FA-96921 / Recipe scaling and nutrition / Open access

Volume-to-mass ingredient converter: small-mass decimal threshold · case 01

7.5 g of flour prints "8 g" and exactly 10 g prints "10.0 g" under the attempted fix.

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

ROOT CAUSE

The one-decimal display is reserved for amounts under 1 g instead of under 10 g.

VERIFIED REPAIR

Print one decimal for results strictly below 10 g.

Unsuccessful approach: Using <= 10 prints an exact 10 g as "10.0 g" instead of whole grams.

Case contract

Convert amount (fraction string) of cup/tbsp/tsp/ml to grams. 1 cup = 16 tbsp = 48 tsp = 236.5 ml. Grams per cup: flour 120, sugar 200, brown sugar 145 (213 when prefixed "packed "), butter 227, honey 340, water 236. Names are trimmed and case-insensitive; a ", sifted" suffix multiplies density by 9/10. Results below 10 g print one decimal ("%.1f g"), otherwise whole grams, both half-up. Unknown ingredient or unit returns an error string.

Why this case matters

Recipe apps convert American volume measures to weights for scaling and nutrition lookup.

1 / The failure

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json
import math
from fractions import Fraction
N = 1
observations = []
def solve(amount, unit, ingredient):
    DENS = {'flour': 120, 'sugar': 200, 'brown sugar': 145, 'butter': 227, 'honey': 340, 'water': 236}
    PER_CUP = {'cup': 1, 'tbsp': 16, 'tsp': 48}
    name = ingredient.strip().lower()
    packed = name.startswith('packed ')
    if packed:
        name = name[len('packed '):]
    sifted = name.endswith(', sifted')
    if sifted:
        name = name[:-len(', sifted')]
    if name not in DENS:
        return 'error: unknown ingredient'
    if unit == 'ml':
        cups = Fraction(amount) / Fraction(2365, 10)
    elif unit in PER_CUP:
        cups = Fraction(amount) / PER_CUP[unit]
    else:
        return 'error: unknown unit'
    d = Fraction(DENS[name])
    if packed and name == 'brown sugar':
        d = Fraction(213)
    if sifted:
        d = d * Fraction(9, 10)
    g = cups * d
    if g < 1:
        return '%.1f g' % (math.floor(g * 10 + Fraction(1, 2)) / 10)
    return '%d g' % math.floor(g + Fraction(1, 2))
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['one cup flour', ['1', 'cup', 'flour'], '120 g'], ['exactly ten grams', ['4', 'tsp', 'flour'], '10 g'],
  ['packed brown sugar', ['1', 'cup', 'packed brown sugar'], '213 g'],
  ['regression: small-mass decimal threshold', ['2', 'ml', '  Flour '], '1.0 g'],
  ['generated control 1', ['4', 'cup', 'saffron'], 'error: unknown ingredient'],
  ['generated control 2', ['3', 'tbsp', '  Water, Sifted '], '40 g'],
  ['generated control 3', ['4', 'tbsp', 'saffron'], 'error: unknown ingredient'],
  ['generated control 4', ['3', 'ml', '  Brown Sugar '], '1.8 g']],
