FAILURE MAP
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FA-61036 / Bond day-count conventions / Open access

NL/365 no-leap day count: at most one leap day is removed · case 01

Spans of more than four years are overstated by the extra leap days.

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

ROOT CAUSE

The leap-day adjustment is a yes/no flag instead of a count.

THE FAILURE

The leap-day adjustment is a yes/no flag instead of a count.

Unsuccessful approach: Counting only the start and end years ignores leap years in between.

Case contract

Inputs start and end [y,m,d]. If end < start return "end before start". Days are actual days minus the number of 29 February dates lying in (start, end]. Return [days, days/365 rounded to 9 decimals].

Why this case matters

Bond accrual and pricing systems depend on exact day-count arithmetic; a single-day error changes settlement cash.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
from fractions import Fraction
N = 1
observations = []
def solve(a, b):
    A = datetime.date(*a)
    B = datetime.date(*b)
    if B < A:
        return 'end before start'
    def leap(y):
        return (y % 4 == 0 and y % 100 != 0) or y % 400 == 0
    leapdays = 1 if any(leap(y) and A < datetime.date(y, 2, 29) <= B for y in range(A.year, B.year + 1)) else 0
    days = (B - A).days - leapdays
    return [days, round(days / 365, 9)]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression multi-year leap count 1', [[2028, 1, 1], [2035, 3, 19]], [2632, 7.210958904]], ['regression multi-year leap count 2', [[2096, 2, 28], [2106, 10, 17]], [3881, 10.632876712]], ['partial repair probe 1', [[1999, 5, 21], [2003, 3, 20]], [1398, 3.830136986]], ['partial repair probe 2', [[2056, 10, 31], [2062, 8, 14]], [2112, 5.78630137]], ['boundary control 1', [[2024, 2, 29], [2025, 2, 28]], [365, 1.0]], ['boundary control 2', [[2024, 3, 1], [2024, 12, 31]], [305, 0.835616438]], ['normal control 1', [[2093, 5, 29], [2093, 9, 30]], [124, 0.339726027]], ['normal control 2', [[1904, 10, 23], [1905, 7, 21]], [271, 0.742465753]]], [['regression multi-year leap count 1', [[2053, 6, 28], [2064, 6, 5]], [3992, 10.936986301]], ['regression multi-year leap count 2', [[1959, 12, 30], [1964, 9, 16]], [1720, 4.712328767]], ['partial repair probe 1', [[1938, 2, 2], [1942, 6, 17]], [1595, 4.369863014]], ['partial repair probe 2', [[2102, 3, 21], [2106, 4, 21]], [1491, 4.084931507]], ['boundary control 1', [[2024, 2, 29], [2025, 2, 28]], [365, 1.0]], ['boundary control 2', [[2099, 1, 1], [2101, 1, 1]], [730, 2.0]], ['normal