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FA-85556 / Ride-hailing fare and surge pricing / Open access

Shared price rounded up against the rider · case 01

Discounted prices are a cent higher than quoted whenever the discount leaves a fraction.

Verified by executionVariant 1 · 6 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The discounted price is rounded up.

VERIFIED REPAIR

Floor the discounted price.

Unsuccessful approach: Nearest-cent rounding still rounds some prices up.

Case contract

In a shared ride, a rider counts as matched if they shared at least min_overlap_s seconds with another rider; a match needs at least two such riders, otherwise nobody is matched. Matched riders pay solo x (100 - discount_pct)% and unmatched riders pay solo x 95%, both floored to the cent in the rider's favor. Return id -> price.

Why this case matters

Ride-hailing prices are computed per trip at scale; ordering, unit and boundary slips become systematic over- or under-charging.

1 / The failure

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

N = 1
observations = []
def solve(riders, discount_pct, min_overlap_s):
    matched = [r['id'] for r in riders if r['overlap_s'] >= min_overlap_s]
    if len(matched) < 2:
        matched = []
    out = {}
    for r in riders:
        pct = discount_pct if r['id'] in matched else 5
        out[r['id']] = -(-r['solo'] * (100 - pct) // 100)
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 877}, {'id': 'r1', 'overlap_s': 180, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 3, 'solo': 877}],
    30, 180],
   {'r0': 833, 'r1': 2379, 'r2': 833}),
  ('partial repair probe: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 877}, {'id': 'r1', 'overlap_s': 180, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 0, 'solo': 2505}, {'id': 'r3', 'overlap_s': 3, 'solo': 1234}],
    40, 180],
   {'r0': 526, 'r1': 1503, 'r2': 2379, 'r3': 1172}),
  ('second regression',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 877}, {'id': 'r1', 'overlap_s': 600, 'solo': 877},
     {'id': 'r2', 'overlap_s': 180, 'solo': 1999}],
    40, 180],
   {'r0': 526, 'r1': 526, 'r2': 1199}),
  ('extra case 1',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 1234}, {'id': 'r1', 'overlap_s': 179, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 180, 'solo': 1234}, {'id': 'r3', 'overlap_s': 3, 'solo': 1999}],
    25, 180],
   {'r0': 925, 'r1': 2379, 'r2': 925, 'r3': 1899}),
  ('extra case 2', [[{'id': 'r0', 'overlap_s': 0, 'solo': 2505}], 25, 180], {'r0': 2379}),
  ('extra case 3',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1999}, {'id': 'r1', 'overlap_s': 181, 'solo': 2505}], 25, 180],
   {'r0': 1899, 'r1': 2379})],
 [('regression: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 877}, {'id': 'r1', 'overlap_s': 181, 'solo': 1999}], 25, 180],
   {'r0': 657, 'r1': 1499}),
  ('partial repair probe: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1999}, {'id': 'r1', 'overlap_s': 600, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 600, 'solo': 877}],
    30, 180],
   {'r0': 1399, 'r1': 1753, 'r2': 613}),
  ('second regression',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1234}, {'id': 'r1', 'overlap_s': 180, 'solo': 1999}], 25, 180],
   {'r0': 1172, 'r1': 1899}),
  ('extra case 1',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 877}, {'id': 'r1', 'overlap_s': 179, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 600, 'solo': 1999}, {'id': 'r3', 'overlap_s': 0, 'solo': 877}],
    30, 180],
   {'r0': 613, 'r1': 1899, 'r2': 1399, 'r3': 833}),
  ('extra case 2',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1234}, {'id': 'r1', 'overlap_s': 180, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 181, 'solo': 2505}],
    30, 180],
   {'r0': 1172, 'r1': 1399, 'r2': 1753}),
  ('extra case 3',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1999}, {'id': 'r1', 'overlap_s': 0, 'solo': 2505}], 30, 180],
