiles = [p for p in profiles if not p.is_candidate]
 lines.append("## Effective codepaths ranking")
 lines.append("")
 lines.append("| Aggregate | Consumers | Total branches | Effective codepaths | Field efficiency |")
 lines.append("|---|---|---|---|---|")
 ranked = sorted(real_profiles, key=lambda p: -compute_effective_codepaths(p, src_dir))
 for p in ranked:
  ec = compute_effective_codepaths(p, src_dir)
  tc = sum(count_branches_in_function(f, src_dir) for f in p.consumers)
  eff = compute_field_access_efficiency(p) * 100
  lines.append(f"| `{p.name}` | {len(p.consumers)} | {tc} | {ec} | {eff:.0f}% |")
 lines.append("")
 lines.append("## Defusing recommendations (top 10)")
 lines.append("")
 all_suggestions: list[tuple[AggregateProfile, dict]] = []
 for p in real_profiles:
  for s in suggest_defusing_technique(p, src_dir):
   all_suggestions.append((p, s))
 all_suggestions.sort(key=lambda ps: -(ps[1]['effective_codepaths_before'] - ps[1]['effective_codepaths_after']))
 if not all_suggestions:
  lines.append("_(no defusing recommendations detected)_\n")
  return "\n".join(lines)
 for p, s in all_suggestions[:10]:
  lines.append(f"### `{p.name}` - {s['technique']}")
  lines.append("")
  lines.append(f"- **Location:** {s['location']}")
  lines.append(f"- **Current state:** {s['current_state']}")
  lines.append(f"- **Recommended change:** {s['recommended_change']}")
  lines.append(f"- **Effective codepaths:** {s['effective_codepaths_before']} -> {s['effective_codepaths_after']}")
  lines.append("")
 return "\n".join(lines)


def render_organization_deductions(profiles: tuple[AggregateProfile, ...], src_dir: str = "src") -> str:
 """Render the organization deductions rollup.

 Cross-aggregate view of codebase organization. Based on SSDL principles:
 - Well-organized: few branches, high field efficiency, few effective codepaths
 - Needs restructuring: many branches, low efficiency, branch-explosion risk
 """
 lines: list[str] = ["# Organization Deductions", ""]
 lines.append("Cross-aggregate view of codebase organization. Verdicts derived from SSDL analysis:")
 lines.append("- **well-organized**: <=50 effective codepaths AND >=50% field efficiency")
 lines.append("- **moderate**: between the two thresholds")
 lines.append("- **needs restructuring**: >200 effective codepaths OR <20% field efficiency")
 lines.append("")
 real_profiles = [p for p in profiles if not p.is_candidate]

 lines.append("## Module organization observations")
 lines.append("")
 lines.append("### Files with most cross-aggregate involvement")
 lines.append("")
 file_agg: dict[str, set[str]] = {}
 file_consumers: dict[str, set[str]] = {}
 for p in real_profiles:
  for f in p.producers:
   file_agg.setdefault(f.file, set()).add(p.name)
  for f in p.consumers:
   file_consumers.setdefault(f.file, set()).add(p.name)
 rows: list[tuple[str, int, int]] = []
 for f in sorted(file_agg.keys()):
  rows.append((f, len(file_agg[f]), len(file_consumers.get(f, set()))))
 rows.sort(key=lambda r: -(r[1] + r[2]))
 lines.append("| file | aggregates produced | aggregates consumed |")
 lines.append("|---|---|---|")
 for f, pc, cc in rows[:15]:
  lines.append(f"| `{f}` | {pc} | {cc} |")
 lines.append("")
 lines.append("### Files with high coupling (producers + consumers >= 8)")
 lines.append("")
 lines.append("These files are the central nervous system of the codebase. Changes ripple across the most aggregates.")
 lines.append("")
 lines.append("| file | coupling score (producers + consumers) |")
 lines.append("|---|---|")
 high_coupling = [(f, pc, cc) for f, pc, cc in rows if (pc + cc) >= 8]
 for f, pc, cc in high_coupling:
  lines.append(f"| `{f}` | {pc + cc} (high) |")
 lines.append("")

 lines.append("## Per-aggregate organization verdict")
 lines.append("")
 lines.append("| Aggregate | Verdict | Notes |")
 lines.append("|---|---|---|")
 verdict_counts = {"well-organized": 0, "moderate": 0, "needs restructuring": 0}
 for p in real_profiles:
  ec = compute_effective_codepaths(p, src_dir)
  eff = compute_field_access_efficiency(p) * 100
  nil_count = sum(1 for f in p.consumers if detect_nil_check_pattern(f, src_dir))
  if ec <= 50 and eff >= 50:
   verdict = "well-organized"
  elif ec > 200 or eff < 20:
   verdict = "needs restructuring"
  else:
   verdict = "moderate"
  verdict_counts[verdict] += 1
  notes: list[str] = []
  if nil_count > 0:
   notes.append(f"{nil_count} nil checks")
  if eff < 50:
   notes.append(f"{eff:.0f}% field efficiency")
  if ec > 100:
   notes.append(f"{ec} effective codepaths")
  note_str = "; ".join(notes) if notes else "no major issues"
  lines.append(f"| `{p.name}` | {verdict} | {note_str} |")
 lines.append("")
 lines.append(f"**Tally:** {verdict_counts['well-organized']} well-organized, {verdict_counts['moderate']} moderate, {verdict_counts['needs restructuring']} needs restructuring")
 lines.append("")

 lines.append("## Restructuring routes (prioritized)")
 lines.append("")
 priority_routes = []
 for p in real_profiles:
  ec = compute_effective_codepaths(p, src_dir)
  eff = compute_field_access_efficiency(p)
  if ec > 100 or eff < 0.3:
   priority_routes.append((p, ec, eff))
 priority_routes.sort(key=lambda r: -r[1])
 if priority_routes:
  lines.append("Top restructuring routes (by effective codepath count):")
  lines.append("")
  for i, (p, ec, eff) in enumerate(priority_routes[:5], 1):
   nil_count = sum(1 for f in p.consumers if detect_nil_check_pattern(f, src_dir))
   lines.append(f"{i}. **`{p.name}`**: {ec} effective codepaths ({eff*100:.0f}% field efficiency)")
   lines.append(f"   - Apply nil sentinel to {nil_count} nil-check functions")
   lines.append(f"   - Migrate to immediate-mode cache for {p.type_alias_coverage.total_sites} field-access sites")
 else:
  lines.append("_(no high-priority restructuring routes; all aggregates have moderate effective codepath counts)_")
 lines.append("")
 return "\n".join(lines)