 [['exactly ten grams', ['4', 'tsp', 'flour'], '10 g'],
  ['packed brown sugar', ['1', 'cup', 'packed brown sugar'], '213 g'],
  ['unknown ingredient', ['1', 'cup', 'saffron'], 'error: unknown ingredient'],
  ['regression: small-mass decimal threshold', ['1/4', 'tbsp', '  Packed Water '], '3.7 g'],
  ['generated control 1', ['1/3', 'ml', 'honey'], '0.5 g'],
  ['generated control 2', ['3/4', 'ml', 'butter, sifted'], '0.6 g'],
  ['generated control 3', ['1/3', 'cup', '  Flour, Sifted '], '36 g'],
  ['generated control 4', ['3/4', 'ml', 'sugar'], '0.6 g']],
 [['packed brown sugar', ['1', 'cup', 'packed brown sugar'], '213 g'],
  ['unknown ingredient', ['1', 'cup', 'saffron'], 'error: unknown ingredient'],
  ['metric cup of water', ['250', 'ml', 'water'], '249 g'],
  ['regression: small-mass decimal threshold', ['1/3', 'tsp', 'packed sugar'], '1.4 g'],
  ['repair check: small-mass decimal threshold', ['4', 'tsp', 'flour'], '10 g'],
  ['generated control 1', ['1', 'ml', 'SUGAR'], '0.8 g'],
  ['generated control 2', ['5', 'tsp', 'water'], '25 g'],
  ['generated control 3', ['250', 'pinch', 'butter'], 'error: unknown unit']],
 [['unknown ingredient', ['1', 'cup', 'saffron'], 'error: unknown ingredient'],
  ['metric cup of water', ['250', 'ml', 'water'], '249 g'],
  ['sifted flour tablespoon', ['2', 'tbsp', 'Flour, sifted'], '14 g'],
  ['regression: small-mass decimal threshold', ['1/2', 'tbsp', 'flour'], '3.8 g'],
  ['repair check: small-mass decimal threshold', ['4', 'tsp', 'flour'], '10 g'],
  ['generated control 1', ['250', 'cup', 'butter'], '56750 g'],
  ['generated control 2', ['1/3', 'ml', 'BUTTER'], '0.3 g'],
  ['generated control 3', ['1/3', 'tsp', '  Brown Sugar, Sifted '], '0.9 g']],
 [['metric cup of water', ['250', 'ml', 'water'], '249 g'],
  ['sifted flour tablespoon', ['2', 'tbsp', 'Flour, sifted'], '14 g'],
  ['one cup flour', ['1', 'cup', 'flour'], '120 g'],
  ['regression: small-mass decimal threshold', ['1', 'tsp', 'honey'], '7.1 g'],
  ['repair check: small-mass decimal threshold', ['4', 'tsp', '  Packed Flour '], '10 g'],
  ['generated control 1', ['1/3', 'cup', '  Flour, Sifted '], '36 g'],
  ['generated control 2', ['6', 'cup', 'packed sugar'], '1200 g'],
  ['generated control 3', ['1', 'ml', 'water'], '1.0 g']]]
for label, args, expected in fixtures[N - 1]:
    check(label, solve(*args), expected)
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 fixture	Actual	Expected	Outcome
one cup flour	`120 g`	`120 g`	Passed
exactly ten grams	`10 g`	`10 g`	Passed
packed brown sugar	`213 g`	`213 g`	Passed
regression: small-mass decimal threshold	`1 g`	`1.0 g`	Failed
generated control 1	`error: unknown ingredient`	`error: unknown ingredient`	Passed
generated control 2	`40 g`	`40 g`	Passed
generated control 3	`error: unknown ingredient`	`error: unknown ingredient`	Passed
generated control 4	`2 g`	`1.8 g`	Failed