   {'r0': 1899, 'r1': 2379})],
 [('regression: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1234}, {'id': 'r1', 'overlap_s': 3, 'solo': 877}], 30, 180],
   {'r0': 1172, 'r1': 833}),
  ('partial repair probe: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 877}, {'id': 'r1', 'overlap_s': 3, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 181, 'solo': 2505}, {'id': 'r3', 'overlap_s': 0, 'solo': 1999}],
    40, 180],
   {'r0': 833, 'r1': 1172, 'r2': 2379, 'r3': 1899}),
  ('second regression', [[{'id': 'r0', 'overlap_s': 180, 'solo': 1999}], 40, 180], {'r0': 1899}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 2505}, {'id': 'r1', 'overlap_s': 181, 'solo': 2505}], 40, 180],
   {'r0': 1503, 'r1': 1503}),
  ('extra case 1', [[{'id': 'r0', 'overlap_s': 600, 'solo': 1234}], 40, 180], {'r0': 1172}),
  ('extra case 2', [[{'id': 'r0', 'overlap_s': 0, 'solo': 2505}], 30, 180], {'r0': 2379})],
 [('regression: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 3, 'solo': 877}, {'id': 'r1', 'overlap_s': 0, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 180, 'solo': 2505}],
    30, 180],
   {'r0': 833, 'r1': 1172, 'r2': 2379}),
  ('partial repair probe: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1234}, {'id': 'r1', 'overlap_s': 0, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 181, 'solo': 1999}, {'id': 'r3', 'overlap_s': 3, 'solo': 2505}],
    40, 180],
   {'r0': 1172, 'r1': 1899, 'r2': 1899, 'r3': 2379}),
  ('second regression', [[{'id': 'r0', 'overlap_s': 181, 'solo': 1234}], 40, 180], {'r0': 1172}),
  ('extra case 1',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 877}, {'id': 'r1', 'overlap_s': 181, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 3, 'solo': 877}],
    40, 180],
   {'r0': 833, 'r1': 1899, 'r2': 833}),
  ('extra case 2', [[{'id': 'r0', 'overlap_s': 3, 'solo': 1999}], 40, 180], {'r0': 1899}),
  ('extra case 3',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 2505}, {'id': 'r1', 'overlap_s': 0, 'solo': 877},
     {'id': 'r2', 'overlap_s': 179, 'solo': 1999}, {'id': 'r3', 'overlap_s': 0, 'solo': 1999}],
    30, 180],
   {'r0': 2379, 'r1': 833, 'r2': 1899, 'r3': 1899})],
 [('regression: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1999}, {'id': 'r1', 'overlap_s': 181, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 0, 'solo': 2505}],
    25, 180],
   {'r0': 1899, 'r1': 1172, 'r2': 2379}),
  ('partial repair probe: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1234}, {'id': 'r1', 'overlap_s': 179, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 179, 'solo': 1999}],
    25, 180],
   {'r0': 1172, 'r1': 2379, 'r2': 1899}),
  ('second regression', [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}], 25, 180], {'r0': 1899}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 2505}, {'id': 'r1', 'overlap_s': 180, 'solo': 2505}], 40, 180],
   {'r0': 1503, 'r1': 1503}),
  ('extra case 1', [[{'id': 'r0', 'overlap_s': 3, 'solo': 1234}], 25, 180], {'r0': 1172}),
  ('extra case 2',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1234}, {'id': 'r1', 'overlap_s': 181, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 180, 'solo': 1234}, {'id': 'r3', 'overlap_s': 600, 'solo': 1234}],
    25, 180],
   {'r0': 1172, 'r1': 1878, 'r2': 925, 'r3': 925})]]
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 fixtureActualExpectedOutcome
regression: rider-favorable rounding{'r0': 834, 'r1': 2380, 'r2': 834}{'r0': 833, 'r1': 2379, 'r2': 833}Failed
partial repair probe: rider-favorable rounding{'r0': 527, 'r1': 1503, 'r2': 2380, 'r3': 1173}{'r0': 526, 'r1': 1503, 'r2': 2379, 'r3': 1172}Failed
second regression{'r0': 527, 'r1': 527, 'r2': 1200}{'r0': 526, 'r1': 526, 'r2': 1199}Failed
extra case 1{'r0': 926, 'r1': 2380, 'r2': 926, 'r3': 1900}{'r0': 925, 'r1': 2379, 'r2': 925, 'r3': 1899}Failed
extra case 2{'r0': 2380}{'r0': 2379}Failed
extra case 3{'r0': 1900, 'r1': 2380}{'r0': 1899, 'r1': 2379}Failed

