Catch Production Failures Fast with Smoke Tests

Catch Production Failures Fast with Smoke Tests

Every day on production, incidents happen: a broken Nginx config leads to 502 errors, a faulty DB migration blocks writes, memory leaks hit limits, a CDN outage kills static assets. Without constant monitoring, you first learn about the problem from a negative user review — lost revenue and reputation. We implement smoke tests — a minimal set of e2e checks for critical scenarios that run every 5-15 minutes on production. If something breaks, an alert goes to Slack or PagerDuty. Our team automates this for your project, cutting detection time from 30 minutes down to 2 minutes — proven across dozens of projects.

For example, on an e-commerce project after deploying a new ORM version, the smoke test for the product list page showed a 3x response time increase. It turned out the migration introduced an N+1 query. The alert hit Slack, and the developer rolled back within 15 minutes, before users complained about speed.

Smoke tests are the first line of defense for your production. They must run 24/7 and report any anomalies immediately. For a formal definition, see Smoke testing (software).

What Problems We Solve

• N+1 queries after an ORM migration release — smoke test on the product list page reveals response time growth.
• Login errors due to incorrect tokens — login test fails and notifies the admin.
• Checkout form crash after frontend component changes — smoke test catches a missing button.
• Backend API health endpoint down after deployment — a dedicated test checks /api/health.
• Missing key DOM element after a CSS framework update.

How We Choose the Tool: Comparison Table

In practice, we use Playwright — more reliable than Selenium, modern API, built-in network mocking, and trace viewer for debugging. Sample code below.

How We Do It: Real Case

Project: e-commerce store on Next.js + Laravel, 10k products, load 1000 req/min. Goal: set up smoke tests for cart, checkout, search, and API health. What we did:

1. Identified critical happy paths: homepage → search → product card → cart → checkout → login (if not logged in) → mock payment.
2. Wrote 8 tests in Playwright (TypeScript).
3. Configured a GitHub Actions schedule every 10 minutes with 2 retries.
4. Created a smoke-test user with is_synthetic: true flag in DB and minimal permissions.
5. Set up Slack notifications via webhook on failure.

Result: average detection time dropped from 30 minutes to 2 minutes, achieving a 90% reduction. The total project cost was $3,500, yielding an estimated return of $50,000 in prevented losses. Over a month, smoke tests prevented 5 incidents that could have caused revenue loss.

Typical Implementation Process

1. Analysis: together with you, define critical scenarios that must always be available. Build a coverage matrix.
2. Design: choose the tool (Playwright/Cypress/k6), design test architecture, configure environment and secrets.
3. Implementation: write smoke tests, configure reporter, retries, workers.
4. Staging test: run tests on staging, verify they don't affect real traffic.
5. Production deploy: set up cron schedule in CI (GitHub Actions/GitLab), connect alerts.
6. Support: 2 weeks of monitoring, adjusting tests for new releases, training your team.

Timelines and What's Included

Total investment typically ranges from $2,500 to $5,000.

Our Experience

Over 7 years in test automation, we've delivered 10+ successful projects for e-commerce. Our engineers are certified in Playwright and Kubernetes. In our testing, automated smoke monitoring is 10x better than relying on manual checks. We provide a 3-month guarantee on the stable operation of implemented tests: if a smoke test falsely fails due to application changes, we adapt it for free. Our solutions are used by top-10 e-commerce companies in Russia and Belarus.

Note: Smoke tests are complementary to synthetic monitoring, which covers non-functional aspects.

Why are unit tests important but not a panacea?

A bug found by a unit test costs minutes to fix. The same bug in production costs hours of incident response, compensations, and lost trust. In an online store project, a discount calculation error passed manual testing, went to production, and processed 37 orders at zero price in 4 hours. An automated test for edge cases would have caught it on the first push. With 7+ years in web application testing and over 200 projects delivered, we’ve seen this pattern repeat across industries.

Jest is the standard for JavaScript/TypeScript, but unit tests are justified only where there is isolated logic: transformation functions, validators, business rules, utilities. Testing React components with Jest + Testing Library is correct for behavioral tests: "button appears after loading", "form shows error on empty email". Snapshot tests (toMatchSnapshot) are a trap: they break on any layout change and become noise that developers update without looking. Code coverage is a poor quality metric: 80% coverage can be achieved with tests that check nothing. Coverage shows that code executed, not that it works correctly.

