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FA-47191 / Bounded deques / Open access

Deque headroom becomes negative above the high watermark · case 01

Deque headroom becomes negative above the high watermark.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

Deque headroom becomes negative above the high watermark.

THE FAILURE

Deque headroom becomes negative above the high watermark.

Unsuccessful approach: The partial repair still applies the incorrect transition to an admitted boundary or multi-element case.

Case contract

Bounded deque pressure notifications are edge-triggered: unpaused producers pause on an upward high crossing; paused producers resume on a downward low crossing. Return state, ordered notification list and distances to both thresholds.

Why this case matters

Controlled bounded deque implementation model with explicit storage and lifecycle observations.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(x):
    old,new,low,high,paused=x
    events=[]
    state=paused
    if not paused and old<high<=new:
        state=True
        events.append('pause')
    if paused and old>low>=new:
        state=False
        events.append('resume')
    at_high=new>=high
    at_low=new<=low
    headroom=high-new
    excess=max(0,new-low)
    return [state,events,at_high,at_low,headroom,excess]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('0', solve([2,5,2,5,False]), {1: [True, ['pause'], True, False, 0, 3], 2: [True, ['pause'], True, False, 0, 3], 3: [True, ['pause'], True, False, 0, 3], 4: [True, ['pause'], True, False, 0, 3], 5: [True, ['pause'], True, False, 0, 3]}[N])
check('1', solve([5,2,2,5,True]), {1: [False, ['resume'], False, True, 3, 0], 2: [False, ['resume'], False, True, 3, 0], 3: [False, ['resume'], False, True, 3, 0], 4: [False, ['resume'], False, True, 3, 0], 5: [False, ['resume'], False, True, 3, 0]}[N])
check('2', solve([5,6,2,5,False]), {1: [False, [], True, False, 0, 4], 2: [False, [], True, False, 0, 4], 3: [False, [], True, False, 0, 4], 4: [False, [], True, False, 0, 4], 5: [False, [], True, False, 0, 4]}[N])
check('3', solve([2,1,2,5,True]), {1: [True, [], False, True, 4, 0], 2: [True, [], False, True, 4, 0], 3: [True, [], False, True, 4, 0], 4: [True, [], False, True, 4, 0], 5: [True, [], False, True, 4, 0]}[N])
check('4', solve([N,N,0,N+3,False]), {1: [False, [], False, False, 3, 1], 2: [False, [], False, False, 3, 2], 3: [False, [], False, False, 3, 3], 4: [False, [], False, False, 3, 4], 5: [False, [], False, False, 3, 5]}[N])
check('5', solve([3,4,2,5,False]), {1: [False, [], False, False, 1, 2], 2: [False, [], False, False, 1, 2], 3: [False, [], False, False, 1, 2], 4: [False, [], False, False, 1, 2], 5: [False, [], False, False, 1, 2]}[N])
check('6', solve([6,1,2,5,True]), {1: [False, ['resume'], False, True, 4, 0], 2: [False, ['resume'], False, True, 4, 0], 3: [False, ['resume'], False, True, 4, 0], 4: [False, ['resume'], False, True, 4, 0], 5: [False, ['resume'], False, True, 4, 0]}[N])
check('7', solve([1,7,2,5,False]), {1: [True, ['pause'], True, False, 0, 5], 2: [True, ['pause'], True, False, 0, 5], 3: [True, ['pause'], True, False, 0, 5], 4: [True, ['pause'], True, False, 0, 5], 5: [True, ['pause'], True, False, 0, 5]}[N])
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
0[True, ['pause'], True, False, 0, 3][True, ['pause'], True, False, 0, 3]Passed
1[False, ['resume'], False, True, 3, 0][False, ['resume'], False, True, 3, 0]Passed
2[False, [], True, False, -1, 4][False, [], True, False, 0, 4]Failed
3[True, [], False, True, 4, 0][True, [], False, True, 4, 0]Passed
4[False, [], False, False, 3, 1][False, [], False, False, 3, 1]Passed
5[False, [], False, False, 1, 2][False, [], False, False, 1, 2]Passed
6[False, ['resume'], False, True, 4, 0][False, ['resume'], False, True, 4, 0]Passed
7[True, ['pause'], True, False, -2, 5][True, ['pause'], True, False, 0, 5]Failed