SHA-256 / 1c3125c773f594a71d9a3895285163dfb4ce54c7a564568b83ee44799a5c921b

2 / The unsuccessful fix

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

N = 1
observations = []
def solve(riders, discount_pct, min_overlap_s):
    matched = [r['id'] for r in riders if r['overlap_s'] >= min_overlap_s]
    if len(matched) < 2:
        matched = []
    out = {}
    for r in riders:
        pct = discount_pct if r['id'] in matched else 5
        out[r['id']] = round(r['solo'] * (100 - pct) / 100)
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 877}, {'id': 'r1', 'overlap_s': 180, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 3, 'solo': 877}],
    30, 180],
   {'r0': 833, 'r1': 2379, 'r2': 833}),
  ('partial repair probe: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 877}, {'id': 'r1', 'overlap_s': 180, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 0, 'solo': 2505}, {'id': 'r3', 'overlap_s': 3, 'solo': 1234}],
    40, 180],
   {'r0': 526, 'r1': 1503, 'r2': 2379, 'r3': 1172}),
  ('second regression',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 877}, {'id': 'r1', 'overlap_s': 600, 'solo': 877},
     {'id': 'r2', 'overlap_s': 180, 'solo': 1999}],
    40, 180],
   {'r0': 526, 'r1': 526, 'r2': 1199}),
  ('extra case 1',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 1234}, {'id': 'r1', 'overlap_s': 179, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 180, 'solo': 1234}, {'id': 'r3', 'overlap_s': 3, 'solo': 1999}],
    25, 180],
   {'r0': 925, 'r1': 2379, 'r2': 925, 'r3': 1899}),
  ('extra case 2', [[{'id': 'r0', 'overlap_s': 0, 'solo': 2505}], 25, 180], {'r0': 2379}),
  ('extra case 3',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1999}, {'id': 'r1', 'overlap_s': 181, 'solo': 2505}], 25, 180],
   {'r0': 1899, 'r1': 2379})],
 [('regression: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 877}, {'id': 'r1', 'overlap_s': 181, 'solo': 1999}], 25, 180],
   {'r0': 657, 'r1': 1499}),
  ('partial repair probe: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1999}, {'id': 'r1', 'overlap_s': 600, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 600, 'solo': 877}],
    30, 180],
   {'r0': 1399, 'r1': 1753, 'r2': 613}),
  ('second regression',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1234}, {'id': 'r1', 'overlap_s': 180, 'solo': 1999}], 25, 180],
   {'r0': 1172, 'r1': 1899}),
  ('extra case 1',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 877}, {'id': 'r1', 'overlap_s': 179, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 600, 'solo': 1999}, {'id': 'r3', 'overlap_s': 0, 'solo': 877}],
    30, 180],
   {'r0': 613, 'r1': 1899, 'r2': 1399, 'r3': 833}),
  ('extra case 2',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1234}, {'id': 'r1', 'overlap_s': 180, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 181, 'solo': 2505}],
    30, 180],
   {'r0': 1172, 'r1': 1399, 'r2': 1753}),
  ('extra case 3',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1999}, {'id': 'r1', 'overlap_s': 0, 'solo': 2505}], 30, 180],
   {'r0': 1899, 'r1': 2379})],
 [('regression: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1234}, {'id': 'r1', 'overlap_s': 3, 'solo': 877}], 30, 180],
   {'r0': 1172, 'r1': 833}),
  ('partial repair probe: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 877}, {'id': 'r1', 'overlap_s': 3, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 181, 'solo': 2505}, {'id': 'r3', 'overlap_s': 0, 'solo': 1999}],
    40, 180],
