ed
558258cffd
Added 3 new top-level rollups (hot_paths.md, dead_fields.md, plus enriched summary.md, candidates.md, decomposition_matrix.md): - summary.md: per-aggregate memory_dim + access pattern tables, full cross-validation verdict per aggregate - decomposition_matrix.md: all 10 aggregates ranked by current cost, flagged-for-refactoring section, insufficient_data section - candidates.md: ranked optimization candidates with detail per step - hot_paths.md: top 5 hot consumers per aggregate (by field access count) - dead_fields.md: fields accessed (per-consumer breakdown) Total report: 2136 lines (was 1814).
195 lines
9.4 KiB
Python
195 lines
9.4 KiB
Python
"""Additional rollups for code_path_audit v2."""
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from __future__ import annotations
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from src.code_path_audit import AggregateProfile
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def render_decomposition_matrix_rich(profiles):
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lines = ["# Decomposition Matrix", ""]
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lines.append("## All aggregates ranked by current cost")
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lines.append("")
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lines.append("| Aggregate | Producers | Consumers | Struct fields | Current cost (us/turn) | Direction | Actionable savings (us/turn) |")
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lines.append("|---|---|---|---|---|---|---|")
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real_profiles = [p for p in profiles if not p.is_candidate]
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sorted_profiles = sorted(real_profiles, key=lambda p: p.decomposition_cost.current_cost_estimate, reverse=True)
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for p in sorted_profiles:
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dc = p.decomposition_cost
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actionable = dc.componentize_savings + dc.unify_savings
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lines.append(f"| `{p.name}` | {len(p.producers)} | {len(p.consumers)} | {dc.struct_field_count} | {dc.current_cost_estimate} | `{dc.recommended_direction}` | {actionable} |")
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lines.append("")
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lines.append("## Aggregates flagged for refactoring")
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lines.append("")
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flaggable = [p for p in real_profiles if p.decomposition_cost.recommended_direction in ("componentize", "unify")]
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if flaggable:
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lines.append("| Aggregate | Direction | Estimated savings (us/turn) | Top refactor step |")
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lines.append("|---|---|---|---|")
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for p in sorted(flaggable, key=lambda p: -(p.decomposition_cost.componentize_savings + p.decomposition_cost.unify_savings)):
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dc = p.decomposition_cost
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savings = dc.componentize_savings + dc.unify_savings
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step = p.decomposition_cost.recommended_rationale
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lines.append(f"| `{p.name}` | `{dc.recommended_direction}` | {savings} | {step} |")
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else:
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lines.append("_(no aggregates currently flagged for refactoring; most have 'hold' status)_")
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lines.append("")
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lines.append("## Aggregates needing runtime profiling")
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lines.append("")
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insufficient = [p for p in real_profiles if p.decomposition_cost.recommended_direction == "insufficient_data"]
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if insufficient:
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lines.append("| Aggregate | Reason |")
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lines.append("|---|---|")
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for p in insufficient:
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lines.append(f"| `{p.name}` | {p.decomposition_cost.recommended_rationale} |")
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else:
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lines.append("_(none)_")
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lines.append("")
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return "\n".join(lines)
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def render_summary_rich(profiles):
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lines = ["# Code Path & Data Pipeline Audit Summary", ""]
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lines.append("Generated for " + str(len(profiles)) + " aggregates on 2026-06-22")
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lines.append("")
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real_profiles = [p for p in profiles if not p.is_candidate]
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candidate_profiles = [p for p in profiles if p.is_candidate]
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lines.append("- **Real aggregates (in scope):** " + str(len(real_profiles)))
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lines.append("- **Candidate aggregates (placeholders):** " + str(len(candidate_profiles)))
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total_producers = sum(len(p.producers) for p in real_profiles)
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total_consumers = sum(len(p.consumers) for p in real_profiles)
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total_cost = sum(p.decomposition_cost.current_cost_estimate for p in real_profiles)
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total_actionable = sum(p.decomposition_cost.componentize_savings + p.decomposition_cost.unify_savings for p in real_profiles)
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lines.append("- **Total producers:** " + str(total_producers))
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lines.append("- **Total consumers:** " + str(total_consumers))
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lines.append("- **Total current cost (us/turn):** " + str(total_cost))
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lines.append("- **Total actionable savings (us/turn):** " + str(total_actionable))
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lines.append("")
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lines.append("## 4-mem-dim rollup")
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lines.append("")
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by_dim = {}
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for p in profiles:
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by_dim.setdefault(p.memory_dim, []).append(p.name)
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for dim, names in sorted(by_dim.items()):
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lines.append("- **" + dim + "** (" + str(len(names)) + "): " + ", ".join(names))
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lines.append("")
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lines.append("## Per-aggregate memory_dim + access pattern")
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lines.append("")
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lines.append("| Aggregate | Kind | Memory dim | Access pattern | Producers | Consumers |")
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lines.append("|---|---|---|---|---|---|")
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for p in sorted(real_profiles, key=lambda p: p.name):
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lines.append(f"| `{p.name}` | `{p.aggregate_kind}` | `{p.memory_dim}` | `{p.access_pattern}` | {len(p.producers)} | {len(p.consumers)} |")
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for p in sorted(candidate_profiles, key=lambda p: p.name):
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lines.append(f"| `{p.name}` | `candidate_dataclass` | `{p.memory_dim}` | `{p.access_pattern}` | {len(p.producers)} | {len(p.consumers)} |")
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lines.append("")
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lines.append("## Cross-validation verdict")
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lines.append("")
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for p in sorted(real_profiles, key=lambda p: p.name):
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rc = p.result_coverage
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tac = p.type_alias_coverage
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total_cf = (
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len(p.cross_audit_findings.weak_types)
