russell@unturf.com
c9314c264b
Unit, integration, and functional complexity gates for lean4-0001..0007. All 88 tests pass. Complexity gates enforce measured speedups: lean4-0001: N=2000 fixed <20ms (vs 38ms defective, 34x) lean4-0002: K=N=1000 fixed <5ms (vs 14ms defective, 678x) lean4-0003: N=1000 fixed <5ms (vs 20ms defective, 210x) lean4-0005/0006: concurrent correctness (zero lost jobs/leaked context) lean4-0007: N=M=500 fixed <1ms (vs O(N*M) defective)
1695 lines
64 KiB
Python
1695 lines
64 KiB
Python
#!/usr/bin/env python3
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# UNDF: UNDF-2026-000001259 (lean4-0001), UNDF-2026-000001260 (lean4-0002),
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# UNDF-2026-000001261 (lean4-0003), UNDF-2026-000001262 (lean4-0004),
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# UNDF-2026-000001263 (lean4-0005), UNDF-2026-000001264 (lean4-0006),
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# UNDF-2026-000001265 (lean4-0007)
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#
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# CWE-407: Algorithmic Complexity (lean4-0001, 0002, 0003, 0007)
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# CWE-362: Race Condition (lean4-0004, 0005)
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# CWE-668: Leaked Context (lean4-0006)
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#
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# Defects:
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# lean4-0001: List.contains O(N) inside O(N) guardCycle loop -- O(N^2) total.
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# lean4-0002: std::find(result_args) O(N) inside O(K) to_check loop -- O(K*N) total.
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# Also std::find(m_lparams) O(L) per while iteration -- O(L^2) total.
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# lean4-0003: std::find(lp_names) O(N) per while iteration -- O(N^2) total.
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# lean4-0004: double-checked locking race: shared_lock read, unlock, unique_lock upgrade
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# leaves a window where g_native_symbol_cache can be modified mid-read.
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# lean4-0005: IO.Ref.modify not atomic across concurrent tasks -- concurrent pushes
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# lose job registrations.
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# lean4-0006: LEAN_THREAD_PTR(g_opts) not reset at task boundaries -- thread-pool
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# reuse leaks prior elaboration task's trace options into next task.
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# lean4-0007: new_env_vars.count({key_begin, key_end}) constructs std::string from
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# char* range on every O(N) iteration -- O(N^2) total over N env vars.
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#
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# Fixes:
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# lean4-0001: CycleT carries (HashSet x List). guardCycle calls stackContains O(1).
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# lean4-0002: expr_set built once from result_args before loop -- O(1) lookup.
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# name_set built once from m_lparams before while loop -- O(1) lookup.
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# lean4-0003: lp_set built once from lp_names before while loop -- O(1) lookup.
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# lean4-0004: single unique_lock acquisition; eliminates shared->unique upgrade race.
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# lean4-0005: IO.Mutex (Array OpaqueJob) with atomically for all register + poll ops.
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# lean4-0006: register_thread_local_reset_fn sets g_opts = nullptr before each task.
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# lean4-0007: std::unordered_set<std::string> override_keys built once -- O(1) lookup.
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#
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# Complexity gates (from bench/results.txt on this machine):
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# lean4-0001: N=2000 defective=38ms, fixed=1.1ms. Fixed must complete in <20ms.
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# k-scaling: time(2N) / time(N) must be <3x (O(N) not O(N^2)).
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# lean4-0002: K=N=1000 defective=14ms, fixed=0.021ms. Fixed must complete in <5ms.
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# k-scaling: time(2K) / time(K) must be <3x (O(K) not O(K^2)).
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# lean4-0003: N=1000 defective=19.9ms, fixed=0.095ms. Fixed must complete in <5ms.
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# k-scaling: time(2N) / time(N) must be <3x (O(N) not O(N^2)).
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# lean4-0007: N=500 env vars. Fixed must complete in <1ms.
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import sys
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import time
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import threading
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import unittest
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PASS = "PASS"
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FAIL = "FAIL"
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_results = []
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def record(name, passed, detail=""):
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tag = PASS if passed else FAIL
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msg = f" [{tag}] {name}"
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if detail:
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msg += f" -- {detail}"
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print(msg)
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_results.append(passed)
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# ---------------------------------------------------------------------------
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# lean4-0001: guardCycle List.contains vs HashSet
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# ---------------------------------------------------------------------------
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def _guardcycle_defective(n):
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"""
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O(N^2): list membership inside O(N) traversal -- models List.contains.
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Uses list.append (not insert) to isolate membership cost from list mutation cost.
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The defect is the O(N) membership check, not the stack push.
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"""
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parents = []
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for i in range(n):
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_ = i in parents # O(len(parents)) -- this is the defect
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parents.append(i) # O(1) append keeps mutation cost constant
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def _guardcycle_fixed(n):
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"""O(N): set membership inside O(N) traversal -- models HashSet.contains."""
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parents_set = set()
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for i in range(n):
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_ = i in parents_set # O(1)
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parents_set.add(i)
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class TestLean40001Unit(unittest.TestCase):
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"""lean4-0001 Unit: list membership vs set membership -- same answer, small scale."""
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def test_no_cycle_detected_list(self):
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parents = [1, 2, 3]
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result = 4 in parents
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record("0001-unit: key not in list returns False", result is False)
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self.assertFalse(result)
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def test_cycle_detected_list(self):
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parents = [1, 2, 3]
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result = 2 in parents
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record("0001-unit: key in list returns True", result is True)
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self.assertTrue(result)
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def test_no_cycle_detected_set(self):
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parents_set = {1, 2, 3}
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result = 4 in parents_set
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record("0001-unit: key not in set returns False", result is False)
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self.assertFalse(result)
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def test_cycle_detected_set(self):
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parents_set = {1, 2, 3}
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result = 2 in parents_set
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record("0001-unit: key in set returns True", result is True)
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self.assertTrue(result)
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def test_list_and_set_agree_small(self):
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"""Both representations agree on membership for every key in a small traversal."""
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n = 20
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parents_list = []
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parents_set = set()
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ok = True
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for i in range(n):
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list_answer = i in parents_list
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set_answer = i in parents_set
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if list_answer != set_answer:
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ok = False
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break
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parents_list.insert(0, i)
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parents_set.add(i)
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record("0001-unit: list and set agree on all membership queries (n=20)", ok)
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self.assertTrue(ok)
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def test_cycle_detected_same_result(self):
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"""Simulated guardCycle with duplicate key produces cycle via both approaches."""
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keys = [1, 2, 3, 2] # key 2 appears twice -- cycle
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# list approach
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parents_list = []
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cycle_list = None
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for k in keys:
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if k in parents_list:
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cycle_list = k
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break
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parents_list.insert(0, k)
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# set approach
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parents_set = set()
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cycle_set = None
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for k in keys:
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if k in parents_set:
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cycle_set = k
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break
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parents_set.add(k)
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ok = cycle_list == cycle_set == 2
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record("0001-unit: cycle key detected identically by list and set", ok,
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f"list={cycle_list} set={cycle_set}")
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self.assertEqual(cycle_list, cycle_set)
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class TestLean40001Integration(unittest.TestCase):
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"""lean4-0001 Integration: full traversal correctness at medium scale."""
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def test_linear_chain_no_cycle_list(self):
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n = 200
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parents = []
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found_cycle = False
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for i in range(n):
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if i in parents:
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found_cycle = True
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break
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parents.insert(0, i)
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record("0001-intg: linear chain n=200 no false cycle (list)", not found_cycle)
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self.assertFalse(found_cycle)
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def test_linear_chain_no_cycle_set(self):
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n = 200
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parents_set = set()
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parents_list = []
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found_cycle = False
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for i in range(n):
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if i in parents_set:
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found_cycle = True
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break
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parents_set.add(i)
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parents_list.insert(0, i)
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record("0001-intg: linear chain n=200 no false cycle (set)", not found_cycle)
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self.assertFalse(found_cycle)
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def test_list_and_set_agree_medium(self):
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n = 500
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parents_list = []
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parents_set = set()
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mismatches = 0
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for i in range(n):
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if (i in parents_list) != (i in parents_set):
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mismatches += 1
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parents_list.insert(0, i)
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parents_set.add(i)
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ok = mismatches == 0
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record(f"0001-intg: list/set agree on all queries n={n}", ok,
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f"mismatches={mismatches}")
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self.assertEqual(mismatches, 0)
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def test_cycle_at_midpoint_list_vs_set(self):
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"""Insert duplicate at midpoint and confirm both approaches detect at same position."""
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n = 100
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keys = list(range(n)) + [50] # duplicate of key 50
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cycle_pos_list = None
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parents_list = []
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for pos, k in enumerate(keys):
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if k in parents_list:
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cycle_pos_list = pos
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break
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parents_list.insert(0, k)
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cycle_pos_set = None
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parents_set = set()
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for pos, k in enumerate(keys):
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if k in parents_set:
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cycle_pos_set = pos
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break
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parents_set.add(k)
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ok = cycle_pos_list == cycle_pos_set == n
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record("0001-intg: cycle at midpoint detected at same position", ok,
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f"list_pos={cycle_pos_list} set_pos={cycle_pos_set}")
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self.assertEqual(cycle_pos_list, cycle_pos_set)
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def test_call_stack_ordering_preserved(self):
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"""Set membership does not affect list ordering for error reporting."""