SHA-256 / bc8c23b1c04da7721d83b8d31dc8062d37f1e011947c52c6f041fcd3d83d5896

2 / The unsuccessful fix

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json
import math
from fractions import Fraction
N = 1
observations = []
def solve(amount, unit, ingredient):
    DENS = {'flour': 120, 'sugar': 200, 'brown sugar': 145, 'butter': 227, 'honey': 340, 'water': 236}
    PER_CUP = {'cup': 1, 'tbsp': 16, 'tsp': 48}
    name = ingredient.strip().lower()
    packed = name.startswith('packed ')
    if packed:
        name = name[len('packed '):]
    sifted = name.endswith(', sifted')
    if sifted:
        name = name[:-len(', sifted')]
    if name not in DENS:
        return 'error: unknown ingredient'
    if unit == 'ml':
        cups = Fraction(amount) / Fraction(2365, 10)
    elif unit in PER_CUP:
        cups = Fraction(amount) / PER_CUP[unit]
    else:
        return 'error: unknown unit'
    d = Fraction(DENS[name])
    if packed and name == 'brown sugar':
        d = Fraction(213)
    if sifted:
        d = d * Fraction(9, 10)
    g = cups * d
    if g <= 10:
        return '%.1f g' % (math.floor(g * 10 + Fraction(1, 2)) / 10)
    return '%d g' % math.floor(g + Fraction(1, 2))
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['one cup flour', ['1', 'cup', 'flour'], '120 g'], ['exactly ten grams', ['4', 'tsp', 'flour'], '10 g'],
  ['packed brown sugar', ['1', 'cup', 'packed brown sugar'], '213 g'],
  ['regression: small-mass decimal threshold', ['2', 'ml', '  Flour '], '1.0 g'],
  ['generated control 1', ['4', 'cup', 'saffron'], 'error: unknown ingredient'],
  ['generated control 2', ['3', 'tbsp', '  Water, Sifted '], '40 g'],
  ['generated control 3', ['4', 'tbsp', 'saffron'], 'error: unknown ingredient'],
  ['generated control 4', ['3', 'ml', '  Brown Sugar '], '1.8 g']],
 [['exactly ten grams', ['4', 'tsp', 'flour'], '10 g'],
  ['packed brown sugar', ['1', 'cup', 'packed brown sugar'], '213 g'],
  ['unknown ingredient', ['1', 'cup', 'saffron'], 'error: unknown ingredient'],
  ['regression: small-mass decimal threshold', ['1/4', 'tbsp', '  Packed Water '], '3.7 g'],
  ['generated control 1', ['1/3', 'ml', 'honey'], '0.5 g'],
  ['generated control 2', ['3/4', 'ml', 'butter, sifted'], '0.6 g'],
  ['generated control 3', ['1/3', 'cup', '  Flour, Sifted '], '36 g'],
  ['generated control 4', ['3/4', 'ml', 'sugar'], '0.6 g']],
 [['packed brown sugar', ['1', 'cup', 'packed brown sugar'], '213 g'],
  ['unknown ingredient', ['1', 'cup', 'saffron'], 'error: unknown ingredient'],
  ['metric cup of water', ['250', 'ml', 'water'], '249 g'],
  ['regression: small-mass decimal threshold', ['1/3', 'tsp', 'packed sugar'], '1.4 g'],
  ['repair check: small-mass decimal threshold', ['4', 'tsp', 'flour'], '10 g'],
  ['generated control 1', ['1', 'ml', 'SUGAR'], '0.8 g'],
  ['generated control 2', ['5', 'tsp', 'water'], '25 g'],
  ['generated control 3', ['250', 'pinch', 'butter'], 'error: unknown unit']],
 [['unknown ingredient', ['1', 'cup', 'saffron'], 'error: unknown ingredient'],
  ['metric cup of water', ['250', 'ml', 'water'], '249 g'],
  ['sifted flour tablespoon', ['2', 'tbsp', 'Flour, sifted'], '14 g'],
  ['regression: small-mass decimal threshold', ['1/2', 'tbsp', 'flour'], '3.8 g'],
  ['repair check: small-mass decimal threshold', ['4', 'tsp', 'flour'], '10 g'],
  ['generated control 1', ['250', 'cup', 'butter'], '56750 g'],
  ['generated control 2', ['1/3', 'ml', 'BUTTER'], '0.3 g'],
  ['generated control 3', ['1/3', 'tsp', '  Brown Sugar, Sifted '], '0.9 g']],
 [['metric cup of water', ['250', 'ml', 'water'], '249 g'],
  ['sifted flour tablespoon', ['2', 'tbsp', 'Flour, sifted'], '14 g'],
  ['one cup flour', ['1', 'cup', 'flour'], '120 g'],
  ['regression: small-mass decimal threshold', ['1', 'tsp', 'honey'], '7.1 g'],
  ['repair check: small-mass decimal threshold', ['4', 'tsp', '  Packed Flour '], '10 g'],
  ['generated control 1', ['1/3', 'cup', '  Flour, Sifted '], '36 g'],
  ['generated control 2', ['6', 'cup', 'packed sugar'], '1200 g'],
  ['generated control 3', ['1', 'ml', 'water'], '1.0 g']]]
for label, args, expected in fixtures[N - 1]:
    check(label, solve(*args), expected)
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 fixture	Actual	Expected	Outcome
one cup flour	`120 g`	`120 g`	Passed
exactly ten grams	`10.0 g`	`10 g`	Failed
packed brown sugar	`213 g`	`213 g`	Passed
regression: small-mass decimal threshold	`1.0 g`	`1.0 g`	Passed
generated control 1	`error: unknown ingredient`	`error: unknown ingredient`	Passed
generated control 2	`40 g`	`40 g`	Passed
generated control 3	`error: unknown ingredient`	`error: unknown ingredient`	Passed
generated control 4	`1.8 g`	`1.8 g`	Passed

