How do you identify and reduce flaky tests at scale?
QA Engineer (Web)
answer
I fight flaky tests with data, isolation, and determinism. First, I tag and quarantine failures, aggregate instability metrics, and auto-rerun to classify true flakes. Then I remove nondeterminism: stabilize time, network, and concurrency; use explicit waits instead of sleeps; seed data; and mock externals. I harden infrastructure (idempotent setup/teardown, hermetic environments) and gate merges with flake budgets. Finally, I refactor brittle tests, add observability, and delete or redesign low-value scenarios.
Long Answer
In large automated test suites, flaky tests erode trust, slow delivery, and hide real regressions. The cure is not more retries; it is a systematic program that makes tests deterministic, environments hermetic, and feedback loops fast. My approach combines telemetry, engineering discipline, and clear ownership.
1) Detect and classify with telemetry
You cannot reduce what you cannot see. I instrument the pipeline to capture per-test pass rate, failure signatures, duration variance, and environment metadata. A test is “flaky” if it both passes and fails on the same code without changes. Automatic reruns help classification: if a single rerun flips the result, mark it as flaky and route it to triage. Trend dashboards highlight top offenders by failure frequency and blast radius.
2) Quarantine without losing signal
Flaky tests must not block releases. I quarantine known flakes into a non-blocking lane while keeping them visible in reports. Service level objectives for test stability define a flake budget; when the budget burns, we throttle feature work and focus on stabilization. This prevents the “green by retries” anti-pattern.
3) Eliminate nondeterminism at the test level
Most flakiness comes from timing, data, and concurrency. I replace fixed sleeps with event-driven waits (element stability, network idle, queue drained). I freeze clocks via time mocking, fix time zones and locales, and seed random values with known seeds. I ensure idempotent setup and teardown, unique test data (namespaced tenants or randomized identifiers), and no shared global state across tests. For user interface automation, I disable animations or wait for motion to settle.
4) Control external variability
Third-party services, networks, and clocks introduce noise. I isolate systems by mocking or recording external calls (service virtualization, contract tests). For end-to-end tests that must call real dependencies, I run them in a tightly controlled staging environment, pin data fixtures, and enforce rate limits to avoid cross-talk.
5) Make environments hermetic and reproducible
I run tests in containerized, version-pinned environments with deterministic browsers, fonts, locales, and resource limits. Test data is provisioned from migrations or snapshots, then reset per test or per suite. Parallelization is safe because every worker owns isolated resources (databases, queues, or topic partitions).
6) Strengthen assertions and selectors
Brittle user interface selectors and over-specific assertions cause flakes. I prefer stable identifiers (data-test attributes), assert on meaningful states rather than transient text, and avoid chaining multiple fragile conditions. For asynchronous workflows, I assert intermediate states explicitly (spinner visible, then gone) instead of sleeping.
7) Improve orchestration and concurrency
Race conditions surface as flakiness. I avoid shared mutable state, use transactional fixtures, and serialize tests that mutate global resources. For microservices, I align readiness checks and health probes with actual dependencies, then gate tests on real readiness rather than container start.
8) Observability and forensics
Every failed test yields artifacts: screenshots, console logs, videos, traces, and network recordings. I attach these to continuous integration reports and link them to failure signatures. This makes triage fast and knowledge shareable.
9) Governance and continuous cleanup
We treat flaky tests as defects with owners, due dates, and root-cause analyses. Low-signal or overlapping scenarios are removed or merged. I maintain a “do not write” list of known brittle patterns and a “golden path” cookbook that demonstrates robust patterns for the automated test suite.
10) Prevent regressions with policy
Merge gates fail if stability dips below target. New tests must demonstrate deterministic behavior locally and in a stress pipeline (randomized order, parallel runs, and low-resource mode). This bakes reliability into the development culture, not just the quality team.
By measuring relentlessly, quarantining safely, eliminating sources of nondeterminism, and institutionalizing stable patterns, large suites become trustworthy, fast, and maintainable—and flaky tests become rare exceptions instead of routine noise.
Table
Common Mistakes
- Masking flaky tests with infinite retries, turning red into fake green.
- Using fixed sleeps instead of event-driven waits, creating timing races.
- Sharing global state or test data across workers, causing nondeterministic collisions.
- Hitting real third-party services in every run, importing external instability.
- Overly specific selectors and assertions that break on minor user interface changes.
- Non-hermetic environments: different locales, time zones, browsers, or font stacks between agents.