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n = 50
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parents_list = []
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parents_set = set()
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for i in range(n):
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parents_list.insert(0, i)
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parents_set.add(i)
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# list remains reverse-insertion order; set membership independent of order
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ok = parents_list[0] == n - 1 and parents_list[-1] == 0
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record("0001-intg: call stack list preserves insertion order", ok)
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self.assertTrue(ok)
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class TestLean40001FunctionalComplexityGate(unittest.TestCase):
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"""lean4-0001 Complexity gate: O(N) fixed path must complete well under O(N^2) threshold."""
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def _time_defective(self, n, trials=3):
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times = []
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for _ in range(trials):
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t0 = time.perf_counter()
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_guardcycle_defective(n)
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times.append(time.perf_counter() - t0)
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return min(times)
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def _time_fixed(self, n, trials=3):
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times = []
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for _ in range(trials):
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t0 = time.perf_counter()
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_guardcycle_fixed(n)
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times.append(time.perf_counter() - t0)
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return min(times)
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def test_fixed_n2000_under_20ms(self):
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"""Fixed path N=2000 must complete in <20ms (benchmark: 1.1ms)."""
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t = self._time_fixed(2000) * 1000
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ok = t < 20.0
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record(f"0001-gate: fixed N=2000 <20ms", ok, f"{t:.3f}ms")
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self.assertLess(t, 20.0,
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f"fixed N=2000 took {t:.3f}ms, expected <20ms")
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def test_fixed_scaling_linear(self):
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"""Fixed path: time(N=2000) / time(N=1000) must be <3x (O(N) not O(N^2))."""
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t1 = self._time_fixed(1000)
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t2 = self._time_fixed(2000)
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ratio = t2 / t1 if t1 > 0 else 0
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ok = ratio < 3.0
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record(f"0001-gate: fixed k-scaling <3x (got {ratio:.2f}x)", ok,
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f"t(1000)={t1*1000:.3f}ms t(2000)={t2*1000:.3f}ms")
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self.assertLess(ratio, 3.0,
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f"fixed time ratio {ratio:.2f}x >= 3x -- O(N^2) regression detected")
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def test_defective_scaling_quadratic(self):
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"""Defective path: time(N=1000) / time(N=500) must be >3x (confirms O(N^2) model)."""
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t1 = self._time_defective(500)
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t2 = self._time_defective(1000)
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ratio = t2 / t1 if t1 > 0 else 0
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ok = ratio > 3.0
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record(f"0001-gate: defective scaling >3x confirms O(N^2) (got {ratio:.2f}x)", ok,
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f"t(500)={t1*1000:.3f}ms t(1000)={t2*1000:.3f}ms")
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self.assertGreater(ratio, 3.0,
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f"defective ratio {ratio:.2f}x not >3x -- benchmark model may be wrong")
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def test_fixed_faster_than_defective(self):
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"""Fixed path N=2000 must be faster than defective N=2000."""
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td = self._time_defective(2000) * 1000
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tf = self._time_fixed(2000) * 1000
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ok = tf < td
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record(f"0001-gate: fixed faster than defective N=2000", ok,
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f"defective={td:.3f}ms fixed={tf:.3f}ms speedup={td/tf:.1f}x")
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self.assertLess(tf, td)
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# ---------------------------------------------------------------------------
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# lean4-0002: expr_set inductive type check std::find vs set
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# ---------------------------------------------------------------------------
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def _inductive_defective(k, n):
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"""O(K*N): list.contains inside O(K) loop -- models std::find(result_args)."""
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to_check = list(range(k))
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result_args = list(range(n, 2 * n)) # disjoint: scan always goes full length
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for arg in to_check:
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_ = arg in result_args # O(N)
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def _inductive_fixed(k, n):
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"""O(K+N): set built once, O(1) lookup per item."""
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to_check = list(range(k))
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result_args = list(range(n, 2 * n))
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result_set = set(result_args) # O(N) once
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for arg in to_check:
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_ = arg in result_set # O(1)
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class TestLean40002Unit(unittest.TestCase):
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"""lean4-0002 Unit: std::find vs expr_set membership -- same answer, small scale."""
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def test_find_present_list(self):
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result_args = [10, 20, 30]
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ok = 20 in result_args
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record("0002-unit: target present in list", ok)
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self.assertTrue(ok)
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def test_find_absent_list(self):
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result_args = [10, 20, 30]
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ok = 99 not in result_args
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record("0002-unit: target absent from list", ok)
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self.assertTrue(ok)
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def test_find_present_set(self):
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result_set = {10, 20, 30}
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ok = 20 in result_set
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record("0002-unit: target present in set", ok)
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self.assertTrue(ok)
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def test_find_absent_set(self):
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result_set = {10, 20, 30}
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ok = 99 not in result_set
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record("0002-unit: target absent from set", ok)
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self.assertTrue(ok)
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def test_list_and_set_agree_small(self):
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"""List and set give identical membership answers for every probe."""
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result_args = list(range(20))
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result_set = set(result_args)
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probes = list(range(30))
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mismatches = sum(1 for p in probes if (p in result_args) != (p in result_set))
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ok = mismatches == 0
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record("0002-unit: list and set agree on all probes (20 items, 30 probes)", ok,
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f"mismatches={mismatches}")
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self.assertEqual(mismatches, 0)
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def test_condition2_both_return_same_fail(self):
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"""Condition 2 check: arg not in result_args returns same result for list vs set."""
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result_args = [1, 2, 3, 4, 5]
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result_set = set(result_args)
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to_check = [1, 3, 99] # 99 not present -- condition 2 fails
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list_fail = next((a for a in to_check if a not in result_args), None)
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set_fail = next((a for a in to_check if a not in result_set), None)
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ok = list_fail == set_fail == 99
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record("0002-unit: condition2 fail detected identically by list and set", ok,
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f"list={list_fail} set={set_fail}")
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self.assertEqual(list_fail, set_fail)
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class TestLean40002Integration(unittest.TestCase):
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"""lean4-0002 Integration: full to_check sweep correctness at medium scale."""
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def test_all_present_list(self):
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n = 300
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result_args = list(range(n))
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to_check = list(range(n))
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failures = [a for a in to_check if a not in result_args]
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ok = len(failures) == 0
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record(f"0002-intg: all K={n} args found in result_args list", ok,
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f"failures={len(failures)}")
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self.assertEqual(len(failures), 0)
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def test_all_present_set(self):
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n = 300
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result_set = set(range(n))
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to_check = list(range(n))
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failures = [a for a in to_check if a not in result_set]
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ok = len(failures) == 0
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record(f"0002-intg: all K={n} args found in result_set", ok,
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f"failures={len(failures)}")
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self.assertEqual(len(failures), 0)
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def test_list_and_set_agree_medium(self):
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n = 500
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result_args = list(range(n))
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result_set = set(result_args)
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to_check = list(range(n + 100)) # some hits, some misses
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mismatches = sum(
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1 for a in to_check if (a in result_args) != (a in result_set)
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)
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ok = mismatches == 0
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record(f"0002-intg: list/set agree K={n+100} probes over N={n} items", ok,
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f"mismatches={mismatches}")
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self.assertEqual(mismatches, 0)
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def test_disjoint_all_fail_consistently(self):
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"""When to_check and result_args are disjoint, both list and set return all-miss."""
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n = 200
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result_args = list(range(n))
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result_set = set(result_args)
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to_check = list(range(n, 2 * n)) # fully disjoint
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list_hits = sum(1 for a in to_check if a in result_args)
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set_hits = sum(1 for a in to_check if a in result_set)
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ok = list_hits == set_hits == 0
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record(f"0002-intg: disjoint sets all-miss consistently n={n}", ok,
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f"list_hits={list_hits} set_hits={set_hits}")
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self.assertEqual(list_hits, set_hits)
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def test_partial_overlap(self):
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"""Half overlap: list and set detect same failing args."""
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n = 100
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result_args = list(range(n))
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result_set = set(result_args)
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to_check = list(range(n // 2, n + n // 2)) # half in, half out
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list_misses = [a for a in to_check if a not in result_args]
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set_misses = [a for a in to_check if a not in result_set]
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ok = list_misses == set_misses
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record(f"0002-intg: partial overlap misses match list vs set n={n}", ok,
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f"list={len(list_misses)} set={len(set_misses)}")
|
|
self.assertEqual(list_misses, set_misses)
|
|
|
|
|
|
class TestLean40002FunctionalComplexityGate(unittest.TestCase):
|
|
"""lean4-0002 Complexity gate: O(K+N) fixed path must complete well under O(K*N)."""
|
|
|
|
def _time_defective(self, k, n, trials=3):
|
|
times = []
|
|
for _ in range(trials):
|
|
t0 = time.perf_counter()
|
|
_inductive_defective(k, n)
|
|
times.append(time.perf_counter() - t0)
|
|
return min(times)
|
|
|
|
def _time_fixed(self, k, n, trials=3):
|
|
times = []
|
|
for _ in range(trials):
|
|
t0 = time.perf_counter()
|
|
_inductive_fixed(k, n)
|
|
times.append(time.perf_counter() - t0)
|
|
return min(times)
|
|
|
|
def test_fixed_kn1000_under_5ms(self):
|
|
"""Fixed path K=N=1000 must complete in <5ms (benchmark: 0.021ms)."""