   {'r0': 833, 'r1': 1172, 'r2': 2379, 'r3': 1899}),
  ('second regression', [[{'id': 'r0', 'overlap_s': 180, 'solo': 1999}], 40, 180], {'r0': 1899}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 2505}, {'id': 'r1', 'overlap_s': 181, 'solo': 2505}], 40, 180],
   {'r0': 1503, 'r1': 1503}),
  ('extra case 1', [[{'id': 'r0', 'overlap_s': 600, 'solo': 1234}], 40, 180], {'r0': 1172}),
  ('extra case 2', [[{'id': 'r0', 'overlap_s': 0, 'solo': 2505}], 30, 180], {'r0': 2379})],
 [('regression: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 3, 'solo': 877}, {'id': 'r1', 'overlap_s': 0, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 180, 'solo': 2505}],
    30, 180],
   {'r0': 833, 'r1': 1172, 'r2': 2379}),
  ('partial repair probe: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1234}, {'id': 'r1', 'overlap_s': 0, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 181, 'solo': 1999}, {'id': 'r3', 'overlap_s': 3, 'solo': 2505}],
    40, 180],
   {'r0': 1172, 'r1': 1899, 'r2': 1899, 'r3': 2379}),
  ('second regression', [[{'id': 'r0', 'overlap_s': 181, 'solo': 1234}], 40, 180], {'r0': 1172}),
  ('extra case 1',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 877}, {'id': 'r1', 'overlap_s': 181, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 3, 'solo': 877}],
    40, 180],
   {'r0': 833, 'r1': 1899, 'r2': 833}),
  ('extra case 2', [[{'id': 'r0', 'overlap_s': 3, 'solo': 1999}], 40, 180], {'r0': 1899}),
  ('extra case 3',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 2505}, {'id': 'r1', 'overlap_s': 0, 'solo': 877},
     {'id': 'r2', 'overlap_s': 179, 'solo': 1999}, {'id': 'r3', 'overlap_s': 0, 'solo': 1999}],
    30, 180],
   {'r0': 2379, 'r1': 833, 'r2': 1899, 'r3': 1899})],
 [('regression: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1999}, {'id': 'r1', 'overlap_s': 181, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 0, 'solo': 2505}],
    25, 180],
   {'r0': 1899, 'r1': 1172, 'r2': 2379}),
  ('partial repair probe: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1234}, {'id': 'r1', 'overlap_s': 179, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 179, 'solo': 1999}],
    25, 180],
   {'r0': 1172, 'r1': 2379, 'r2': 1899}),
  ('second regression', [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}], 25, 180], {'r0': 1899}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 2505}, {'id': 'r1', 'overlap_s': 180, 'solo': 2505}], 40, 180],
   {'r0': 1503, 'r1': 1503}),
  ('extra case 1', [[{'id': 'r0', 'overlap_s': 3, 'solo': 1234}], 25, 180], {'r0': 1172}),
  ('extra case 2',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1234}, {'id': 'r1', 'overlap_s': 181, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 180, 'solo': 1234}, {'id': 'r3', 'overlap_s': 600, 'solo': 1234}],
    25, 180],
   {'r0': 1172, 'r1': 1878, 'r2': 925, 'r3': 925})]]
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 fixtureActualExpectedOutcome
regression: rider-favorable rounding{'r0': 833, 'r1': 2380, 'r2': 833}{'r0': 833, 'r1': 2379, 'r2': 833}Failed
partial repair probe: rider-favorable rounding{'r0': 526, 'r1': 1503, 'r2': 2380, 'r3': 1172}{'r0': 526, 'r1': 1503, 'r2': 2379, 'r3': 1172}Failed
second regression{'r0': 526, 'r1': 526, 'r2': 1199}{'r0': 526, 'r1': 526, 'r2': 1199}Passed
extra case 1{'r0': 926, 'r1': 2380, 'r2': 926, 'r3': 1899}{'r0': 925, 'r1': 2379, 'r2': 925, 'r3': 1899}Failed
extra case 2{'r0': 2380}{'r0': 2379}Failed
extra case 3{'r0': 1899, 'r1': 2380}{'r0': 1899, 'r1': 2379}Failed