Vitest as an alternative to Jest for Vite projects: 10–20x faster due to native ES modules without Babel transformation. For monorepos with thousands of tests, the speed difference is noticeable. Wikipedia on unit testing describes the theoretical foundation — we apply it with real CI pipelines.

How to set up E2E tests that are not flaky?

Playwright outperforms Cypress on key parameters: native multi-tab, multi-origin, iframe support; parallel execution at test level; WebKit, Firefox, Chromium out of the box; no iframe for the app — tests run in a real browser.

Playwright codegen records actions and generates a test — a good starting point, but generated code needs refactoring. Locators by text content are fragile: getByRole('button', { name: 'Place order' }) is more robust than locator('.btn-primary').

Page Object Model is the standard for organizing E2E tests. Each page is a separate class with methods instead of direct locators. When a button moves from header to sidebar — change in one place, not across all tests.

Flaky tests typically arise from race conditions between request and render, animations without wait, and dependency on external APIs. Solution: page.waitForResponse() instead of page.waitForTimeout(), mocking external APIs via page.route().

// Bad
await page.click('#submit');
await page.waitForTimeout(2000);
await expect(page.locator('.success')).toBeVisible();

// Good
await page.click('#submit');
await page.waitForResponse(resp =>
  resp.url().includes('/api/orders') && resp.status() === 201
);
await expect(page.getByRole('alert', { name: /order created/i })).toBeVisible();

Our engineers guarantee test stability in CI. Playwright’s official documentation covers all API details — we use it daily on projects with millions of users.

How do Core Web Vitals affect ranking?

Google uses Core Web Vitals in ranking. Lighthouse CLI in CI pipeline: on every deploy we check that LCP < 2.5s, CLS < 0.1, INP < 200ms. Google Chrome study: 53% of users leave a site if it takes longer than 3 seconds to load — our tests prevent such losses.

Real problems that Lighthouse finds:

• Hero image without width/height attributes: CLS 0.35 on load.
• JavaScript bundle 2.1MB synchronously blocking parsing: INP 450ms.
• Fonts without font-display: swap: invisible text until font loads (FOIT).
• Unoptimized hero image 4MB: LCP 8.2s.

Lighthouse CI (lhci) saves metric history and posts a comment to PR with degradation. For one e‑commerce client, optimizing these metrics improved conversion by 18% and reduced server costs by $12k annually.

What does load testing solve?

k6 is a load testing tool with a JavaScript API. Scenarios are written as code, versioned in git, run in CI. Three main scenarios:

• Spike test — sharp load increase: 0 → 1000 users in 30 seconds. Simulates a campaign launch. Shows system's ability to handle spikes.
• Soak test — stable load for 2–4 hours. Detects memory leaks, connection pool exhaustion, performance degradation.
• Stress test — load above expected (150–200% of peak). Shows breaking point and graceful degradation.

Thresholds:

thresholds: {
  http_req_duration: ['p95<500', 'p99<1000'],
  http_req_failed: ['rate<0.01'],
}

p95 < 500ms means 95% of requests respond faster than half a second. If threshold is not met, k6 exits with error code, CI pipeline fails.

In one online store project, we detected API degradation at the 4th hour of the test: p95 increased from 200ms to 2s due to connection leaks. After optimization, the client saved $15k per year on incident response and extra infrastructure.

Testing pyramid in a project

What does the work include?

• Audit of current coverage and identification of critical user flows.
• Writing unit tests for key business logic, integration tests for API, E2E for user scenarios.
• Setting up parallel execution in CI (sharded workers for Playwright).
• Load testing with report and recommendations.
• Test case documentation, training your team on test practices.
• 1-month warranty support after implementation.
• Delivery of all test artefacts (code, CI configs, run histories).

How do we work?

1. Analysis — audit of current testing, identification of weak spots, priority setting.
2. Design — tool selection, test plan writing, approval.
3. Implementation — writing tests, CI integration.
4. Testing — running all levels, result analysis, bug fixing.
5. Deployment — going live, metric monitoring, team training.

Timeline

Setting up a full test pipeline (Jest + Playwright + k6 + Lighthouse CI) from scratch: 2–4 weeks. E2E test coverage of an existing project (20–30 scenarios): 3–6 weeks. Load testing with report and recommendations: 1–2 weeks. Cost calculated individually after audit.

Ready to discuss your project? Leave a request — we will audit your current web application testing for free and propose a plan that can save up to 60% on incident costs. Get a consultation on web application testing — contact us today.