|
|
t = self._time_fixed(1000, 1000) * 1000
|
|
ok = t < 5.0
|
|
record(f"0002-gate: fixed K=N=1000 <5ms", ok, f"{t:.3f}ms")
|
|
self.assertLess(t, 5.0,
|
|
f"fixed K=N=1000 took {t:.3f}ms, expected <5ms")
|
|
|
|
def test_fixed_scaling_linear(self):
|
|
"""Fixed path: time(K=N=1000) / time(K=N=500) must be <3x (O(K+N) not O(K*N))."""
|
|
t1 = self._time_fixed(500, 500)
|
|
t2 = self._time_fixed(1000, 1000)
|
|
ratio = t2 / t1 if t1 > 0 else 0
|
|
ok = ratio < 3.0
|
|
record(f"0002-gate: fixed k-scaling <3x (got {ratio:.2f}x)", ok,
|
|
f"t(500)={t1*1000:.3f}ms t(1000)={t2*1000:.3f}ms")
|
|
self.assertLess(ratio, 3.0,
|
|
f"fixed time ratio {ratio:.2f}x >= 3x -- O(K*N) regression detected")
|
|
|
|
def test_defective_scaling_quadratic(self):
|
|
"""Defective path: time(K=N=500) / time(K=N=250) must be >3x (confirms O(K*N))."""
|
|
t1 = self._time_defective(250, 250)
|
|
t2 = self._time_defective(500, 500)
|
|
ratio = t2 / t1 if t1 > 0 else 0
|
|
ok = ratio > 3.0
|
|
record(f"0002-gate: defective scaling >3x confirms O(K*N) (got {ratio:.2f}x)", ok,
|
|
f"t(250)={t1*1000:.3f}ms t(500)={t2*1000:.3f}ms")
|
|
self.assertGreater(ratio, 3.0,
|
|
f"defective ratio {ratio:.2f}x not >3x -- benchmark model may be wrong")
|
|
|
|
def test_fixed_faster_than_defective(self):
|
|
"""Fixed path K=N=1000 must be faster than defective K=N=1000."""
|
|
td = self._time_defective(1000, 1000) * 1000
|
|
tf = self._time_fixed(1000, 1000) * 1000
|
|
ok = tf < td
|
|
record(f"0002-gate: fixed faster than defective K=N=1000", ok,
|
|
f"defective={td:.3f}ms fixed={tf:.3f}ms speedup={td/tf:.1f}x")
|
|
self.assertLess(tf, td)
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# lean4-0003: mk_fresh_lp_name while loop
|
|
# ---------------------------------------------------------------------------
|
|
|
|
def _fresh_name_defective(n):
|
|
"""O(N^2): list.contains O(N) per while iteration, N iterations worst case.
|
|
Uses string keys to avoid CPython small-int cache optimizations that mask O(N^2)."""
|
|
existing = [f"l{i}" for i in range(n)]
|
|
candidate = f"l{n}" # not in list -- must scan full list every probe
|
|
for _ in range(n):
|
|
_ = candidate in existing # O(N) each time
|
|
|
|
|
|
def _fresh_name_fixed(n):
|
|
"""O(N): set built once O(N), O(1) per while iteration."""
|
|
existing = [f"l{i}" for i in range(n)]
|
|
existing_set = set(existing) # O(N) build once
|
|
candidate = f"l{n}"
|
|
for _ in range(n):
|
|
_ = candidate in existing_set # O(1)
|
|
|
|
|
|
class TestLean40003Unit(unittest.TestCase):
|
|
"""lean4-0003 Unit: while-loop name collision check list vs set."""
|
|
|
|
def test_name_not_in_list(self):
|
|
names = ["l1", "l2", "l3"]
|
|
ok = "l4" not in names
|
|
record("0003-unit: fresh candidate not in list", ok)
|
|
self.assertTrue(ok)
|
|
|
|
def test_name_in_list(self):
|
|
names = ["l1", "l2", "l3"]
|
|
ok = "l2" in names
|
|
record("0003-unit: collision detected in list", ok)
|
|
self.assertTrue(ok)
|
|
|
|
def test_name_not_in_set(self):
|
|
name_set = {"l1", "l2", "l3"}
|
|
ok = "l4" not in name_set
|
|
record("0003-unit: fresh candidate not in set", ok)
|
|
self.assertTrue(ok)
|
|
|
|
def test_name_in_set(self):
|
|
name_set = {"l1", "l2", "l3"}
|
|
ok = "l2" in name_set
|
|
record("0003-unit: collision detected in set", ok)
|
|
self.assertTrue(ok)
|
|
|
|
def test_list_and_set_agree_small(self):
|
|
names = [f"l{i}" for i in range(20)]
|
|
name_set = set(names)
|
|
probes = [f"l{i}" for i in range(25)]
|
|
mismatches = sum(1 for p in probes if (p in names) != (p in name_set))
|
|
ok = mismatches == 0
|
|
record("0003-unit: list and set agree on all name probes (20 names, 25 probes)", ok,
|
|
f"mismatches={mismatches}")
|
|
self.assertEqual(mismatches, 0)
|
|
|
|
def test_fresh_name_found_same(self):
|
|
"""Both approaches find the same fresh name (first not in existing)."""
|
|
existing = [f"l{i}" for i in range(1, 6)] # l1..l5 taken
|
|
existing_set = set(existing)
|
|
# list approach: scan l1..l6 until free
|
|
candidate_list = None
|
|
for i in range(1, 20):
|
|
n = f"l{i}"
|
|
if n not in existing:
|
|
candidate_list = n
|
|
break
|
|
# set approach
|
|
candidate_set = None
|
|
for i in range(1, 20):
|
|
n = f"l{i}"
|
|
if n not in existing_set:
|
|
candidate_set = n
|
|
break
|
|
ok = candidate_list == candidate_set == "l6"
|
|
record("0003-unit: list and set find same fresh name", ok,
|
|
f"list={candidate_list} set={candidate_set}")
|
|
self.assertEqual(candidate_list, candidate_set)
|
|
|
|
|
|
class TestLean40003Integration(unittest.TestCase):
|
|
"""lean4-0003 Integration: fresh name generation correctness at medium scale."""
|
|
|
|
def test_fresh_name_not_in_existing(self):
|
|
n = 200
|
|
existing = [f"l{i}" for i in range(1, n + 1)]
|
|
existing_set = set(existing)
|
|
# simulate: start at l1, increment until free
|
|
i = 1
|
|
while f"l{i}" in existing_set:
|
|
i += 1
|
|
fresh = f"l{i}"
|
|
ok = fresh not in existing_set and fresh == f"l{n+1}"
|
|
record(f"0003-intg: fresh name not in existing n={n}", ok, f"fresh={fresh}")
|
|
self.assertTrue(ok)
|
|
|
|
def test_list_and_set_same_result_medium(self):
|
|
n = 300
|
|
existing = list(range(n))
|
|
existing_set = set(existing)
|
|
probes = list(range(n + 50))
|
|
mismatches = sum(1 for p in probes if (p in existing) != (p in existing_set))
|
|
ok = mismatches == 0
|
|
record(f"0003-intg: list/set agree n={n} with {n+50} probes", ok,
|
|
f"mismatches={mismatches}")
|
|
self.assertEqual(mismatches, 0)
|
|
|
|
def test_dense_collisions_same_outcome(self):
|
|
"""When all candidates collide until the very last slot, both find it."""
|
|
n = 100
|
|
# l1..l{n} all taken; fresh = l{n+1}
|
|
existing = [f"l{i}" for i in range(1, n + 1)]
|
|
existing_set = set(existing)
|
|
|
|
# list approach
|
|
i = 1
|
|
while f"l{i}" in existing:
|
|
i += 1
|
|
fresh_list = f"l{i}"
|
|
|
|
# set approach
|
|
i = 1
|
|
while f"l{i}" in existing_set:
|
|
i += 1
|
|
fresh_set = f"l{i}"
|
|
|
|
ok = fresh_list == fresh_set
|
|
record(f"0003-intg: dense collision fresh name agrees list vs set n={n}", ok,
|
|
f"list={fresh_list} set={fresh_set}")
|
|
self.assertEqual(fresh_list, fresh_set)
|
|
|
|
def test_no_collision_returns_first(self):
|
|
"""Empty existing: fresh name is the first candidate."""
|
|
existing = []
|
|
existing_set = set()
|
|
i = 1
|
|
while f"l{i}" in existing_set:
|
|
i += 1
|
|
fresh = f"l{i}"
|
|
ok = fresh == "l1"
|
|
record("0003-intg: empty existing returns first candidate l1", ok, f"fresh={fresh}")
|
|
self.assertEqual(fresh, "l1")
|
|
|
|
|
|
class TestLean40003FunctionalComplexityGate(unittest.TestCase):
|
|
"""lean4-0003 Complexity gate: O(N) fixed path well under O(N^2) threshold."""
|
|
|
|
def _time_defective(self, n, trials=3):
|
|
times = []
|
|
for _ in range(trials):
|
|
t0 = time.perf_counter()
|
|
_fresh_name_defective(n)
|
|
times.append(time.perf_counter() - t0)
|
|
return min(times)
|
|
|
|
def _time_fixed(self, n, trials=3):
|
|
times = []
|
|
for _ in range(trials):
|
|
t0 = time.perf_counter()
|
|
_fresh_name_fixed(n)
|
|
times.append(time.perf_counter() - t0)
|
|
return min(times)
|
|
|
|
def test_fixed_n1000_under_5ms(self):
|
|
"""Fixed path N=1000 must complete in <5ms (benchmark: 0.095ms)."""
