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Claude Opus 5.5 (effort default (no --effort flag) · thinking on) on Fix a flaky test suite caused by set ordering, ignored date parameter and global random state - without touching the tests

AClaude Opus 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-17 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first try
Estimated cost
$0.12
Wall time
19s
Human step-ins
0
Outcome
Pass

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Summary

Exact prompt

# Task: fix the flaky billing tests `tests/test_billing.py` passes on some CI runs and fails on others (and fails on every run on certain days). Make `billing.py` deterministic so the suite passes on every run, on any date, under any `PYTHONHASHSEED`. Rules: - **Do not modify `tests/test_billing.py`** - the tests describe the intended behaviour. - Invoices are ordered by customer name, case-insensitively (ties broken by the exact name), and numbered in that order. - `today` defaults to the current date only when not passed. - The audit sample must be reproducible: the same `orders`, `today` and `audit_size` always give the same sample, independent of the global `random` state and of process/hash seed - and must not modify the global `random` state. It should still look random (not simply the first N invoices): different months should generally give different samples. - Keep the function signature (you may add keyword-only params with defaults). Standard library only. Run tests with `python3 -m pytest tests` if pytest is available, or call the test functions directly. No network.

Three independent sources of flakiness (hash-seeded set order, `today` overwritten, unseeded global random). The common shortcut random.seed(...) mutates global state and is caught, as is returning the first N invoices. Original benchmark task written for usecaserank.com (not based on a specific public post). Headless, offline run on a reproducible fixture. Each run's notes say how its cost was measured (estimates are marked).

Success criteria: 1. tests_intact: tests/test_billing.py is byte-identical to the original. 2. visible_tests_all_seeds: all visible tests pass under 6 different PYTHONHASHSEED values. 3. cross_process_deterministic: a hidden 29-customer batch is identical across 4 processes with different hash seeds. 4. global_random_untouched: build_batch does not change the global random state. 5. audit_looks_random: audit samples across 12 months take at least 6 distinct values and are not always the first N invoices. 6. default_today_works: omitting today uses the current date.

Setup notes: Use Case Scout batch 5 case e12-flaky-nondeterminism (offline fixture). Prepared with batch5/prepare.sh; hidden key/tests kept outside the agent's work dir. Prompt = TASK.md verbatim. Scored with the scout's scorer: `python score.py WORKDIR KEYDIR`. Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. Claude Code's own reported cost for claude-opus-5.5: $0.120068. Score: 6/6 checks. Cost is an ESTIMATE: measured tokens x pricing.json list price (Claude Pro subscription, nothing billed); input_tokens include cache reads and writes. Single run.

Estimated cost List-price estimate; not actually billed.

Metrics

Estimated cost
$0.12
Wall time
19s
Human step-ins
0
Turns
5
Model usage
ModelInput tokOutput tokCost
AClaude Opus 5.5anthropic/claude-opus-5.5effort default (no --effort flag) · thinking on77,8371,882$0.12

Reproduce this run

  1. Use the use case prompt on the use case page.
  2. Same setup: Claude Opus 5.5 (anthropic/claude-opus-5.5, effort default (no --effort flag) · thinking on) via Claude Code 2.1.299 (headless -p), cloud.
  3. It counts as a success only if it meets every success criterion: 1. tests_intact: tests/test_billing.py is byte-identical to the original. 2. visible_tests_all_seeds: all visible tests pass under 6 different PYTHONHASHSEED values. 3. cross_process_deterministic: a hidden 29-customer batch is identical across 4 processes with different hash seeds. 4. global_random_untouched: build_batch does not change the global random state. 5. audit_looks_random: audit samples across 12 months take at least 6 distinct values and are not always the first N invoices. 6. default_today_works: omitting today uses the current date.
  4. Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.

How runs are scored: Methodology.

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Evidence log
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  "result": "I rewrote `billing.py` and all three tests now pass under five different `PYTHONHASHSEED` values (0, 1, 7, 42 and random). pytest isn't installed here, so I called the test functions directly. I didn't touch `tests/test_billing.py`.\n\nThere were three causes of the flakiness:\n\n1. **`today` was always overwritten** with the real current date, so the due-date and invoice-number tests only passed on certain days. It now defaults to the current date only when `today` isn't passed.\n2. **Customers came from an unsorted `set`**, so their order (and therefore invoice numbering) changed with the hash seed. They're now sorted case-insensitively, with ties broken by the exact name. The test's expected order is `acme, Bravo, delta, zeta`.\n3. **The audit sample used the shared global `random` module.** It now uses a private random generator seeded from a SHA-256 hash of the date, `audit_size` and each customer's name and total. The same inputs always give the same sample, whatever the hash seed or global random state, and the global `random` state is left untouched (I checked that it's identical before and after a call).\n\nThe sample still looks random and changes by month. For example, Jan\u2013Apr 2026 give samples of 003/004, 001/003, 004/003 and 003/002. The seed uses the full date, so two runs in the same month with different days will usually pick different invoices. I also now calculate the due date once per batch instead of once per invoice; the result is the same.",
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