SHA-256 / b48972a2c7ac5d89399a25bc6d20e65ec231fbe5dee8d1615c00a5ab026eb644

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(x):
    old,new,low,high,paused=x
    events=[]
    state=paused
    if not paused and old<high<=new:
        state=True
        events.append('pause')
    if paused and old>low>=new:
        state=False
        events.append('resume')
    at_high=new>=high
    at_low=new<=low
    headroom=max(0,high-new) if new<=high else high-new
    excess=max(0,new-low)
    return [state,events,at_high,at_low,headroom,excess]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('0', solve([2,5,2,5,False]), {1: [True, ['pause'], True, False, 0, 3], 2: [True, ['pause'], True, False, 0, 3], 3: [True, ['pause'], True, False, 0, 3], 4: [True, ['pause'], True, False, 0, 3], 5: [True, ['pause'], True, False, 0, 3]}[N])
check('1', solve([5,2,2,5,True]), {1: [False, ['resume'], False, True, 3, 0], 2: [False, ['resume'], False, True, 3, 0], 3: [False, ['resume'], False, True, 3, 0], 4: [False, ['resume'], False, True, 3, 0], 5: [False, ['resume'], False, True, 3, 0]}[N])
check('2', solve([5,6,2,5,False]), {1: [False, [], True, False, 0, 4], 2: [False, [], True, False, 0, 4], 3: [False, [], True, False, 0, 4], 4: [False, [], True, False, 0, 4], 5: [False, [], True, False, 0, 4]}[N])
check('3', solve([2,1,2,5,True]), {1: [True, [], False, True, 4, 0], 2: [True, [], False, True, 4, 0], 3: [True, [], False, True, 4, 0], 4: [True, [], False, True, 4, 0], 5: [True, [], False, True, 4, 0]}[N])
check('4', solve([N,N,0,N+3,False]), {1: [False, [], False, False, 3, 1], 2: [False, [], False, False, 3, 2], 3: [False, [], False, False, 3, 3], 4: [False, [], False, False, 3, 4], 5: [False, [], False, False, 3, 5]}[N])
check('5', solve([3,4,2,5,False]), {1: [False, [], False, False, 1, 2], 2: [False, [], False, False, 1, 2], 3: [False, [], False, False, 1, 2], 4: [False, [], False, False, 1, 2], 5: [False, [], False, False, 1, 2]}[N])
check('6', solve([6,1,2,5,True]), {1: [False, ['resume'], False, True, 4, 0], 2: [False, ['resume'], False, True, 4, 0], 3: [False, ['resume'], False, True, 4, 0], 4: [False, ['resume'], False, True, 4, 0], 5: [False, ['resume'], False, True, 4, 0]}[N])
check('7', solve([1,7,2,5,False]), {1: [True, ['pause'], True, False, 0, 5], 2: [True, ['pause'], True, False, 0, 5], 3: [True, ['pause'], True, False, 0, 5], 4: [True, ['pause'], True, False, 0, 5], 5: [True, ['pause'], True, False, 0, 5]}[N])
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
0[True, ['pause'], True, False, 0, 3][True, ['pause'], True, False, 0, 3]Passed
1[False, ['resume'], False, True, 3, 0][False, ['resume'], False, True, 3, 0]Passed
2[False, [], True, False, -1, 4][False, [], True, False, 0, 4]Failed
3[True, [], False, True, 4, 0][True, [], False, True, 4, 0]Passed
4[False, [], False, False, 3, 1][False, [], False, False, 3, 1]Passed
5[False, [], False, False, 1, 2][False, [], False, False, 1, 2]Passed
6[False, ['resume'], False, True, 4, 0][False, ['resume'], False, True, 4, 0]Passed
7[True, ['pause'], True, False, -2, 5][True, ['pause'], True, False, 0, 5]Failed

SHA-256 / d601276ecf747ab0213acecacff01fb7c66ec5c39896467c7630b3024f91009c

HELD IN THE MEMBER ARCHIVE

The verified repair and its recorded checks are member-only.

This mechanism has 8 recorded checks per implementation. The open-access tier publishes the failure and the unsuccessful fix; the repaired source that passes every check, and the observations that prove it, are available to members.

Every case sharing this mechanism uses the same contract and the same repair, so this one record is held back for all of them.

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Verification & scope

Offline finite deterministic model; no claim of production implementation or concurrent memory-model conformance. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.

Observations recorded using Python 3.12.14 at 2026-09-29T14:44:39.114606+00:00.

Case digest / 2d05a2bd2757e81a4dfeabe651424d79addb7a7af1245ed80553856501ff4347