|
|
t = self._time_fixed(1000) * 1000
|
|
ok = t < 5.0
|
|
record(f"0003-gate: fixed N=1000 <5ms", ok, f"{t:.3f}ms")
|
|
self.assertLess(t, 5.0,
|
|
f"fixed N=1000 took {t:.3f}ms, expected <5ms")
|
|
|
|
def test_fixed_scaling_linear(self):
|
|
"""Fixed path: time(N=4000) / time(N=1000) must be <10x (O(N) not O(N^2)).
|
|
Using 4x N step with 10x ratio budget to accommodate Python timer variance
|
|
at these sub-millisecond timescales. O(N^2) would give ~16x ratio."""
|
|
t1 = self._time_fixed(1000)
|
|
t2 = self._time_fixed(4000)
|
|
ratio = t2 / t1 if t1 > 0 else 0
|
|
ok = ratio < 10.0
|
|
record(f"0003-gate: fixed k-scaling <10x for 4x N step (got {ratio:.2f}x)", ok,
|
|
f"t(1000)={t1*1000:.3f}ms t(4000)={t2*1000:.3f}ms")
|
|
self.assertLess(ratio, 10.0,
|
|
f"fixed time ratio {ratio:.2f}x >= 10x -- O(N^2) regression detected")
|
|
|
|
def test_defective_scaling_quadratic(self):
|
|
"""Defective path: time(N=2000) / time(N=1000) must be >3x (confirms O(N^2))."""
|
|
t1 = self._time_defective(1000)
|
|
t2 = self._time_defective(2000)
|
|
ratio = t2 / t1 if t1 > 0 else 0
|
|
ok = ratio > 3.0
|
|
record(f"0003-gate: defective scaling >3x confirms O(N^2) (got {ratio:.2f}x)", ok,
|
|
f"t(1000)={t1*1000:.3f}ms t(2000)={t2*1000:.3f}ms")
|
|
self.assertGreater(ratio, 3.0,
|
|
f"defective ratio {ratio:.2f}x not >3x -- benchmark model may be wrong")
|
|
|
|
def test_fixed_faster_than_defective(self):
|
|
"""Fixed path N=1000 must be faster than defective N=1000."""
|
|
td = self._time_defective(1000) * 1000
|
|
tf = self._time_fixed(1000) * 1000
|
|
ok = tf < td
|
|
record(f"0003-gate: fixed faster than defective N=1000", ok,
|
|
f"defective={td:.3f}ms fixed={tf:.3f}ms speedup={td/tf:.1f}x")
|
|
self.assertLess(tf, td)
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# lean4-0004: double-checked locking race in ir_interpreter lookup_symbol
|
|
# (CWE-362 -- correctness model, no timing gate applicable)
|
|
# ---------------------------------------------------------------------------
|
|
|
|
class _SymbolCache:
|
|
"""Python model of m_symbol_cache (instance-local, no global lock needed)."""
|
|
def __init__(self):
|
|
self._cache = {}
|
|
|
|
def get(self, key):
|
|
return self._cache.get(key)
|
|
|
|
def insert(self, key, value):
|
|
self._cache[key] = value
|
|
|
|
|
|
class _NativeSymbolCache:
|
|
"""Python model of g_native_symbol_cache (shared across threads, needs lock)."""
|
|
def __init__(self, symbols):
|
|
self._symbols = dict(symbols)
|
|
self._lock = threading.Lock()
|
|
|
|
def find_under_lock(self, key):
|
|
"""Single exclusive acquisition -- models single unique_lock fix."""
|
|
with self._lock:
|
|
return self._symbols.get(key)
|
|
|
|
|
|
def _lookup_symbol_defective(fn, local_cache, global_cache, global_lock):
|
|
"""
|
|
Models the double-checked locking defect:
|
|
1. Check global without lock (unsafe read).
|
|
2. Acquire shared lock.
|
|
3. Release shared lock.
|
|
4. Acquire unique lock.
|
|
5. Check again and insert.
|
|
Race window between steps 3 and 4 allows another thread to modify cache.
|
|
Python simulation: skips the unsafe read step but models the upgrade pattern.
|
|
"""
|
|
e = local_cache.get(fn)
|
|
if e is not None:
|
|
return e
|
|
# Simulated unsafe pre-check (no lock) -- data race in C++ on weak-order arch.
|
|
# Then: shared lock, release, unique lock -- upgrade race window.
|
|
with global_lock: # shared lock (simulated as exclusive in Python)
|
|
ne = global_cache._symbols.get(fn)
|
|
if ne is not None:
|
|
entry = ("decl:" + fn, ne)
|
|
local_cache.insert(fn, entry)
|
|
return entry
|
|
# Re-acquire (models unlock + unique_lock upgrade -- race window here)
|
|
with global_lock:
|
|
ne = global_cache._symbols.get(fn)
|
|
if ne is not None:
|
|
entry = ("decl:" + fn, ne)
|
|
local_cache.insert(fn, entry)
|
|
return entry
|
|
entry = ("decl:" + fn, None)
|
|
global_cache._symbols[fn] = None
|
|
local_cache.insert(fn, entry)
|
|
return entry
|
|
|
|
|
|
def _lookup_symbol_fixed(fn, local_cache, global_cache):
|
|
"""
|
|
Models the single unique_lock fix:
|
|
1. Check local cache (no lock needed -- instance-local).
|
|
2. Acquire one exclusive lock for all global cache access.
|
|
3. Release lock.
|
|
No race window.
|
|
"""
|
|
e = local_cache.get(fn)
|
|
if e is not None:
|
|
return e
|
|
# Single exclusive lock acquisition.
|
|
ne = global_cache.find_under_lock(fn)
|
|
if ne is not None:
|
|
entry = ("decl:" + fn, ne)
|
|
local_cache.insert(fn, entry)
|
|
return entry
|
|
entry = ("decl:" + fn, None)
|
|
local_cache.insert(fn, entry)
|
|
return entry
|
|
|
|
|
|
class TestLean40004Unit(unittest.TestCase):
|
|
"""lean4-0004 Unit: lookup_symbol correctness -- local hit, global hit, miss."""
|
|
|
|
def _make_caches(self, natives):
|
|
lock = threading.Lock()
|
|
local = _SymbolCache()
|
|
native = _NativeSymbolCache(natives)
|
|
return local, native, lock
|
|
|
|
def test_local_cache_hit(self):
|
|
local, native, lock = self._make_caches({"foo": 0xDEAD})
|
|
local.insert("foo", ("decl:foo", 0xDEAD))
|
|
result = _lookup_symbol_fixed("foo", local, native)
|
|
ok = result == ("decl:foo", 0xDEAD)
|
|
record("0004-unit: local cache hit returns cached entry", ok, f"result={result}")
|
|
self.assertEqual(result, ("decl:foo", 0xDEAD))
|
|
|
|
def test_global_cache_hit(self):
|
|
local, native, lock = self._make_caches({"bar": 0xBEEF})
|
|
result = _lookup_symbol_fixed("bar", local, native)
|
|
ok = result == ("decl:bar", 0xBEEF)
|
|
record("0004-unit: global cache hit returns correct entry", ok, f"result={result}")
|
|
self.assertEqual(result, ("decl:bar", 0xBEEF))
|
|
|
|
def test_cache_miss(self):
|
|
local, native, lock = self._make_caches({})
|
|
result = _lookup_symbol_fixed("baz", local, native)
|
|
ok = result == ("decl:baz", None)
|
|
record("0004-unit: cache miss returns entry with null native ptr", ok,
|
|
f"result={result}")
|
|
self.assertEqual(result, ("decl:baz", None))
|
|
|
|
def test_local_populated_after_global_hit(self):
|
|
"""After a global hit, subsequent lookup is served from local cache."""
|
|
local, native, lock = self._make_caches({"qux": 0x1234})
|
|
_lookup_symbol_fixed("qux", local, native)
|
|
# Remove from native to confirm second call uses local cache
|
|
native._symbols.clear()
|
|
result = _lookup_symbol_fixed("qux", local, native)
|
|
ok = result == ("decl:qux", 0x1234)
|
|
record("0004-unit: local cache populated after global hit", ok,
|
|
f"result={result}")
|
|
self.assertEqual(result, ("decl:qux", 0x1234))
|
|
|
|
def test_fixed_and_defective_agree_single_thread(self):
|
|
"""Fixed and defective paths return the same result in single-threaded use."""
|
|
natives = {"sym_a": 0xAABB, "sym_b": 0xCCDD}
|
|
lock = threading.Lock()
|
|
local_d = _SymbolCache()
|
|
native_d = _NativeSymbolCache(natives)
|
|
local_f = _SymbolCache()
|
|
native_f = _NativeSymbolCache(natives)
|
|
|
|
for fn in ["sym_a", "sym_b", "sym_missing"]:
|
|
rd = _lookup_symbol_defective(fn, local_d, native_d, lock)
|
|
rf = _lookup_symbol_fixed(fn, local_f, native_f)
|
|
if rd != rf:
|
|
record(f"0004-unit: fixed/defective agree single-thread fn={fn}",
|
|
False, f"defective={rd} fixed={rf}")
|
|
self.assertEqual(rd, rf)
|
|
record("0004-unit: fixed and defective agree single-thread (3 symbols)", True)
|
|
|
|
|
|
class TestLean40004Integration(unittest.TestCase):
|
|
"""lean4-0004 Integration: concurrent lookup correctness under fixed path."""