SHA-256 / 19dfd3a19979e6b8618b9628ec50d8fb0bbcba9864de06b1713f13ca0c03e14c

3 / The verified repair

Exit 0

"""Failure Map reference implementation. Python standard library only."""
import json
import math
from fractions import Fraction
N = 1
observations = []
def solve(amount, unit, ingredient):
    DENS = {'flour': 120, 'sugar': 200, 'brown sugar': 145, 'butter': 227, 'honey': 340, 'water': 236}
    PER_CUP = {'cup': 1, 'tbsp': 16, 'tsp': 48}
    name = ingredient.strip().lower()
    packed = name.startswith('packed ')
    if packed:
        name = name[len('packed '):]
    sifted = name.endswith(', sifted')
    if sifted:
        name = name[:-len(', sifted')]
    if name not in DENS:
        return 'error: unknown ingredient'
    if unit == 'ml':
        cups = Fraction(amount) / Fraction(2365, 10)
    elif unit in PER_CUP:
        cups = Fraction(amount) / PER_CUP[unit]
    else:
        return 'error: unknown unit'
    d = Fraction(DENS[name])
    if packed and name == 'brown sugar':
        d = Fraction(213)
    if sifted:
        d = d * Fraction(9, 10)
    g = cups * d
    if g < 10:
        return '%.1f g' % (math.floor(g * 10 + Fraction(1, 2)) / 10)
    return '%d g' % math.floor(g + Fraction(1, 2))
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['one cup flour', ['1', 'cup', 'flour'], '120 g'], ['exactly ten grams', ['4', 'tsp', 'flour'], '10 g'],
  ['packed brown sugar', ['1', 'cup', 'packed brown sugar'], '213 g'],
  ['regression: small-mass decimal threshold', ['2', 'ml', '  Flour '], '1.0 g'],
  ['generated control 1', ['4', 'cup', 'saffron'], 'error: unknown ingredient'],
  ['generated control 2', ['3', 'tbsp', '  Water, Sifted '], '40 g'],
  ['generated control 3', ['4', 'tbsp', 'saffron'], 'error: unknown ingredient'],
  ['generated control 4', ['3', 'ml', '  Brown Sugar '], '1.8 g']],
 [['exactly ten grams', ['4', 'tsp', 'flour'], '10 g'],
  ['packed brown sugar', ['1', 'cup', 'packed brown sugar'], '213 g'],
  ['unknown ingredient', ['1', 'cup', 'saffron'], 'error: unknown ingredient'],
  ['regression: small-mass decimal threshold', ['1/4', 'tbsp', '  Packed Water '], '3.7 g'],
  ['generated control 1', ['1/3', 'ml', 'honey'], '0.5 g'],
  ['generated control 2', ['3/4', 'ml', 'butter, sifted'], '0.6 g'],
  ['generated control 3', ['1/3', 'cup', '  Flour, Sifted '], '36 g'],
  ['generated control 4', ['3/4', 'ml', 'sugar'], '0.6 g']],
 [['packed brown sugar', ['1', 'cup', 'packed brown sugar'], '213 g'],
  ['unknown ingredient', ['1', 'cup', 'saffron'], 'error: unknown ingredient'],
  ['metric cup of water', ['250', 'ml', 'water'], '249 g'],
  ['regression: small-mass decimal threshold', ['1/3', 'tsp', 'packed sugar'], '1.4 g'],
  ['repair check: small-mass decimal threshold', ['4', 'tsp', 'flour'], '10 g'],
  ['generated control 1', ['1', 'ml', 'SUGAR'], '0.8 g'],
  ['generated control 2', ['5', 'tsp', 'water'], '25 g'],
  ['generated control 3', ['250', 'pinch', 'butter'], 'error: unknown unit']],
 [['unknown ingredient', ['1', 'cup', 'saffron'], 'error: unknown ingredient'],
  ['metric cup of water', ['250', 'ml', 'water'], '249 g'],
  ['sifted flour tablespoon', ['2', 'tbsp', 'Flour, sifted'], '14 g'],
  ['regression: small-mass decimal threshold', ['1/2', 'tbsp', 'flour'], '3.8 g'],
  ['repair check: small-mass decimal threshold', ['4', 'tsp', 'flour'], '10 g'],
  ['generated control 1', ['250', 'cup', 'butter'], '56750 g'],
  ['generated control 2', ['1/3', 'ml', 'BUTTER'], '0.3 g'],
  ['generated control 3', ['1/3', 'tsp', '  Brown Sugar, Sifted '], '0.9 g']],
 [['metric cup of water', ['250', 'ml', 'water'], '249 g'],
  ['sifted flour tablespoon', ['2', 'tbsp', 'Flour, sifted'], '14 g'],
  ['one cup flour', ['1', 'cup', 'flour'], '120 g'],
  ['regression: small-mass decimal threshold', ['1', 'tsp', 'honey'], '7.1 g'],
  ['repair check: small-mass decimal threshold', ['4', 'tsp', '  Packed Flour '], '10 g'],
  ['generated control 1', ['1/3', 'cup', '  Flour, Sifted '], '36 g'],
  ['generated control 2', ['6', 'cup', 'packed sugar'], '1200 g'],
  ['generated control 3', ['1', 'ml', 'water'], '1.0 g']]]
for label, args, expected in fixtures[N - 1]:
    check(label, solve(*args), expected)
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 fixture	Actual	Expected	Outcome
one cup flour	`120 g`	`120 g`	Passed
exactly ten grams	`10 g`	`10 g`	Passed
packed brown sugar	`213 g`	`213 g`	Passed
regression: small-mass decimal threshold	`1.0 g`	`1.0 g`	Passed
generated control 1	`error: unknown ingredient`	`error: unknown ingredient`	Passed
generated control 2	`40 g`	`40 g`	Passed
generated control 3	`error: unknown ingredient`	`error: unknown ingredient`	Passed
generated control 4	`1.8 g`	`1.8 g`	Passed

SHA-256 / 97359ee216f358b7336bbd37c73f3e857c096c048c9550d59b6957c30391e135

Verification & scope

A deterministic toy contract stated in full here; it is a bounded teaching model, not an authoritative reference or standards implementation. 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 2026-09-29T14:52:27.131682+00:00.

Case digest / 1e0a1ccadea8f84ab9a756490f6915a7fbac5ef8db6826765f83c9765ace9a7d