control 1', [[2095, 4, 29], [2095, 9, 2]], [126, 0.345205479]], ['normal control 2', [[2096, 1, 1], [2097, 12, 27]], [725, 1.98630137]]], [['regression multi-year leap count 1', [[2031, 4, 30], [2042, 4, 8]], [3993, 10.939726027]], ['regression multi-year leap count 2', [[2086, 8, 20], [2094, 3, 26]], [2773, 7.597260274]], ['partial repair probe 1', [[1978, 3, 20], [1983, 7, 7]], [1934, 5.298630137]], ['partial repair probe 2', [[2021, 1, 20], [2026, 2, 28]], [1864, 5.106849315]], ['boundary control 1', [[2024, 3, 1], [2024, 12, 31]], [305, 0.835616438]], ['boundary control 2', [[2023, 2, 28], [2024, 2, 29]], [365, 1.0]], ['normal control 1', [[2004, 1, 1], [2006, 5, 6]], [855, 2.342465753]], ['normal control 2', [[2061, 4, 26], [2061, 9, 24]], [151, 0.41369863]]], [['regression multi-year leap count 1', [[2016, 6, 29], [2024, 8, 6]], [2958, 8.104109589]], ['regression multi-year leap count 2', [[1995, 1, 29], [2001, 6, 8]], [2320, 6.356164384]], ['partial repair probe 1', [[2094, 10, 26], [2097, 2, 28]], [855, 2.342465753]], ['partial repair probe 2', [[2089, 3, 24], [2095, 12, 22]], [2463, 6.747945205]], ['boundary control 1', [[2023, 2, 28], [2024, 2, 29]], [365, 1.0]], ['boundary control 2', [[2020, 1, 1], [2020, 1, 1]], [0, 0.0]], ['normal control 1', [[2074, 2, 4], [2075, 2, 4]], [365, 1.0]], ['normal control 2', [[2076, 5, 16], [2076, 7, 27]], [72, 0.197260274]]], [['regression multi-year leap count 1', [[2011, 9, 13], [2020, 2, 16]], [3076, 8.42739726]], ['regression multi-year leap count 2', [[2037, 2, 6], [2047, 6, 19]], [3783, 10.364383562]], ['partial repair probe 1', [[2036, 10, 17], [2042, 2, 13]], [1944, 5.326027397]], ['partial repair probe 2', [[2013, 11, 1], [2019, 12, 24]], [2243, 6.145205479]], ['boundary control 1', [[2023, 2, 28], [2024, 2, 29]], [365, 1.0]], ['boundary control 2', [[2024, 2, 29], [2025, 2, 28]], [365, 1.0]], ['normal control 1', [[2000, 3, 15], [2004, 6, 27]], [1564, 4.284931507]], ['normal control 2', [[2024, 1, 1], [2024, 9, 26]], [268, 0.734246575]]]]
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 multi-year leap count 1[2633, 7.21369863][2632, 7.210958904]Failed
regression multi-year leap count 2[3882, 10.635616438][3881, 10.632876712]Failed
partial repair probe 1[1398, 3.830136986][1398, 3.830136986]Passed
partial repair probe 2[2112, 5.78630137][2112, 5.78630137]Passed
boundary control 1[365, 1.0][365, 1.0]Passed
boundary control 2[305, 0.835616438][305, 0.835616438]Passed
normal control 1[124, 0.339726027][124, 0.339726027]Passed
normal control 2[271, 0.742465753][271, 0.742465753]Passed