|
|
|
|
def test_concurrent_lookups_all_succeed(self):
|
|
"""N threads each looking up same symbol all get consistent result."""
|
|
n_threads = 8
|
|
symbols = {f"sym_{i}": i * 0x100 for i in range(20)}
|
|
native = _NativeSymbolCache(symbols)
|
|
results = {}
|
|
errors = []
|
|
lock = threading.Lock()
|
|
|
|
def worker(fn):
|
|
local = _SymbolCache()
|
|
try:
|
|
result = _lookup_symbol_fixed(fn, local, native)
|
|
with lock:
|
|
results[fn] = result
|
|
except Exception as e:
|
|
with lock:
|
|
errors.append(str(e))
|
|
|
|
threads = [threading.Thread(target=worker, args=(f"sym_{i}",))
|
|
for i in range(n_threads)]
|
|
for t in threads:
|
|
t.start()
|
|
for t in threads:
|
|
t.join()
|
|
|
|
ok = len(errors) == 0 and len(results) == n_threads
|
|
record(f"0004-intg: {n_threads} concurrent lookups all succeed", ok,
|
|
f"results={len(results)} errors={len(errors)}")
|
|
self.assertEqual(len(errors), 0)
|
|
self.assertEqual(len(results), n_threads)
|
|
|
|
def test_no_result_corruption_under_contention(self):
|
|
"""Multiple threads looking up the same key all get the same value."""
|
|
native = _NativeSymbolCache({"shared_sym": 0xFACE})
|
|
results = []
|
|
lock_r = threading.Lock()
|
|
|
|
def worker():
|
|
local = _SymbolCache()
|
|
r = _lookup_symbol_fixed("shared_sym", local, native)
|
|
with lock_r:
|
|
results.append(r)
|
|
|
|
threads = [threading.Thread(target=worker) for _ in range(16)]
|
|
for t in threads:
|
|
t.start()
|
|
for t in threads:
|
|
t.join()
|
|
|
|
ok = all(r == ("decl:shared_sym", 0xFACE) for r in results)
|
|
record(f"0004-intg: 16 threads on shared_sym all get identical result", ok,
|
|
f"unique_results={len(set(map(str, results)))}")
|
|
self.assertTrue(ok)
|
|
|
|
def test_miss_consistent_under_contention(self):
|
|
"""Multiple threads looking up a missing key all get the null-native entry."""
|
|
native = _NativeSymbolCache({})
|
|
results = []
|
|
lock_r = threading.Lock()
|
|
|
|
def worker():
|
|
local = _SymbolCache()
|
|
r = _lookup_symbol_fixed("no_such_sym", local, native)
|
|
with lock_r:
|
|
results.append(r)
|
|
|
|
threads = [threading.Thread(target=worker) for _ in range(8)]
|
|
for t in threads:
|
|
t.start()
|
|
for t in threads:
|
|
t.join()
|
|
|
|
ok = all(r == ("decl:no_such_sym", None) for r in results)
|
|
record(f"0004-intg: 8 threads on missing symbol all get null-native entry", ok)
|
|
self.assertTrue(ok)
|
|
|
|
|
|
class TestLean40004FunctionalComplexityGate(unittest.TestCase):
|
|
"""lean4-0004 Correctness gate: fixed path produces no lost lookups under concurrency."""
|
|
|
|
def test_high_concurrency_no_lost_results(self):
|
|
"""32 threads, 10 symbols each: all 320 lookups succeed with correct values."""
|
|
n_threads = 32
|
|
n_symbols = 10
|
|
symbols = {f"fn_{i}": i for i in range(n_symbols)}
|
|
native = _NativeSymbolCache(symbols)
|
|
total_expected = n_threads * n_symbols
|
|
results = []
|
|
lock_r = threading.Lock()
|
|
|
|
def worker():
|
|
local = _SymbolCache()
|
|
for i in range(n_symbols):
|
|
r = _lookup_symbol_fixed(f"fn_{i}", local, native)
|
|
with lock_r:
|
|
results.append(r)
|
|
|
|
threads = [threading.Thread(target=worker) for _ in range(n_threads)]
|
|
for t in threads:
|
|
t.start()
|
|
for t in threads:
|
|
t.join()
|
|
|
|
ok = len(results) == total_expected and all(r is not None for r in results)
|
|
record(f"0004-gate: 32 threads x 10 symbols = {total_expected} lookups, none lost",
|
|
ok, f"got={len(results)}")
|
|
self.assertEqual(len(results), total_expected)
|
|
|
|
def test_correctness_values_under_concurrency(self):
|
|
"""All thread-local results match expected native values."""
|
|
n_threads = 16
|
|
symbols = {f"fn_{i}": i * 7 for i in range(5)}
|
|
native = _NativeSymbolCache(symbols)
|
|
mismatches = []
|
|
lock_r = threading.Lock()
|
|
|
|
def worker(tid):
|
|
local = _SymbolCache()
|
|
for key, expected_native in symbols.items():
|
|
r = _lookup_symbol_fixed(key, local, native)
|
|
if r != ("decl:" + key, expected_native):
|
|
with lock_r:
|
|
mismatches.append((tid, key, r))
|
|
|
|
threads = [threading.Thread(target=worker, args=(i,))
|
|
for i in range(n_threads)]
|
|
for t in threads:
|
|
t.start()
|
|
for t in threads:
|
|
t.join()
|
|
|
|
ok = len(mismatches) == 0
|
|
record(f"0004-gate: {n_threads} threads correctness, no value mismatches", ok,
|
|
f"mismatches={len(mismatches)}")
|
|
self.assertEqual(len(mismatches), 0)
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# lean4-0005: Lake job registry IO.Ref race -> Std.Mutex
|
|
# (CWE-362 -- concurrent push correctness model)
|
|
# ---------------------------------------------------------------------------
|
|
|
|
class _JobRefDefective:
|
|
"""Models IO.Ref (Array OpaqueJob) -- not atomic: concurrent modify loses entries."""
|
|
def __init__(self):
|
|
self._array = []
|
|
# No lock -- models bare IO.Ref
|
|
|
|
def push(self, job):
|
|
# Simulate non-atomic read-modify-write: read, compute, write
|
|
current = list(self._array)
|
|
# Yield opportunity for interleaving (Python GIL limits this but models intent)
|
|
current.append(job)
|
|
self._array = current
|
|
|
|
def get(self):
|
|
return list(self._array)
|
|
|
|
|
|
class _JobMutexFixed:
|
|
"""Models Std.Mutex (Array OpaqueJob) -- atomic modify via lock."""
|
|
def __init__(self):
|
|
self._array = []
|
|
self._lock = threading.Lock()
|
|
|
|
def push_atomically(self, job):
|
|
with self._lock:
|
|
self._array.append(job)
|
|
|
|
def get(self):
|
|
with self._lock:
|
|
return list(self._array)
|
|
|
|
|
|
class TestLean40005Unit(unittest.TestCase):
|
|
"""lean4-0005 Unit: single-threaded push correctness for both Ref and Mutex models."""
|
|
|
|
def test_ref_push_single_thread(self):
|
|
ref = _JobRefDefective()
|
|
for i in range(10):
|
|
ref.push(f"job_{i}")
|
|
ok = len(ref.get()) == 10
|
|
record("0005-unit: IO.Ref push 10 jobs single-thread", ok,
|
|
f"count={len(ref.get())}")
|
|
self.assertEqual(len(ref.get()), 10)
|
|
|
|
def test_mutex_push_single_thread(self):
|
|
mx = _JobMutexFixed()
|
|
for i in range(10):
|
|
mx.push_atomically(f"job_{i}")
|
|
ok = len(mx.get()) == 10
|
|
record("0005-unit: Std.Mutex push 10 jobs single-thread", ok,
|
|
f"count={len(mx.get())}")
|
|
self.assertEqual(len(mx.get()), 10)
|
|
|
|
def test_mutex_ordering_preserved(self):
|
|
mx = _JobMutexFixed()
|
|
for i in range(5):
|
|
mx.push_atomically(f"job_{i}")
|
|
jobs = mx.get()
|
|
ok = jobs == [f"job_{i}" for i in range(5)]
|
|
record("0005-unit: Std.Mutex insertion order preserved (5 jobs)", ok,
|
|
f"jobs={jobs}")
|
|
self.assertEqual(jobs, [f"job_{i}" for i in range(5)])
|
|
|
|
def test_mutex_empty_initial(self):
|
|
mx = _JobMutexFixed()
|
|
ok = mx.get() == []
|
|
record("0005-unit: Std.Mutex initial state is empty", ok)
|
|
self.assertEqual(mx.get(), [])
|
|
|
|
def test_mutex_get_returns_copy(self):
|
|
mx = _JobMutexFixed()
|
|
mx.push_atomically("job_0")
|
|
snapshot = mx.get()
|
|
snapshot.append("external_modification")
|
|
ok = len(mx.get()) == 1 # internal state unaffected
|
|
record("0005-unit: Std.Mutex.get returns copy, not reference", ok)
|
|
self.assertEqual(len(mx.get()), 1)
|
|
|
|
|
|
class TestLean40005Integration(unittest.TestCase):
|
|
"""lean4-0005 Integration: mutex correctness under concurrent push."""
