russell@unturf.com
b5b9cce0a1
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.
42 lines
2.5 KiB
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42 lines
2.5 KiB
Text
=== kubernetes-0001: CWE-407: list-scan inside loop in kubernetes-0001 (generic model) ===
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N=100 k=100 : defective=0.103ms fixed=0.004ms speedup=24.4x
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N=500 k=500 : defective=2.561ms fixed=0.025ms speedup=100.5x
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N=1000 k=1000 : defective=10.626ms fixed=0.056ms speedup=190.1x
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N=2000 k=2000 : defective=37.728ms fixed=0.096ms speedup=392.6x
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=== kubernetes-0002: CWE-407: list-scan inside loop in kubernetes-0002 (generic model) ===
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N=100 k=100 : defective=0.084ms fixed=0.003ms speedup=25.2x
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N=500 k=500 : defective=2.254ms fixed=0.020ms speedup=111.7x
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N=1000 k=1000 : defective=8.496ms fixed=0.046ms speedup=183.1x
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N=2000 k=2000 : defective=36.771ms fixed=0.113ms speedup=325.5x
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=== kubernetes-0003: CWE-407: list-scan inside loop in kubernetes-0003 (generic model) ===
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N=100 k=100 : defective=0.102ms fixed=0.004ms speedup=24.8x
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N=500 k=500 : defective=2.501ms fixed=0.024ms speedup=102.8x
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N=1000 k=1000 : defective=9.702ms fixed=0.047ms speedup=204.7x
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N=2000 k=2000 : defective=37.915ms fixed=0.097ms speedup=390.7x
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=== kubernetes-0004: TaintSetDiff — O(T²) quadratic taint membership test in node lifecycle controller ===
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N=100 k=100 : defective=0.084ms fixed=0.003ms speedup=24.3x
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N=500 k=500 : defective=2.113ms fixed=0.021ms speedup=101.2x
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N=1000 k=1000 : defective=8.829ms fixed=0.067ms speedup=131.9x
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N=2000 k=2000 : defective=38.312ms fixed=0.102ms speedup=376.4x
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=== kubernetes-0005: Scheduler TaintToleration Score — O(N×T×L) quadratic toleration scan per scheduling cycle ===
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N=100 k=100 : defective=0.088ms fixed=0.004ms speedup=24.6x
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N=500 k=500 : defective=2.291ms fixed=0.022ms speedup=105.6x
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N=1000 k=1000 : defective=8.538ms fixed=0.047ms speedup=183.5x
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N=2000 k=2000 : defective=36.090ms fixed=0.125ms speedup=288.3x
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=== kubernetes-0006: tainteviction handleNodeUpdate — O(P×T×L) GetMatchingTolerations on every node taint change ===
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N=100 k=100 : defective=0.111ms fixed=0.004ms speedup=25.5x
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N=500 k=500 : defective=2.778ms fixed=0.026ms speedup=108.7x
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N=1000 k=1000 : defective=10.153ms fixed=0.056ms speedup=181.7x
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N=2000 k=2000 : defective=39.998ms fixed=0.097ms speedup=410.7x
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=== kubernetes-0007: CWE-407 — Quadratic exit-code scan in pod failure policy matching ===
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N=100 k=100 : defective=0.258ms fixed=0.004ms speedup=70.4x
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N=500 k=500 : defective=2.207ms fixed=0.022ms speedup=101.6x
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N=1000 k=1000 : defective=9.922ms fixed=0.052ms speedup=189.8x
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N=2000 k=2000 : defective=36.303ms fixed=0.097ms speedup=375.4x
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