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FA-8931 / Table interactions / Open access

Multi-column sort interaction: New sort columns start descending despite ascending contract · case 01

New sort columns start descending despite ascending contract.

Verified by executionVariant 1 · 9 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The new column operation uses `if index is None: entry = (column, 'desc')` where the contract requires `if index is None: entry = (column, 'asc')`.

THE FAILURE

The new column operation uses `if index is None: entry = (column, 'desc')` where the contract requires `if index is None: entry = (column, 'asc')`.

Unsuccessful approach: Skipping the entry prevents starting sorting.

Case contract

New sort is ascending; activation cycles ascending to descending to removed when allowed; single mode replaces all sorts; multi mode preserves priorities when changing direction.

Why this case matters

A deterministic model of multi-column sort interaction; this isolates one interface invariant without requiring a browser.

1 / The failure

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

N = 1
observations = []
def solve(sorting, column, multi, removable):
    sorting = list(sorting)
    index = next((i for i, (c, d) in enumerate(sorting) if c == column), None)
    if index is None: entry = (column, 'desc')
    elif sorting[index][1] == 'asc': entry = (column, 'desc')
    else: entry = None if removable else (column, 'asc')
    if not multi: return [entry] if entry else []
    if index is None: return sorting + [entry]
    if entry is None: return sorting[:index] + sorting[index+1:]
    sorting[index] = entry
    return sorting
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('new', solve([], 'a', False, True), [('a', 'asc')])
check('reverse', solve([('a','asc')], 'a', False, True), [('a', 'desc')])
check('locked', solve([('a','desc')], 'a', False, False), [('a', 'asc')])
check('remove', solve([('a','desc')], 'a', False, True), [])
check('single', solve([('a','asc')], 'b', False, True), [('b', 'asc')])
check('priority', solve([('a','asc'),('b','asc')], 'a', True, True), [('a', 'desc'), ('b', 'asc')])
check('append', solve([('a','asc')], 'b', True, True), [('a', 'asc'), ('b', 'asc')])
check('parameterized sort identity', solve([],str(N),False,True), [(str(N),'asc')])
for repetition in range(N):
    check('repeat append', solve([('a','asc')], 'b', True, True), [('a', 'asc'), ('b', 'asc')])
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
new[['a', 'desc']][['a', 'asc']]Failed
reverse[['a', 'desc']][['a', 'desc']]Passed
locked[['a', 'asc']][['a', 'asc']]Passed
remove[][]Passed
single[['b', 'desc']][['b', 'asc']]Failed
priority[['a', 'desc'], ['b', 'asc']][['a', 'desc'], ['b', 'asc']]Passed
append[['a', 'asc'], ['b', 'desc']][['a', 'asc'], ['b', 'asc']]Failed
parameterized sort identity[['1', 'desc']][['1', 'asc']]Failed
repeat append[['a', 'asc'], ['b', 'desc']][['a', 'asc'], ['b', 'asc']]Failed

SHA-256 / 490b0f846b4e2fb656b5d1b7d407ab4340b128d6e455e73a3c23538a5b9d0a87

2 / The unsuccessful fix

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

N = 1
observations = []
def solve(sorting, column, multi, removable):
    sorting = list(sorting)
    index = next((i for i, (c, d) in enumerate(sorting) if c == column), None)
    if index is None: entry = None
    elif sorting[index][1] == 'asc': entry = (column, 'desc')
    else: entry = None if removable else (column, 'asc')
    if not multi: return [entry] if entry else []
    if index is None: return sorting + [entry]
    if entry is None: return sorting[:index] + sorting[index+1:]
    sorting[index] = entry
    return sorting
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('new', solve([], 'a', False, True), [('a', 'asc')])
check('reverse', solve([('a','asc')], 'a', False, True), [('a', 'desc')])
check('locked', solve([('a','desc')], 'a', False, False), [('a', 'asc')])
check('remove', solve([('a','desc')], 'a', False, True), [])
check('single', solve([('a','asc')], 'b', False, True), [('b', 'asc')])
check('priority', solve([('a','asc'),('b','asc')], 'a', True, True), [('a', 'desc'), ('b', 'asc')])
check('append', solve([('a','asc')], 'b', True, True), [('a', 'asc'), ('b', 'asc')])
check('parameterized sort identity', solve([],str(N),False,True), [(str(N),'asc')])
for repetition in range(N):
    check('repeat append', solve([('a','asc')], 'b', True, True), [('a', 'asc'), ('b', 'asc')])
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
new[][['a', 'asc']]Failed
reverse[['a', 'desc']][['a', 'desc']]Passed
locked[['a', 'asc']][['a', 'asc']]Passed
remove[][]Passed
single[][['b', 'asc']]Failed
priority[['a', 'desc'], ['b', 'asc']][['a', 'desc'], ['b', 'asc']]Passed
append[['a', 'asc'], None][['a', 'asc'], ['b', 'asc']]Failed
parameterized sort identity[][['1', 'asc']]Failed
repeat append[['a', 'asc'], None][['a', 'asc'], ['b', 'asc']]Failed

SHA-256 / 295c0140a2b135fe6eecc104b056e537ad69c8e029c8ba72bd989d0dd8fe2968

HELD IN THE MEMBER ARCHIVE

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

This mechanism has 9 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

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 2026-09-29T14:38:25.741482+00:00.

Case digest / 064b50730288a724f67533d53bd915d2b2d3c7b6e01574535874167a60fdcd1d