|
|
|
|
def test_mutex_no_lost_registrations(self):
|
|
"""N concurrent threads each push M jobs -- total must be N*M."""
|
|
n_threads = 8
|
|
n_jobs_each = 25
|
|
mx = _JobMutexFixed()
|
|
|
|
def worker(tid):
|
|
for j in range(n_jobs_each):
|
|
mx.push_atomically(f"t{tid}_j{j}")
|
|
|
|
threads = [threading.Thread(target=worker, args=(i,)) for i in range(n_threads)]
|
|
for t in threads:
|
|
t.start()
|
|
for t in threads:
|
|
t.join()
|
|
|
|
total = len(mx.get())
|
|
expected = n_threads * n_jobs_each
|
|
ok = total == expected
|
|
record(f"0005-intg: {n_threads} threads x {n_jobs_each} jobs = {expected}, got {total}",
|
|
ok)
|
|
self.assertEqual(total, expected)
|
|
|
|
def test_mutex_all_job_ids_present(self):
|
|
"""Every pushed job ID appears exactly once in the registry."""
|
|
n_threads = 4
|
|
n_jobs_each = 50
|
|
mx = _JobMutexFixed()
|
|
expected_ids = set()
|
|
|
|
def worker(tid):
|
|
for j in range(n_jobs_each):
|
|
jid = f"t{tid}_j{j}"
|
|
expected_ids.add(jid)
|
|
mx.push_atomically(jid)
|
|
|
|
threads = [threading.Thread(target=worker, args=(i,)) for i in range(n_threads)]
|
|
for t in threads:
|
|
t.start()
|
|
for t in threads:
|
|
t.join()
|
|
|
|
actual_ids = set(mx.get())
|
|
ok = actual_ids == expected_ids
|
|
record(f"0005-intg: all {len(expected_ids)} job IDs present exactly once", ok,
|
|
f"missing={len(expected_ids - actual_ids)} extra={len(actual_ids - expected_ids)}")
|
|
self.assertEqual(actual_ids, expected_ids)
|
|
|
|
def test_mutex_modifyget_atomic(self):
|
|
"""Simulates poll() modifyGet: drain returns exact count, none double-counted."""
|
|
mx = _JobMutexFixed()
|
|
for i in range(20):
|
|
mx.push_atomically(f"job_{i}")
|
|
|
|
# Atomic drain
|
|
with mx._lock:
|
|
drained = list(mx._array)
|
|
mx._array = []
|
|
|
|
ok = len(drained) == 20 and len(mx.get()) == 0
|
|
record(f"0005-intg: atomic drain returns all 20 jobs, leaves queue empty", ok,
|
|
f"drained={len(drained)} remaining={len(mx.get())}")
|
|
self.assertEqual(len(drained), 20)
|
|
self.assertEqual(len(mx.get()), 0)
|
|
|
|
def test_mutex_high_concurrency(self):
|
|
"""32 threads pushing simultaneously, no registrations lost."""
|
|
n_threads = 32
|
|
mx = _JobMutexFixed()
|
|
barrier = threading.Barrier(n_threads)
|
|
|
|
def worker(tid):
|
|
barrier.wait() # maximize contention
|
|
mx.push_atomically(f"job_{tid}")
|
|
|
|
threads = [threading.Thread(target=worker, args=(i,)) for i in range(n_threads)]
|
|
for t in threads:
|
|
t.start()
|
|
for t in threads:
|
|
t.join()
|
|
|
|
total = len(mx.get())
|
|
ok = total == n_threads
|
|
record(f"0005-intg: {n_threads}-way concurrent push, all registered", ok,
|
|
f"expected={n_threads} got={total}")
|
|
self.assertEqual(total, n_threads)
|
|
|
|
|
|
class TestLean40005FunctionalComplexityGate(unittest.TestCase):
|
|
"""lean4-0005 Correctness gate: zero lost registrations across stress run."""
|
|
|
|
def test_stress_no_lost_jobs(self):
|
|
"""64 threads x 100 jobs = 6400 total, none lost under Std.Mutex model."""
|
|
n_threads = 64
|
|
n_jobs = 100
|
|
mx = _JobMutexFixed()
|
|
barrier = threading.Barrier(n_threads)
|
|
|
|
def worker(tid):
|
|
barrier.wait()
|
|
for j in range(n_jobs):
|
|
mx.push_atomically((tid, j))
|
|
|
|
threads = [threading.Thread(target=worker, args=(i,)) for i in range(n_threads)]
|
|
for t in threads:
|
|
t.start()
|
|
for t in threads:
|
|
t.join()
|
|
|
|
total = len(mx.get())
|
|
expected = n_threads * n_jobs
|
|
ok = total == expected
|
|
record(f"0005-gate: stress {n_threads}x{n_jobs}={expected} jobs, zero lost", ok,
|
|
f"got={total}")
|
|
self.assertEqual(total, expected)
|
|
|
|
def test_no_duplicate_registrations(self):
|
|
"""Each job ID appears exactly once (no double-counting from non-atomic state)."""
|
|
n_threads = 16
|
|
n_jobs = 50
|
|
mx = _JobMutexFixed()
|
|
|
|
def worker(tid):
|
|
for j in range(n_jobs):
|
|
mx.push_atomically(f"{tid}:{j}")
|
|
|
|
threads = [threading.Thread(target=worker, args=(i,)) for i in range(n_threads)]
|
|
for t in threads:
|
|
t.start()
|
|
for t in threads:
|
|
t.join()
|
|
|
|
all_jobs = mx.get()
|
|
ok = len(all_jobs) == len(set(all_jobs))
|
|
record(f"0005-gate: no duplicate job IDs in registry", ok,
|
|
f"total={len(all_jobs)} unique={len(set(all_jobs))}")
|
|
self.assertEqual(len(all_jobs), len(set(all_jobs)))
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# lean4-0006: g_opts thread-local not reset at task boundaries
|
|
# (CWE-668 -- isolation correctness model)
|
|
# ---------------------------------------------------------------------------
|
|
|
|
class _TraceContextDefective:
|
|
"""
|
|
Models LEAN_THREAD_PTR(g_opts) without reset.
|
|
Thread-local state persists across simulated task boundaries.
|
|
Python uses threading.local() to model thread-local storage.
|
|
"""
|
|
_tls = threading.local()
|
|
|
|
@classmethod
|
|
def set_opts(cls, opts):
|
|
cls._tls.g_opts = opts
|
|
|
|
@classmethod
|
|
def get_opts(cls):
|
|
return getattr(cls._tls, "g_opts", None)
|
|
|
|
@classmethod
|
|
def reset_for_task(cls):
|
|
# Defective: does NOT reset g_opts.
|
|
pass
|
|
|
|
|
|
class _TraceContextFixed:
|
|
"""
|
|
Models LEAN_THREAD_PTR(g_opts) WITH register_thread_local_reset_fn.
|
|
Thread-local state cleared to None at each task boundary.
|
|
"""
|
|
_tls = threading.local()
|
|
|
|
@classmethod
|
|
def set_opts(cls, opts):
|
|
cls._tls.g_opts = opts
|
|
|
|
@classmethod
|
|
def get_opts(cls):
|
|
return getattr(cls._tls, "g_opts", None)
|
|
|
|
@classmethod
|
|
def reset_for_task(cls):
|
|
# Fixed: reset_thread_local() calls registered fn: g_opts = nullptr
|
|
cls._tls.g_opts = None
|
|
|
|
|
|
class TestLean40006Unit(unittest.TestCase):
|
|
"""lean4-0006 Unit: thread-local reset behavior."""
|
|
|
|
def test_defective_leaks_opts(self):
|
|
"""Defective: opts set in task 1 visible in task 2 on same thread."""
|
|
_TraceContextDefective.set_opts({"trace": True, "level": 3})
|
|
_TraceContextDefective.reset_for_task() # no-op in defective
|
|
leaked = _TraceContextDefective.get_opts()
|
|
ok = leaked is not None # leak confirmed
|
|
record("0006-unit: defective leaks opts across task boundary", ok,
|
|
f"leaked={leaked}")
|
|
self.assertIsNotNone(leaked)
|
|
|
|
def test_fixed_clears_opts(self):
|
|
"""Fixed: opts set in task 1 cleared after reset_for_task."""
|
|
_TraceContextFixed.set_opts({"trace": True, "level": 3})
|
|
_TraceContextFixed.reset_for_task()
|
|
after = _TraceContextFixed.get_opts()
|
|
ok = after is None
|
|
record("0006-unit: fixed clears opts at task boundary", ok,
|
|
f"after_reset={after}")
|
|
self.assertIsNone(after)
|
|
|
|
def test_fixed_initial_state_is_none(self):
|
|
results = []
|
|
def check():
|
|
results.append(_TraceContextFixed.get_opts())
|
|
t = threading.Thread(target=check)
|
|
t.start()
|
|
t.join()
|
|
ok = results[0] is None
|
|
record("0006-unit: fixed initial g_opts is None on fresh thread", ok)
|
|
self.assertIsNone(results[0])
|
|
|
|
def test_fixed_new_task_starts_clean(self):
|
|
"""Simulate task 1 sets opts, reset, task 2 reads None."""
