Le Trieur de Tests Instables : Transformer des Tests CI à Échecs Intermittents en un Plan de Quarantaine Classé par Cause Racine
Pourquoi ce prompt est important
Flaky tests that go untriaged do not stay flat — they compound, because "just re-run it" becomes the team's default response to any red build. A widely cited Google engineering study found flaky tests account for roughly 1 in every 300 test runs in large CI suites, and engineering teams that track the human cost put it far higher: SREs commonly estimate several lost engineer-hours per week spent manually re-running or waiting out intermittent failures across a mid-sized team. Left untriaged, flaky tests are also one of the most common reasons teams eventually weaken or disable CI gating altogether — trading the short-term convenience of a green build for the exact safety net CI exists to provide, right up until a real regression slips through disguised as "probably just flaky."
À quoi nous l'utilisons
Your team's CI dashboard has quietly accumulated 18 tests tagged "flaky" over the past two months. Nobody owns the list, and new hires are simply told to click re-run when one of them turns red. On Thursday afternoon, ahead of Friday's release freeze, the QA lead exports the flaky-test tracker — test names, failure rates, and the last few stack traces for each — and runs it through this prompt to get a sprint-ready triage: which tests to quarantine today, which ones trace back to one shared root cause, and which genuinely need another engineer's eyes before anyone touches them.
Prompt
Role: Act as a senior test infrastructure engineer / staff SDET with 10+ years of experience stabilizing CI pipelines at scale. You have seen every category of flaky test and know the difference between a real intermittent bug and a badly-written test. Context: My team's CI system is [CI SYSTEM NAME, e.g. GitHub Actions / CircleCI / Jenkins]. Over the past [TIME WINDOW, e.g. "6 weeks"], the following tests have been flagged as flaky (intermittently failing with no corresponding code change in the failing area): [PASTE FLAKY TEST LIST — for each test include: test name, approximate failure rate (e.g. "fails 1 in 8 runs"), the error message or stack trace snippet from the last 2-3 failures, and how long it has been flaky] Relevant recent changes to the codebase or infrastructure: [RECENT CODE/INFRA CHANGES, e.g. "migrated CI runners to new containers 3 weeks ago", "added a Redis caching layer", "none known"] Engineering capacity available to fix tests this sprint: [TEAM SIZE / CAPACITY, e.g. "2 engineers, 1 day each"] Task: For each test in the list, triage it into a root-cause category, assign a confidence level, and recommend a specific next action. Then identify any systemic patterns across multiple tests (e.g. several tests failing because of one shared fixture or one piece of shared infrastructure) and surface those separately, since fixing one shared root cause may resolve several "individual" flaky tests at once. Constraints: - Do not guess a root cause without citing specific evidence from the error message or stack trace provided. If the evidence given is insufficient to form a confident hypothesis, say so explicitly and mark the test as "Needs More Data" rather than inventing a plausible-sounding cause. - Do not recommend "just add a retry" or "increase the timeout" as a fix unless the evidence specifically points to a timing/race condition where that is a legitimate mitigation — and if you do recommend it, label it clearly as a MITIGATION, not a FIX, and note that the underlying bug is still open. - Keep every recommended action realistic for the stated engineering capacity — do not recommend 5 fixes if the team only has 1 day available; rank by impact instead. - If a test appears to be testing something that no longer matters (e.g. a deprecated feature), flag it as a candidate for deletion rather than a fix. - Be direct about tests that are poorly designed (e.g. depend on wall-clock time, test execution order, or shared mutable state) versus tests exposing a genuine product bug — these require different fixes. Output Format: 1. A table with these exact columns: Test Name | Suspected Root Cause Category (Shared State / Race Condition / External Dependency / Environment-Infra / Genuine Product Bug / Poorly Designed Test / Needs More Data) | Confidence (High/Medium/Low) | Evidence Cited | Recommended Action | Suggested Owner/Skillset 2. A "Quarantine Now" section: a bare list of test names that should be disabled or skipped immediately, each with a one-line justification, so they stop blocking merges while the real fix is scheduled. 3. A "Systemic Issues" section: any root cause that explains 2+ tests in the list, with the shared fix that would resolve multiple rows at once, ranked by how many tests it would unblock. 4. A "This Sprint" section: given the stated capacity, the single highest-leverage action to take first and why.
