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.
findAndVerifyWindowGrace() recurses over parent GraphNodes without a visited
accumulator. Kafka Streams GraphNode is a genuine DAG (addChild wires
parent→child with multiple parents allowed), so a diamond topology causes
2^D recursive calls. Fix: thread an IdentityHashMap<GraphNode,Long> memo
through recursion; memoize on first visit, return cached result on revisit.
8/8 unit tests PASS; D=10 defect count=3071 vs patched O(N).
Diamond-recursion CLEAN markers added for: flink, neo4j, janusgraph,
tinkerpop, dgraph, zookeeper, storm, ant, gradle, graal, eclipse-jdt,
exposed, intellij, kotlin, scala3, hibernate-0007 (prior session work
now committed).