|
|
_TraceContextFixed.set_opts({"important": "secret_trace"})
|
|
_TraceContextFixed.reset_for_task()
|
|
# Simulate task 2 on same thread
|
|
opts_task2 = _TraceContextFixed.get_opts()
|
|
ok = opts_task2 is None
|
|
record("0006-unit: task 2 starts with None after reset", ok,
|
|
f"opts_task2={opts_task2}")
|
|
self.assertIsNone(opts_task2)
|
|
|
|
def test_set_after_reset_works(self):
|
|
"""After reset, new task can set its own opts without interference."""
|
|
_TraceContextFixed.set_opts({"from_task_1": True})
|
|
_TraceContextFixed.reset_for_task()
|
|
_TraceContextFixed.set_opts({"from_task_2": True})
|
|
ok = _TraceContextFixed.get_opts() == {"from_task_2": True}
|
|
record("0006-unit: task 2 can set its own opts after reset", ok)
|
|
self.assertEqual(_TraceContextFixed.get_opts(), {"from_task_2": True})
|
|
|
|
|
|
class TestLean40006Integration(unittest.TestCase):
|
|
"""lean4-0006 Integration: no cross-task trace leakage under thread-pool reuse."""
|
|
|
|
def test_thread_reuse_isolation_fixed(self):
|
|
"""
|
|
Simulate thread pool: same thread runs task 1 (sets opts), then task 2.
|
|
Fixed: task 2 sees None. Defective: task 2 sees task 1's opts.
|
|
"""
|
|
task2_opts = []
|
|
|
|
def run_tasks():
|
|
# Task 1
|
|
_TraceContextFixed.set_opts({"task": 1, "verbose": True})
|
|
# Simulate task boundary -- reset_thread_local() fires
|
|
_TraceContextFixed.reset_for_task()
|
|
# Task 2
|
|
task2_opts.append(_TraceContextFixed.get_opts())
|
|
|
|
t = threading.Thread(target=run_tasks)
|
|
t.start()
|
|
t.join()
|
|
|
|
ok = task2_opts[0] is None
|
|
record("0006-intg: thread reuse -- task 2 opts isolated (fixed)", ok,
|
|
f"task2_opts={task2_opts[0]}")
|
|
self.assertIsNone(task2_opts[0])
|
|
|
|
def test_multiple_tasks_on_same_thread_all_isolated(self):
|
|
"""Each of 5 sequential tasks on same thread sees None at start."""
|
|
seen_opts = []
|
|
|
|
def run_five_tasks():
|
|
for i in range(5):
|
|
_TraceContextFixed.reset_for_task() # boundary before each task
|
|
seen_opts.append(_TraceContextFixed.get_opts())
|
|
_TraceContextFixed.set_opts({"task": i})
|
|
|
|
t = threading.Thread(target=run_five_tasks)
|
|
t.start()
|
|
t.join()
|
|
|
|
ok = all(o is None for o in seen_opts)
|
|
record(f"0006-intg: 5 sequential tasks all start with None", ok,
|
|
f"seen={seen_opts}")
|
|
self.assertTrue(ok)
|
|
|
|
def test_parallel_threads_no_cross_contamination(self):
|
|
"""
|
|
N threads run concurrently, each sets its own opts.
|
|
Each thread confirms it reads back its own opts, not another thread's.
|
|
"""
|
|
n_threads = 8
|
|
mismatches = []
|
|
lock_r = threading.Lock()
|
|
|
|
def worker(tid):
|
|
_TraceContextFixed.reset_for_task()
|
|
expected = {"thread": tid}
|
|
_TraceContextFixed.set_opts(expected)
|
|
actual = _TraceContextFixed.get_opts()
|
|
if actual != expected:
|
|
with lock_r:
|
|
mismatches.append((tid, actual))
|
|
|
|
threads = [threading.Thread(target=worker, args=(i,)) for i in range(n_threads)]
|
|
for t in threads:
|
|
t.start()
|
|
for t in threads:
|
|
t.join()
|
|
|
|
ok = len(mismatches) == 0
|
|
record(f"0006-intg: {n_threads} parallel threads, no cross-contamination", ok,
|
|
f"mismatches={len(mismatches)}")
|
|
self.assertEqual(len(mismatches), 0)
|
|
|
|
def test_defective_shows_leak_on_reuse(self):
|
|
"""Confirm the defective model DOES leak (validates our test is meaningful)."""
|
|
leaked_opts = []
|
|
|
|
def run_tasks():
|
|
_TraceContextDefective.set_opts({"secret": "from_task_1"})
|
|
_TraceContextDefective.reset_for_task() # no-op
|
|
leaked_opts.append(_TraceContextDefective.get_opts())
|
|
|
|
t = threading.Thread(target=run_tasks)
|
|
t.start()
|
|
t.join()
|
|
|
|
ok = leaked_opts[0] is not None # defective SHOULD leak
|
|
record("0006-intg: defective model confirms leak (validates test sensitivity)",
|
|
ok, f"leaked={leaked_opts[0]}")
|
|
self.assertIsNotNone(leaked_opts[0])
|
|
|
|
|
|
class TestLean40006FunctionalComplexityGate(unittest.TestCase):
|
|
"""lean4-0006 Correctness gate: zero leakage events in stress run."""
|
|
|
|
def test_no_leakage_stress(self):
|
|
"""64 threads, each running 10 simulated tasks: zero opts leaked."""
|
|
n_threads = 64
|
|
n_tasks = 10
|
|
leaks = []
|
|
lock_r = threading.Lock()
|
|
|
|
def worker(tid):
|
|
for task in range(n_tasks):
|
|
_TraceContextFixed.reset_for_task()
|
|
start_opts = _TraceContextFixed.get_opts()
|
|
if start_opts is not None:
|
|
with lock_r:
|
|
leaks.append((tid, task, start_opts))
|
|
_TraceContextFixed.set_opts({"tid": tid, "task": task})
|
|
|
|
threads = [threading.Thread(target=worker, args=(i,)) for i in range(n_threads)]
|
|
for t in threads:
|
|
t.start()
|
|
for t in threads:
|
|
t.join()
|
|
|
|
ok = len(leaks) == 0
|
|
record(f"0006-gate: {n_threads} threads x {n_tasks} tasks, zero leakage events",
|
|
ok, f"leaks={len(leaks)}")
|
|
self.assertEqual(len(leaks), 0)
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# lean4-0007: runtime/process.cpp env var inheritance O(N^2) -> O(N)
|
|
# ---------------------------------------------------------------------------
|
|
|
|
def _envvar_inherit_defective(inherited_env, override_keys_list):
|
|
"""
|
|
O(N*M): for each of N inherited env vars, construct a string from char* range
|
|
and call new_env_vars.count() -- modeled as list membership O(M).
|
|
N = len(inherited_env), M = len(override_keys_list).
|
|
"""
|
|
result = []
|
|
for key in inherited_env:
|
|
# Models: new_env_vars.count({key_begin, key_end}) -- O(M) list scan
|
|
if key not in override_keys_list: # O(M)
|
|
result.append(key)
|
|
return result
|
|
|
|
|
|
def _envvar_inherit_fixed(inherited_env, override_keys_list):
|
|
"""
|
|
O(N+M): pre-build unordered_set<string> of override keys, then O(1) lookup per inherited var.
|
|
"""
|
|
override_set = set(override_keys_list) # O(M) build once
|
|
result = []
|
|
for key in inherited_env:
|
|
if key not in override_set: # O(1)
|
|
result.append(key)
|
|
return result
|
|
|
|
|
|
class TestLean40007Unit(unittest.TestCase):
|
|
"""lean4-0007 Unit: env var inheritance correctness, list vs set lookup."""
|
|
|
|
def test_no_override_all_inherited(self):
|
|
env = ["PATH", "HOME", "USER"]
|
|
result = _envvar_inherit_fixed(env, [])
|
|
ok = result == env
|
|
record("0007-unit: no overrides -- all env vars inherited", ok,
|
|
f"result={result}")
|
|
self.assertEqual(result, env)
|
|
|
|
def test_all_overridden_none_inherited(self):
|
|
env = ["PATH", "HOME", "USER"]
|
|
result = _envvar_inherit_fixed(env, ["PATH", "HOME", "USER"])
|
|
ok = result == []
|
|
record("0007-unit: all overridden -- no env vars inherited", ok,
|
|
f"result={result}")
|
|
self.assertEqual(result, [])
|
|
|
|
def test_partial_override(self):
|
|
env = ["PATH", "HOME", "USER", "TERM"]
|
|
overrides = ["HOME", "TERM"]
|
|
result = _envvar_inherit_fixed(env, overrides)
|
|
ok = set(result) == {"PATH", "USER"}
|
|
record("0007-unit: partial override -- correct subset inherited", ok,
|
|
f"result={result}")
|
|
self.assertEqual(set(result), {"PATH", "USER"})
|
|
|
|
def test_list_and_set_agree_small(self):
|
|
env = [f"VAR_{i}" for i in range(20)]
|
|
overrides = [f"VAR_{i}" for i in range(0, 20, 2)] # even-numbered overridden
|
|
list_result = _envvar_inherit_defective(env, overrides)
|
|
set_result = _envvar_inherit_fixed(env, overrides)
|
|
ok = list_result == set_result
|
|
record("0007-unit: list and set approaches return same result (20 vars)", ok,
|
|
f"list={len(list_result)} set={len(set_result)}")
|
|
self.assertEqual(list_result, set_result)
|
|
|
|
def test_empty_env_empty_result(self):
|
|
result = _envvar_inherit_fixed([], ["OVERRIDE"])
|
|
ok = result == []
|
|
record("0007-unit: empty inherited env returns empty", ok)
|
|
self.assertEqual(result, [])
|
|
|
|
|
|
class TestLean40007Integration(unittest.TestCase):
|
|
"""lean4-0007 Integration: correctness at medium scale env var sets."""
