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.
50 lines
1.4 KiB
Python
50 lines
1.4 KiB
Python
#!/usr/bin/env python3
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# bench-ruby-0003.py
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# RubyGems Gem::Specification#dependent_gems — O(N²×D) nested scan
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# Models O(N*k) -> O(N+k) via linear-scan membership inside a loop vs set/dict.
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import sys
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import time
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def bench_defective(n, k):
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pool = list(range(k))
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items = list(range(n))
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t0 = time.perf_counter()
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seen = []
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for x in items:
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if x not in pool: # O(k)
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seen.append(x)
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return time.perf_counter() - t0
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def bench_fixed(n, k):
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pool_set = set(range(k))
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items = list(range(n))
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t0 = time.perf_counter()
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seen = []
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for x in items:
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if x not in pool_set: # O(1)
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seen.append(x)
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return time.perf_counter() - t0
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TRIALS = 3
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CASES = [(100, 100), (500, 500), (1000, 1000), (2000, 2000)]
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def run():
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lines = []
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header = "=== ruby-0003: RubyGems Gem::Specification#dependent_gems — O(N²×D) nested scan ==="
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print(header); lines.append(header)
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for n, k in CASES:
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df = min(bench_defective(n, k) for _ in range(TRIALS))
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fx = min(bench_fixed(n, k) for _ in range(TRIALS))
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speedup = (df / fx) if fx > 0 else float("inf")
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line = f"N={n:<5} k={k:<5}: defective={df*1000:.3f}ms fixed={fx*1000:.3f}ms speedup={speedup:.1f}x"
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print(line); lines.append(line); sys.stdout.flush()
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return lines
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if __name__ == "__main__":
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run()
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