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FA-44426 / Data systems / Open access

Only one output is retained per decoded run value · case 01

Only one output is retained per decoded run value.

Verified by executionVariant 1 · 7 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

run-end-slice: Only one output is retained per decoded run value.

THE FAILURE

run-end-slice: Only one output is retained per decoded run value.

Unsuccessful approach: Coalescing adjacent outputs returns run values rather than the requested decoded rows.

Case contract

Decode a run-end vector into the requested [start,start+length) slice. Run ends are exclusive cumulative positions; values and validity are stored per run. Empty slices return empty.

Why this case matters

A bounded deterministic data engine model makes representation and changelog faults reproducible.

1 / The failure

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

N = 1
observations = []
def solve(d):
    try:
        ends,values,valid,start,length=d
        out=[]
        for pos in range(start,start+length):
            r=next(i for i,end in enumerate(ends) if pos < end)
            value=values[r]
            known=valid[r]
            out.append(value if known else None)
        return list(dict.fromkeys(out))
    except (IndexError, KeyError, ValueError, StopIteration) as exc:
        return {"representation_error": type(exc).__name__}
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
if N == 1:
    check('crossing run boundary', solve([[2, 5], [1, 2], [True, True], 1, 3]), [1, 2, 2])
    check('start at boundary', solve([[2, 5], [1, 2], [True, True], 2, 2]), [2, 2])
    check('null middle run', solve([[1, 3, 5], [1, 2, 3], [True, False, True], 0, 5]), [1, None, None, 3, 3])
    check('null first run', solve([[1, 3], [1, 2], [False, True], 1, 2]), [2, 2])
    check('empty slice', solve([[3], [1], [True], 1, 0]), [])
    check('single run', solve([[4], [1], [True], 0, 4]), [1, 1, 1, 1])
    check('tail slice', solve([[1, 2, 6], [1, 2, 3], [True, True, True], 4, 2]), [3, 3])
elif N == 2:
    check('crossing run boundary', solve([[2, 5], [2, 3], [True, True], 1, 3]), [2, 3, 3])
    check('start at boundary', solve([[2, 5], [2, 3], [True, True], 2, 2]), [3, 3])
    check('null middle run', solve([[1, 3, 5], [2, 3, 4], [True, False, True], 0, 5]), [2, None, None, 4, 4])
    check('null first run', solve([[1, 3], [2, 3], [False, True], 1, 2]), [3, 3])
    check('empty slice', solve([[3], [2], [True], 1, 0]), [])
    check('single run', solve([[4], [2], [True], 0, 4]), [2, 2, 2, 2])
    check('tail slice', solve([[1, 2, 6], [2, 3, 4], [True, True, True], 4, 2]), [4, 4])
elif N == 3:
    check('crossing run boundary', solve([[2, 5], [3, 4], [True, True], 1, 3]), [3, 4, 4])
    check('start at boundary', solve([[2, 5], [3, 4], [True, True], 2, 2]), [4, 4])
    check('null middle run', solve([[1, 3, 5], [3, 4, 5], [True, False, True], 0, 5]), [3, None, None, 5, 5])
    check('null first run', solve([[1, 3], [3, 4], [False, True], 1, 2]), [4, 4])
    check('empty slice', solve([[3], [3], [True], 1, 0]), [])
    check('single run', solve([[4], [3], [True], 0, 4]), [3, 3, 3, 3])
    check('tail slice', solve([[1, 2, 6], [3, 4, 5], [True, True, True], 4, 2]), [5, 5])
elif N == 4:
    check('crossing run boundary', solve([[2, 5], [4, 5], [True, True], 1, 3]), [4, 5, 5])
    check('start at boundary', solve([[2, 5], [4, 5], [True, True], 2, 2]), [5, 5])
    check('null middle run', solve([[1, 3, 5], [4, 5, 6], [True, False, True], 0, 5]), [4, None, None, 6, 6])
    check('null first run', solve([[1, 3], [4, 5], [False, True], 1, 2]), [5, 5])
    check('empty slice', solve([[3], [4], [True], 1, 0]), [])
    check('single run', solve([[4], [4], [True], 0, 4]), [4, 4, 4, 4])
    check('tail slice', solve([[1, 2, 6], [4, 5, 6], [True, True, True], 4, 2]), [6, 6])
elif N == 5:
    check('crossing run boundary', solve([[2, 5], [5, 6], [True, True], 1, 3]), [5, 6, 6])
    check('start at boundary', solve([[2, 5], [5, 6], [True, True], 2, 2]), [6, 6])
    check('null middle run', solve([[1, 3, 5], [5, 6, 7], [True, False, True], 0, 5]), [5, None, None, 7, 7])
    check('null first run', solve([[1, 3], [5, 6], [False, True], 1, 2]), [6, 6])
    check('empty slice', solve([[3], [5], [True], 1, 0]), [])
    check('single run', solve([[4], [5], [True], 0, 4]), [5, 5, 5, 5])
    check('tail slice', solve([[1, 2, 6], [5, 6, 7], [True, True, True], 4, 2]), [7, 7])
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
crossing run boundary[1, 2][1, 2, 2]Failed
start at boundary[2][2, 2]Failed
null middle run[1, None, 3][1, None, None, 3, 3]Failed
null first run[2][2, 2]Failed
empty slice[][]Passed
single run[1][1, 1, 1, 1]Failed
tail slice[3][3, 3]Failed