SHA-256 / db9afc529771dbe59129bfc326527ce8ce322cb08ae45bf7b90eda118efc0541

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(riders, discount_pct, min_overlap_s):
    matched = [r['id'] for r in riders if r['overlap_s'] >= min_overlap_s]
    if len(matched) < 2:
        matched = []
    out = {}
    for r in riders:
        pct = discount_pct if r['id'] in matched else 5
        out[r['id']] = r['solo'] * (100 - pct) // 100
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 877}, {'id': 'r1', 'overlap_s': 180, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 3, 'solo': 877}],
    30, 180],
   {'r0': 833, 'r1': 2379, 'r2': 833}),
  ('partial repair probe: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 877}, {'id': 'r1', 'overlap_s': 180, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 0, 'solo': 2505}, {'id': 'r3', 'overlap_s': 3, 'solo': 1234}],
    40, 180],
   {'r0': 526, 'r1': 1503, 'r2': 2379, 'r3': 1172}),
  ('second regression',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 877}, {'id': 'r1', 'overlap_s': 600, 'solo': 877},
     {'id': 'r2', 'overlap_s': 180, 'solo': 1999}],
    40, 180],
   {'r0': 526, 'r1': 526, 'r2': 1199}),
  ('extra case 1',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 1234}, {'id': 'r1', 'overlap_s': 179, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 180, 'solo': 1234}, {'id': 'r3', 'overlap_s': 3, 'solo': 1999}],
    25, 180],
   {'r0': 925, 'r1': 2379, 'r2': 925, 'r3': 1899}),
  ('extra case 2', [[{'id': 'r0', 'overlap_s': 0, 'solo': 2505}], 25, 180], {'r0': 2379}),
  ('extra case 3',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1999}, {'id': 'r1', 'overlap_s': 181, 'solo': 2505}], 25, 180],
   {'r0': 1899, 'r1': 2379})],
 [('regression: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 877}, {'id': 'r1', 'overlap_s': 181, 'solo': 1999}], 25, 180],
   {'r0': 657, 'r1': 1499}),
  ('partial repair probe: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1999}, {'id': 'r1', 'overlap_s': 600, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 600, 'solo': 877}],
    30, 180],
   {'r0': 1399, 'r1': 1753, 'r2': 613}),
  ('second regression',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1234}, {'id': 'r1', 'overlap_s': 180, 'solo': 1999}], 25, 180],
   {'r0': 1172, 'r1': 1899}),
  ('extra case 1',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 877}, {'id': 'r1', 'overlap_s': 179, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 600, 'solo': 1999}, {'id': 'r3', 'overlap_s': 0, 'solo': 877}],
    30, 180],
   {'r0': 613, 'r1': 1899, 'r2': 1399, 'r3': 833}),
  ('extra case 2',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1234}, {'id': 'r1', 'overlap_s': 180, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 181, 'solo': 2505}],
    30, 180],
   {'r0': 1172, 'r1': 1399, 'r2': 1753}),
  ('extra case 3',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1999}, {'id': 'r1', 'overlap_s': 0, 'solo': 2505}], 30, 180],
   {'r0': 1899, 'r1': 2379})],
 [('regression: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1234}, {'id': 'r1', 'overlap_s': 3, 'solo': 877}], 30, 180],
   {'r0': 1172, 'r1': 833}),
  ('partial repair probe: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 877}, {'id': 'r1', 'overlap_s': 3, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 181, 'solo': 2505}, {'id': 'r3', 'overlap_s': 0, 'solo': 1999}],
    40, 180],
   {'r0': 833, 'r1': 1172, 'r2': 2379, 'r3': 1899}),
  ('second regression', [[{'id': 'r0', 'overlap_s': 180, 'solo': 1999}], 40, 180], {'r0': 1899}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 2505}, {'id': 'r1', 'overlap_s': 181, 'solo': 2505}], 40, 180],