- Ignoring artifacts; no logs or screenshots, forcing guesswork in triage.
- Letting flakes linger without ownership, allowing the automated test suite to lose credibility.
Sample Answers
Junior:
“I tag failures, auto-rerun once to confirm flaky tests, and quarantine them so they do not block merges. Then I replace sleeps with waits, freeze time, and use unique test data. I add screenshots and logs to every failure for quick triage.”
Mid:
“I track per-test stability and failure signatures, enforce a flake budget, and mock external services. Tests run in pinned containers with deterministic browsers. I use data-test selectors, event-driven waits, and transactional fixtures to remove nondeterminism.”
Senior:
“I run a reliability program: telemetry, quarantine, and root-cause elimination. We stabilize infrastructure (hermetic environments, idempotent setup), adopt contract tests and virtualization, and gate merges on stability. Randomized order and parallel stress catch order dependencies. Low-value brittle tests are refactored or removed to keep the automated test suite lean and trustworthy.”
Evaluation Criteria
Look for a data-driven approach to flaky tests: telemetry, failure signature clustering, and stability dashboards. Strong answers quarantine flakes without hiding them, replace sleeps with event-driven waits, freeze time and locale, seed random values, and ensure idempotent setup/teardown with isolated data. They control external variability through service virtualization and contract tests, run in hermetic containers, and produce rich artifacts for forensics. They mention merge gates, flake budgets, randomized order, and parallel stress runs. Red flags include “just retry,” hitting live externals by default, global shared state, and missing ownership.
Preparation Tips
- Add auto-rerun on fail and record pass/fail ratios per test for a week; build a top offenders list.
- Enable artifacts (screenshots, videos, logs, har files) and link them from continuous integration.
- Replace two fixed sleeps with state-based waits; verify stability improves.
- Freeze time, set a fixed locale and time zone in runners; seed random number generation.
- Introduce namespaced fixtures and transactional cleanup; confirm no cross-test contamination.
- Stand up service virtualization or contract tests for one external dependency; measure flake reduction.
- Run a nightly stress job: randomized order, parallelism, and low-resource mode; inspect new failures.
- Define a flake budget and a rotation to own triage; document fixes and patterns in a playbook.
Real-world Context
A retail platform cut flaky tests by sixty percent in three sprints after quarantining top offenders, replacing sleeps with event-driven waits, and freezing time and locale. A fintech team stopped calling live payment sandboxes for every end-to-end run, switching to contract tests and a virtualized gateway; spurious failures vanished and pipelines sped up. A media company containerized test runners with deterministic browsers and fonts; visual timing flakes dropped dramatically. Another organization added failure signature clustering and a flake budget, making instability visible and triggering focused stabilization weeks that restored confidence in the automated test suite.
Key Takeaways
- Measure and classify flaky tests; quarantine but keep visible.
- Remove nondeterminism: event-driven waits, frozen time/locale, seeded data.
- Hermetic containers and mocked externals keep runs reproducible.
- Strengthen selectors and assertions; add rich artifacts for triage.
- Enforce flake budgets and stability gates to prevent regression.
Practice Exercise
Scenario:
Your company’s web pipeline runs ten thousand tests per day. Failures often disappear on rerun, blocking releases and eroding trust. You must deliver a plan that reduces flaky tests and protects delivery speed.
Tasks:
- Instrument telemetry: record per-test pass rate, failure signatures (exception type, stack, message), duration, runner metadata, and environment hashes.
- Add single auto-rerun on failure and mark tests “suspected flaky” if rerun passes. Quarantine them into a non-blocking lane while keeping their results on the main dashboard.
- Replace fixed sleeps with event-driven waits in three high-flake suites; freeze time, set a fixed time zone and locale, and seed random number generation for all runners.
- Make setup and teardown idempotent: namespaced fixtures, transactional cleanup, unique identifiers, and parallel-safe resources.
- Introduce service virtualization for one external dependency and convert two user interface suites to contract tests for that dependency.
- Containerize runners with pinned browser versions, fonts, and locales; enforce resource quotas.
- Enable artifacts (logs, screenshots, videos, har files) and link them to failure signatures.
- Establish a flake budget and a weekly triage rotation; set a merge gate that fails if stability drops below the target.
- Run a nightly stress job with randomized order and maximum parallelism. Track new order dependencies and fix them.
Deliverable:
A stabilization report showing reduced flake rate, faster pipelines, and a maintainable program that keeps the automated test suite reliable over time.