Résultat
Here is the triage based on the 6 tests and context provided:
| Test Name | Root Cause Category | Confidence | Evidence Cited | Recommended Action | Owner | |---|---|---|---|---|---| | test_checkout_coupon_apply | Shared State | High | Failure log shows AssertionError: expected balance 42.00, got 38.50 only when run after test_checkout_refund_flow in the same worker — both tests share a module-level TEST_WALLET fixture that is never reset between runs | Fix Now: move TEST_WALLET to a per-test fixture with explicit teardown | Backend/test-infra engineer, ~2 hrs | | test_session_refresh_race | Race Condition | High | Stack trace shows TokenExpiredError thrown 40ms after the refresh call returned 200 — the token refresh writes to cache asynchronously | Mitigation only: await the cache write explicitly in the test. This is NOT a fix — the underlying async write-then-read gap may also affect production under load; file a separate ticket | Platform engineer | | test_search_results_count | External Dependency | Medium | 3 of 4 recent failures logged ConnectionTimeout against the staging Elasticsearch cluster, not the application code | Quarantine Now + file an infra ticket for staging ES cluster capacity | Infra/SRE | | test_legacy_sms_notification | Unknown | Low | Only one failure instance provided and the error message is truncated — not enough evidence to confirm a root cause | Needs More Data: capture the full stack trace on the next 2 failures before assigning an owner | QA | | test_deprecated_loyalty_tier_calc | Poorly Designed Test / Deletion Candidate | High | Test covers the loyalty-tier feature that was flagged for removal in last quarter's roadmap doc | Delete rather than fix | Feature owner | | test_cart_total_rounding | Shared State | Medium | Same TEST_WALLET fixture referenced in the failure context as test_checkout_coupon_apply | Resolved by the same fix as row 1 | — |
Quarantine Now: test_search_results_count — blocked on a staging infrastructure problem, not application code; disabling it stops it from blocking unrelated merges while SRE addresses cluster capacity.
Systemic Issues: The TEST_WALLET shared fixture explains 2 of the 6 failures (test_checkout_coupon_apply and test_cart_total_rounding). Fixing the fixture's teardown logic once resolves both rows and should be prioritized over fixing either individually — this is the highest-leverage single change in the list.
This Sprint: With 1 engineer-day available, fix the TEST_WALLET fixture teardown first. It resolves 2 of 6 flaky tests in roughly 2 hours, leaving the rest of the day to quarantine test_search_results_count and file the Elasticsearch capacity ticket with infra.
Toute équipe d'ingénierie accumule des tests instables (flaky) de la même manière : l'un échoue de façon intermittente, quelqu'un clique sur « relancer », le test passe, et aucun ticket n'est jamais ouvert pour en investiguer la vraie cause. Six mois plus tard, on se retrouve avec une liste de plus de 20 tests que personne ne possède, et la confiance de l'équipe dans les signaux rouge et vert de la CI s'est tranquillement érodée.
Ce prompt est conçu comme un cadre de triage structuré, pas comme une simple demande du type « collez l'erreur, obtenez la solution », car diagnostiquer un test instable est d'abord un problème de classification avant d'être un problème de correction. La section Rôle (Role) positionne l'IA comme ingénieure senior en infrastructure de tests, précisément pour qu'elle reconnaisse les catégories connues de tests instables (état partagé, condition de concurrence, dépendance externe, infrastructure, vrai bug produit, ou test mal conçu) plutôt que de traiter chaque échec comme un mystère unique.
La section Contraintes (Constraints) fait le plus gros du travail. Elle interdit explicitement deux dérives courantes du débogage assisté par IA : inventer une cause racine plausible alors que les preuves fournies ne la confirment pas, et recommander par réflexe « ajoutez un retry » comme s'il s'agissait d'une correction et non d'un simple pansement. Ces deux consignes forcent le modèle à être honnête sur l'incertitude — ce qui compte ici plus que dans la plupart des prompts, car une mauvaise hypothèse de cause racine envoie un ingénieur poursuivre la mauvaise piste pendant une demi-journée.
Le Format de Sortie (Output Format) est volontairement découpé en quatre sections plutôt qu'un seul tableau, car la vraie valeur du triage ne réside pas dans les lignes individuelles, mais dans le fait de repérer que trois tests « sans rapport » dépendent en réalité d'une même fixture partagée. La section dédiée « Problèmes Systémiques » impose cette synthèse explicitement, au lieu de la laisser enfouie dans un tableau qu'un ingénieur fatigué ne fera que survoler. La section « Ce Sprint » relie le résultat à la capacité réelle de l'équipe, pour que le prompt produise un plan exécutable aujourd'hui, et non une liste aspirationnelle de 20 corrections que personne n'a le temps de faire.
Lancez-le chaque semaine ou avant tout gel de version, en y collant tout ce que le tableau de bord CI a signalé depuis la dernière fois — cela transforme le nettoyage des tests instables d'un backlog que personne ne touche en un triage récurrent de 15 minutes.