SHA-256 / 543c1c8e90663e5530d9b106ad92bfc19f9968d5d5ae35a67e552c91efb94582

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
from fractions import Fraction
N = 1
observations = []
def solve(a, b):
    A = datetime.date(*a)
    B = datetime.date(*b)
    if B < A:
        return 'end before start'
    def leap(y):
        return (y % 4 == 0 and y % 100 != 0) or y % 400 == 0
    leapdays = sum(1 for y in {A.year, B.year} if leap(y) and A < datetime.date(y, 2, 29) <= B)
    days = (B - A).days - leapdays
    return [days, round(days / 365, 9)]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression multi-year leap count 1', [[2028, 1, 1], [2035, 3, 19]], [2632, 7.210958904]], ['regression multi-year leap count 2', [[2096, 2, 28], [2106, 10, 17]], [3881, 10.632876712]], ['partial repair probe 1', [[1999, 5, 21], [2003, 3, 20]], [1398, 3.830136986]], ['partial repair probe 2', [[2056, 10, 31], [2062, 8, 14]], [2112, 5.78630137]], ['boundary control 1', [[2024, 2, 29], [2025, 2, 28]], [365, 1.0]], ['boundary control 2', [[2024, 3, 1], [2024, 12, 31]], [305, 0.835616438]], ['normal control 1', [[2093, 5, 29], [2093, 9, 30]], [124, 0.339726027]], ['normal control 2', [[1904, 10, 23], [1905, 7, 21]], [271, 0.742465753]]], [['regression multi-year leap count 1', [[2053, 6, 28], [2064, 6, 5]], [3992, 10.936986301]], ['regression multi-year leap count 2', [[1959, 12, 30], [1964, 9, 16]], [1720, 4.712328767]], ['partial repair probe 1', [[1938, 2, 2], [1942, 6, 17]], [1595, 4.369863014]], ['partial repair probe 2', [[2102, 3, 21], [2106, 4, 21]], [1491, 4.084931507]], ['boundary control 1', [[2024, 2, 29], [2025, 2, 28]], [365, 1.0]], ['boundary control 2', [[2099, 1, 1], [2101, 1, 1]], [730, 2.0]], ['normal control 1', [[2095, 4, 29], [2095, 9, 2]], [126, 0.345205479]], ['normal control 2', [[2096, 1, 1], [2097, 12, 27]], [725, 1.98630137]]], [['regression multi-year leap count 1', [[2031, 4, 30], [2042, 4, 8]], [3993, 10.939726027]], ['regression multi-year leap count 2', [[2086, 8, 20], [2094, 3, 26]], [2773, 7.597260274]], ['partial repair probe 1', [[1978, 3, 20], [1983, 7, 7]], [1934, 5.298630137]], ['partial repair probe 2', [[2021, 1, 20], [2026, 2, 28]], [1864, 5.106849315]], ['boundary control 1', [[2024, 3, 1], [2024, 12, 31]], [305, 0.835616438]], ['boundary control 2', [[2023, 2, 28], [2024, 2, 29]], [365, 1.0]], ['normal control 1', [[2004, 1, 1], [2006, 5, 6]], [855, 2.342465753]], ['normal control 2', [[2061, 4, 26], [2061, 9, 24]], [151, 0.41369863]]], [['regression multi-year leap count 1', [[2016, 6, 29], [2024, 8, 6]], [2958, 8.104109589]], ['regression multi-year leap count 2', [[1995, 1, 29], [2001, 6, 8]], [2320, 6.356164384]], ['partial repair probe 1', [[2094, 10, 26], [2097, 2, 28]], [855, 2.342465753]], ['partial repair probe 2', [[2089, 3, 24], [2095, 12, 22]], [2463, 6.747945205]], ['boundary control 1', [[2023, 2, 28], [2024, 2, 29]], [365, 1.0]], ['boundary control 2', [[2020, 1, 1], [2020, 1, 1]], [0, 0.0]], ['normal control 1', [[2074, 2, 4], [2075, 2, 4]], [365, 1.0]], ['normal control 2', [[2076, 5, 16], [2076, 7, 27]], [72, 0.197260274]]], [['regression multi-year leap count 1', [[2011, 9, 13], [2020, 2, 16]], [3076, 8.42739726]], ['regression multi-year leap count 2', [[2037, 2, 6], [2047, 6, 19]], [3783, 10.364383562]], ['partial repair probe 1', [[2036, 10, 17], [2042, 2, 13]], [1944, 5.326027397]], ['partial repair probe 2', [[2013, 11, 1], [2019, 12, 24]], [2243, 6.145205479]], ['boundary control 1', [[2023, 2, 28], [2024, 2, 29]], [365, 1.0]], ['boundary control 2', [[2024, 2, 29], [2025, 2, 28]], [365, 1.0]], ['normal control 1', [[2000, 3, 15], [2004, 6, 27]], [1564, 4.284931507]], ['normal control 2', [[2024, 1, 1], [2024, 9, 26]], [268, 0.734246575]]]]
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 multi-year leap count 1[2633, 7.21369863][2632, 7.210958904]Failed
regression multi-year leap count 2[3882, 10.635616438][3881, 10.632876712]Failed
partial repair probe 1[1399, 3.832876712][1398, 3.830136986]Failed
partial repair probe 2[2113, 5.789041096][2112, 5.78630137]Failed
boundary control 1[365, 1.0][365, 1.0]Passed
boundary control 2[305, 0.835616438][305, 0.835616438]Passed
normal control 1[124, 0.339726027][124, 0.339726027]Passed
normal control 2[271, 0.742465753][271, 0.742465753]Passed

SHA-256 / ea861e44c92e800d1aa6e35ca970532fabfd64dda06767ea0787859a8ceac94b

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

A deterministic toy contract stated explicitly in the contract field; no claim of conformance to any published convention text. 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 2026-09-29T14:46:51.391171+00:00.

Case digest / d4803d090ba57a520226f20d4853b4b814896a801728afbbba5b74c3c5e38ac3