|
|
|
|
def test_medium_env_list_vs_set_agree(self):
|
|
n = 500
|
|
env = [f"VAR_{i}" for i in range(n)]
|
|
overrides = [f"VAR_{i}" for i in range(0, n, 3)] # every 3rd overridden
|
|
list_result = _envvar_inherit_defective(env, overrides)
|
|
set_result = _envvar_inherit_fixed(env, overrides)
|
|
ok = list_result == set_result
|
|
record(f"0007-intg: N={n} env vars, list/set results agree", ok,
|
|
f"inherited={len(set_result)} overridden={len(overrides)}")
|
|
self.assertEqual(list_result, set_result)
|
|
|
|
def test_no_duplicates_in_result(self):
|
|
n = 200
|
|
env = [f"VAR_{i}" for i in range(n)]
|
|
overrides = [f"VAR_{i}" for i in range(50)]
|
|
result = _envvar_inherit_fixed(env, overrides)
|
|
ok = len(result) == len(set(result))
|
|
record(f"0007-intg: no duplicate env vars in inherited result N={n}", ok,
|
|
f"total={len(result)} unique={len(set(result))}")
|
|
self.assertEqual(len(result), len(set(result)))
|
|
|
|
def test_override_set_not_in_result(self):
|
|
n = 300
|
|
env = [f"VAR_{i}" for i in range(n)]
|
|
overrides_set = {f"VAR_{i}" for i in range(100, 200)}
|
|
overrides_list = list(overrides_set)
|
|
result = _envvar_inherit_fixed(env, overrides_list)
|
|
leaked = [k for k in result if k in overrides_set]
|
|
ok = len(leaked) == 0
|
|
record(f"0007-intg: no overridden keys appear in inherited result N={n}", ok,
|
|
f"leaked={len(leaked)}")
|
|
self.assertEqual(len(leaked), 0)
|
|
|
|
def test_non_overridden_all_present(self):
|
|
n = 300
|
|
env = [f"VAR_{i}" for i in range(n)]
|
|
overrides = [f"VAR_{i}" for i in range(100)]
|
|
result_set = set(_envvar_inherit_fixed(env, overrides))
|
|
expected = {f"VAR_{i}" for i in range(100, n)}
|
|
ok = result_set == expected
|
|
record(f"0007-intg: all non-overridden vars present in result N={n}", ok,
|
|
f"expected={len(expected)} got={len(result_set)}")
|
|
self.assertEqual(result_set, expected)
|
|
|
|
|
|
class TestLean40007FunctionalComplexityGate(unittest.TestCase):
|
|
"""lean4-0007 Complexity gate: O(N+M) fixed path well under O(N*M) threshold."""
|
|
|
|
def _time_defective(self, n, m, trials=3):
|
|
env = [f"VAR_{i}" for i in range(n)]
|
|
overrides = [f"OVER_{i}" for i in range(m)] # disjoint: scan goes full length
|
|
times = []
|
|
for _ in range(trials):
|
|
t0 = time.perf_counter()
|
|
_envvar_inherit_defective(env, overrides)
|
|
times.append(time.perf_counter() - t0)
|
|
return min(times)
|
|
|
|
def _time_fixed(self, n, m, trials=3):
|
|
env = [f"VAR_{i}" for i in range(n)]
|
|
overrides = [f"OVER_{i}" for i in range(m)]
|
|
times = []
|
|
for _ in range(trials):
|
|
t0 = time.perf_counter()
|
|
_envvar_inherit_fixed(env, overrides)
|
|
times.append(time.perf_counter() - t0)
|
|
return min(times)
|
|
|
|
def test_fixed_n500_under_1ms(self):
|
|
"""Fixed path N=500, M=500 must complete in <1ms (patch gate)."""
|
|
t = self._time_fixed(500, 500) * 1000
|
|
ok = t < 1.0
|
|
record(f"0007-gate: fixed N=500 M=500 <1ms", ok, f"{t:.3f}ms")
|
|
self.assertLess(t, 1.0,
|
|
f"fixed N=500 M=500 took {t:.3f}ms, expected <1ms")
|
|
|
|
def test_fixed_scaling_linear(self):
|
|
"""Fixed path: time(N=1000) / time(N=500) must be <3x (O(N+M) not O(N*M))."""
|
|
t1 = self._time_fixed(500, 500)
|
|
t2 = self._time_fixed(1000, 1000)
|
|
ratio = t2 / t1 if t1 > 0 else 0
|
|
ok = ratio < 3.0
|
|
record(f"0007-gate: fixed k-scaling <3x (got {ratio:.2f}x)", ok,
|
|
f"t(500)={t1*1000:.3f}ms t(1000)={t2*1000:.3f}ms")
|
|
self.assertLess(ratio, 3.0,
|
|
f"fixed time ratio {ratio:.2f}x >= 3x -- O(N*M) regression detected")
|
|
|
|
def test_defective_scaling_quadratic(self):
|
|
"""Defective path: time(N=M=2000) / time(N=M=500) must be >5x (confirms O(N*M)).
|
|
4x N and M step: O(N*M) predicts 16x ratio; O(N+M) predicts 4x ratio.
|
|
Requiring >5x confirms super-linear growth well above the O(N+M) baseline."""
|
|
t1 = self._time_defective(500, 500)
|
|
t2 = self._time_defective(2000, 2000)
|
|
ratio = t2 / t1 if t1 > 0 else 0
|
|
ok = ratio > 5.0
|
|
record(f"0007-gate: defective scaling >5x for 4x N step (got {ratio:.2f}x)", ok,
|
|
f"t(500)={t1*1000:.3f}ms t(2000)={t2*1000:.3f}ms")
|
|
self.assertGreater(ratio, 5.0,
|
|
f"defective ratio {ratio:.2f}x not >5x -- benchmark model may be wrong")
|
|
|
|
def test_fixed_faster_than_defective(self):
|
|
"""Fixed path N=M=1000 must be faster than defective N=M=1000."""
|
|
td = self._time_defective(1000, 1000) * 1000
|
|
tf = self._time_fixed(1000, 1000) * 1000
|
|
ok = tf < td
|
|
record(f"0007-gate: fixed faster than defective N=M=1000", ok,
|
|
f"defective={td:.3f}ms fixed={tf:.3f}ms speedup={td/tf:.1f}x")
|
|
self.assertLess(tf, td)
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Runner
|
|
# ---------------------------------------------------------------------------
|
|
|
|
def main():
|
|
print("=" * 70)
|
|
print("lean4 CWE-407/362/668 patch test suite")
|
|
print("UNDF-2026-000001259 through UNDF-2026-000001265")
|
|
print("=" * 70)
|
|
|
|
loader = unittest.TestLoader()
|
|
suite = unittest.TestSuite()
|
|
|
|
test_classes = [
|
|
TestLean40001Unit,
|
|
TestLean40001Integration,
|
|
TestLean40001FunctionalComplexityGate,
|
|
TestLean40002Unit,
|
|
TestLean40002Integration,
|
|
TestLean40002FunctionalComplexityGate,
|
|
TestLean40003Unit,
|
|
TestLean40003Integration,
|
|
TestLean40003FunctionalComplexityGate,
|
|
TestLean40004Unit,
|
|
TestLean40004Integration,
|
|
TestLean40004FunctionalComplexityGate,
|
|
TestLean40005Unit,
|
|
TestLean40005Integration,
|
|
TestLean40005FunctionalComplexityGate,
|
|
TestLean40006Unit,
|
|
TestLean40006Integration,
|
|
TestLean40006FunctionalComplexityGate,
|
|
TestLean40007Unit,
|
|
TestLean40007Integration,
|
|
TestLean40007FunctionalComplexityGate,
|
|
]
|
|
|
|
for cls in test_classes:
|
|
print(f"\n--- {cls.__name__} ---")
|
|
tests = loader.loadTestsFromTestCase(cls)
|
|
suite.addTests(tests)
|
|
runner = unittest.TextTestRunner(
|
|
stream=open("/dev/null", "w"),
|
|
verbosity=0
|
|
)
|
|
result = runner.run(tests)
|
|
|
|
print("\n" + "=" * 70)
|
|
total = len(_results)
|
|
passed = sum(_results)
|
|
failed = total - passed
|
|
print(f"Results: {passed}/{total} passed, {failed} failed")
|
|
print("=" * 70)
|
|
|
|
if failed > 0:
|
|
print(f"\nFAIL -- {failed} test(s) failed")
|
|
sys.exit(1)
|
|
else:
|
|
print("\nPASS -- all tests passed")
|
|
sys.exit(0)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|