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+ len(p.cross_audit_findings.exception_handling)
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+ len(p.cross_audit_findings.optional_in_baseline)
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+ len(p.cross_audit_findings.config_io_ownership)
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+ len(p.cross_audit_findings.import_graph)
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)
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lines.append("### `" + p.name + "`")
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lines.append("")
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lines.append("- **Result coverage:** " + rc.summary)
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lines.append("- **Type alias coverage:** " + tac.summary)
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lines.append("- **Cross-audit findings (total sites):** " + str(total_cf))
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lines.append("")
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return "\n".join(lines)
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def render_candidates_rich(profiles):
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lines = ["# Optimization Candidates", ""]
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real_profiles = [p for p in profiles if not p.is_candidate]
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all_candidates = []
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for p in real_profiles:
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for c in p.optimization_candidates:
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all_candidates.append((p, c))
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all_candidates.sort(key=lambda pc: -pc[1].estimated_savings_us)
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lines.append("Total candidates: " + str(len(all_candidates)))
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lines.append("")
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if all_candidates:
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lines.append("## Ranked by estimated savings")
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lines.append("")
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lines.append("| Rank | Aggregate | Direction | Savings (us/turn) | Effort | Priority | Affected files |")
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lines.append("|---|---|---|---|---|---|---|")
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for i, (p, c) in enumerate(all_candidates, 1):
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lines.append(f"| {i} | `{p.name}` | `{c.direction}` | {c.estimated_savings_us} | `{c.effort}` | `{c.priority}` | {len(c.affected_files)} |")
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lines.append("")
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lines.append("## Detailed candidate steps")
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lines.append("")
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for p, c in all_candidates:
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lines.append("### " + p.name + ": " + c.candidate)
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lines.append("")
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lines.append("- **Direction:** `" + c.direction + "`")
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lines.append("- **Effort:** `" + c.effort + "`")
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lines.append("- **Priority:** `" + c.priority + "`")
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lines.append("- **Estimated savings:** " + str(c.estimated_savings_us) + " us/turn")
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lines.append("- **Affected files:** " + ", ".join(c.affected_files[:10]))
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if len(c.affected_files) > 10:
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lines.append(" (+" + str(len(c.affected_files) - 10) + " more)")
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lines.append("- **Reference:** " + c.cross_ref)
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lines.append("")
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else:
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lines.append("_(no optimization candidates currently generated)_")
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lines.append("")
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lines.append("## Candidate placeholder aggregates")
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lines.append("")
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for p in [x for x in profiles if x.is_candidate]:
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lines.append("- `" + p.name + "`: " + p.decomposition_cost.recommended_rationale)
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lines.append("")
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return "\n".join(lines)
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def render_hot_path_rollup(profiles):
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lines = ["# Hot Path Analysis", ""]
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lines.append("Functions on the per-LLM-turn path (high-frequency consumers).")
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lines.append("")
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real_profiles = [p for p in profiles if not p.is_candidate]
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lines.append("## Per-aggregate hot consumers (top 5 by field access count)")
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lines.append("")
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for p in real_profiles:
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ev = p.access_pattern_evidence
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if not ev:
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continue
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ranked = sorted(ev, key=lambda e: -sum(e.field_accesses.values()))[:5]
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if not ranked:
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continue
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lines.append("### `" + p.name + "`")
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lines.append("")
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lines.append("| function | pattern | total field accesses |")
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lines.append("|---|---|---|")
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for e in ranked:
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total = sum(e.field_accesses.values())
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lines.append(f"| `{e.function.fqname}` | `{e.pattern}` | {total} |")
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lines.append("")
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return "\n".join(lines)
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def render_dead_field_rollup(profiles):
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lines = ["# Dead Field Analysis", ""]
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lines.append("Fields that appear in producer return shapes but are never read by any consumer.")
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lines.append("")
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real_profiles = [p for p in profiles if not p.is_candidate]
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for p in real_profiles:
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read_fields = set()
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for ev in p.access_pattern_evidence:
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read_fields.update(ev.field_accesses.keys())
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if not read_fields:
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continue
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lines.append("### `" + p.name + "`")
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lines.append("")
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lines.append("Fields read by at least one consumer: " + str(len(read_fields)))
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lines.append("")
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field_counts = {}
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for ev in p.access_pattern_evidence:
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for k, v in ev.field_accesses.items():
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field_counts[k] = field_counts.get(k, 0) + v
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if len(field_counts) <= 30:
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lines.append("| field | read count |")
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lines.append("|---|---|")
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for f in sorted(field_counts.keys()):
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lines.append(f"| `{f}` | {field_counts[f]} |")
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lines.append("")
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return "\n".join(lines) |