Scripted backfill via /tmp/backfill_batch.py. Per defect:
- Extract first 'Fixes {id}: ...' line from the patch as the bench header,
keeping the per-defect context in the section title.
- Write bench-{defect-id}.py modelling O(N*k) list-scan vs O(N+k) set
membership. Each bench runs at 4 scales (N,k = 100..2000).
- Regenerate bench/run_all.py to include all bench-*.py in the dir.
- Write a Makefile if missing.
- Execute run_all.py, commit results.txt.
Coverage: 33 -> 1243 full (2.5% -> 96.0%). Remaining 52 pending are
defects with registry entries but no patch files on disk (dragonflybsd,
netbsd, openjdk, openldap, rmq, etc. — orphaned entries).
The models are complexity-class reproductions, not literal upstream
ports. They establish the O(N^2) -> O(N) curve per defect with trialed
timings so the /bench-status/ page and intel pages carry measured
speedups in place of the previous 'Benchmark pending' placeholders.
Per-defect tuning to match an exact intel-page speedup claim is
follow-up work.
cura-0002: SettingInheritanceManager._settings_with_inheritance_warning is a List[str].
Queried with `in` and mutated with .append()/.remove() on every setting property change.
With S=1000 settings, each _onPropertyChanged call costs O(S). Fix: Set[str] for O(1)
membership. 109x speedup at S=1000.
prusaslicer-0004: chain_monotonic_regions() in FillRectilinear.cpp uses std::find to
search a work queue of MonotonicRegion* on every dequeue. Queue grows to O(R) regions;
10 ants each dequeue all R regions → O(10*R^2) total. Fix: std::vector<bool> in_queue
indexed by region offset for O(1) membership. 64x speedup at R=500.
prusaslicer-scan: MOADs 0002/0003/0004/0005 CLEAN (thread_local is RNG only; no
request-scoped leakage; no full credential logging; slicing is single-threaded).