   {'r0': 1503, 'r1': 1503}),
  ('extra case 1', [[{'id': 'r0', 'overlap_s': 600, 'solo': 1234}], 40, 180], {'r0': 1172}),
  ('extra case 2', [[{'id': 'r0', 'overlap_s': 0, 'solo': 2505}], 30, 180], {'r0': 2379})],
 [('regression: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 3, 'solo': 877}, {'id': 'r1', 'overlap_s': 0, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 180, 'solo': 2505}],
    30, 180],
   {'r0': 833, 'r1': 1172, 'r2': 2379}),
  ('partial repair probe: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1234}, {'id': 'r1', 'overlap_s': 0, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 181, 'solo': 1999}, {'id': 'r3', 'overlap_s': 3, 'solo': 2505}],
    40, 180],
   {'r0': 1172, 'r1': 1899, 'r2': 1899, 'r3': 2379}),
  ('second regression', [[{'id': 'r0', 'overlap_s': 181, 'solo': 1234}], 40, 180], {'r0': 1172}),
  ('extra case 1',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 877}, {'id': 'r1', 'overlap_s': 181, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 3, 'solo': 877}],
    40, 180],
   {'r0': 833, 'r1': 1899, 'r2': 833}),
  ('extra case 2', [[{'id': 'r0', 'overlap_s': 3, 'solo': 1999}], 40, 180], {'r0': 1899}),
  ('extra case 3',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 2505}, {'id': 'r1', 'overlap_s': 0, 'solo': 877},
     {'id': 'r2', 'overlap_s': 179, 'solo': 1999}, {'id': 'r3', 'overlap_s': 0, 'solo': 1999}],
    30, 180],
   {'r0': 2379, 'r1': 833, 'r2': 1899, 'r3': 1899})],
 [('regression: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1999}, {'id': 'r1', 'overlap_s': 181, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 0, 'solo': 2505}],
    25, 180],
   {'r0': 1899, 'r1': 1172, 'r2': 2379}),
  ('partial repair probe: rider-favorable rounding',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1234}, {'id': 'r1', 'overlap_s': 179, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 179, 'solo': 1999}],
    25, 180],
   {'r0': 1172, 'r1': 2379, 'r2': 1899}),
  ('second regression', [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}], 25, 180], {'r0': 1899}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 2505}, {'id': 'r1', 'overlap_s': 180, 'solo': 2505}], 40, 180],
   {'r0': 1503, 'r1': 1503}),
  ('extra case 1', [[{'id': 'r0', 'overlap_s': 3, 'solo': 1234}], 25, 180], {'r0': 1172}),
  ('extra case 2',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1234}, {'id': 'r1', 'overlap_s': 181, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 180, 'solo': 1234}, {'id': 'r3', 'overlap_s': 600, 'solo': 1234}],
    25, 180],
   {'r0': 1172, 'r1': 1878, 'r2': 925, 'r3': 925})]]
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 fixtureActualExpectedOutcome
regression: rider-favorable rounding{'r0': 833, 'r1': 2379, 'r2': 833}{'r0': 833, 'r1': 2379, 'r2': 833}Passed
partial repair probe: rider-favorable rounding{'r0': 526, 'r1': 1503, 'r2': 2379, 'r3': 1172}{'r0': 526, 'r1': 1503, 'r2': 2379, 'r3': 1172}Passed
second regression{'r0': 526, 'r1': 526, 'r2': 1199}{'r0': 526, 'r1': 526, 'r2': 1199}Passed
extra case 1{'r0': 925, 'r1': 2379, 'r2': 925, 'r3': 1899}{'r0': 925, 'r1': 2379, 'r2': 925, 'r3': 1899}Passed
extra case 2{'r0': 2379}{'r0': 2379}Passed
extra case 3{'r0': 1899, 'r1': 2379}{'r0': 1899, 'r1': 2379}Passed

SHA-256 / 032ba91803516b0a71bec4453413486f7f3aa57396f4d7bfecb8e809db0786da

Verification & scope

A deterministic toy pricing contract stipulated for this example; it does not reproduce the pricing of any real ride-hailing operator or regulator. 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 2026-09-29T14:50:41.552786+00:00.

Case digest / deead3d3b93248fffec50d30d4dc83a856cd4978b8ebe81fc734b0fa8abecc07