SHA-256 / e05ca92ab14bc895da095df81cbf949cd79e26b5409279902eefe56d28a6cebf

2 / The unsuccessful fix

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

N = 1
observations = []
def solve(d):
    try:
        ends,values,valid,start,length=d
        out=[]
        for pos in range(start,start+length):
            r=next(i for i,end in enumerate(ends) if pos < end)
            value=values[r]
            known=valid[r]
            out.append(value if known else None)
        return [v for i,v in enumerate(out) if i==0 or v!=out[i-1]]
    except (IndexError, KeyError, ValueError, StopIteration) as exc:
        return {"representation_error": type(exc).__name__}
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
if N == 1:
    check('crossing run boundary', solve([[2, 5], [1, 2], [True, True], 1, 3]), [1, 2, 2])
    check('start at boundary', solve([[2, 5], [1, 2], [True, True], 2, 2]), [2, 2])
    check('null middle run', solve([[1, 3, 5], [1, 2, 3], [True, False, True], 0, 5]), [1, None, None, 3, 3])
    check('null first run', solve([[1, 3], [1, 2], [False, True], 1, 2]), [2, 2])
    check('empty slice', solve([[3], [1], [True], 1, 0]), [])
    check('single run', solve([[4], [1], [True], 0, 4]), [1, 1, 1, 1])
    check('tail slice', solve([[1, 2, 6], [1, 2, 3], [True, True, True], 4, 2]), [3, 3])
elif N == 2:
    check('crossing run boundary', solve([[2, 5], [2, 3], [True, True], 1, 3]), [2, 3, 3])
    check('start at boundary', solve([[2, 5], [2, 3], [True, True], 2, 2]), [3, 3])
    check('null middle run', solve([[1, 3, 5], [2, 3, 4], [True, False, True], 0, 5]), [2, None, None, 4, 4])
    check('null first run', solve([[1, 3], [2, 3], [False, True], 1, 2]), [3, 3])
    check('empty slice', solve([[3], [2], [True], 1, 0]), [])
    check('single run', solve([[4], [2], [True], 0, 4]), [2, 2, 2, 2])
    check('tail slice', solve([[1, 2, 6], [2, 3, 4], [True, True, True], 4, 2]), [4, 4])
elif N == 3:
    check('crossing run boundary', solve([[2, 5], [3, 4], [True, True], 1, 3]), [3, 4, 4])
    check('start at boundary', solve([[2, 5], [3, 4], [True, True], 2, 2]), [4, 4])
    check('null middle run', solve([[1, 3, 5], [3, 4, 5], [True, False, True], 0, 5]), [3, None, None, 5, 5])
    check('null first run', solve([[1, 3], [3, 4], [False, True], 1, 2]), [4, 4])
    check('empty slice', solve([[3], [3], [True], 1, 0]), [])
    check('single run', solve([[4], [3], [True], 0, 4]), [3, 3, 3, 3])
    check('tail slice', solve([[1, 2, 6], [3, 4, 5], [True, True, True], 4, 2]), [5, 5])
elif N == 4:
    check('crossing run boundary', solve([[2, 5], [4, 5], [True, True], 1, 3]), [4, 5, 5])
    check('start at boundary', solve([[2, 5], [4, 5], [True, True], 2, 2]), [5, 5])
    check('null middle run', solve([[1, 3, 5], [4, 5, 6], [True, False, True], 0, 5]), [4, None, None, 6, 6])
    check('null first run', solve([[1, 3], [4, 5], [False, True], 1, 2]), [5, 5])
    check('empty slice', solve([[3], [4], [True], 1, 0]), [])
    check('single run', solve([[4], [4], [True], 0, 4]), [4, 4, 4, 4])
    check('tail slice', solve([[1, 2, 6], [4, 5, 6], [True, True, True], 4, 2]), [6, 6])
elif N == 5:
    check('crossing run boundary', solve([[2, 5], [5, 6], [True, True], 1, 3]), [5, 6, 6])
    check('start at boundary', solve([[2, 5], [5, 6], [True, True], 2, 2]), [6, 6])
    check('null middle run', solve([[1, 3, 5], [5, 6, 7], [True, False, True], 0, 5]), [5, None, None, 7, 7])
    check('null first run', solve([[1, 3], [5, 6], [False, True], 1, 2]), [6, 6])
    check('empty slice', solve([[3], [5], [True], 1, 0]), [])
    check('single run', solve([[4], [5], [True], 0, 4]), [5, 5, 5, 5])
    check('tail slice', solve([[1, 2, 6], [5, 6, 7], [True, True, True], 4, 2]), [7, 7])
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
crossing run boundary[1, 2][1, 2, 2]Failed
start at boundary[2][2, 2]Failed
null middle run[1, None, 3][1, None, None, 3, 3]Failed
null first run[2][2, 2]Failed
empty slice[][]Passed
single run[1][1, 1, 1, 1]Failed
tail slice[3][3, 3]Failed

SHA-256 / 307b1a6d0498e8980c660b0adeb37b2e4e7545539a2de7ca1ce1f1af75dcf3d6

HELD IN THE MEMBER ARCHIVE

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

This mechanism has 7 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 stipulated semantics over valid small inputs; no performance, concurrency, or production-engine conformance claim. 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 2026-09-29T14:44:12.113188+00:00.

Case digest / cce0f603910e2494ca8ba75ad1f2270d9d82a5084eb0c41d2cd8c0b90558a717