QA Engineering & Test Automation

The testing pyramid is a useful guide: many fast unit tests at the base (business logic, utilities), fewer integration tests in the middle (API endpoints, database interactions), and a small number of end-to-end tests at the top (critical user journeys). Each layer has different speed, cost, and feedback granularity tradeoffs. We design the right mix based on your application type — a data-heavy API needs more integration tests; a complex UI needs more E2E coverage.

We prioritise by risk: the highest-value user flows (sign-up, checkout, core feature usage), the most frequently changed code paths, and the areas with the highest historical bug rate. Chasing 100% code coverage is usually the wrong goal — we focus on meaningful coverage of the paths that matter most and build from there. An imperfect test suite that catches 70% of bugs and runs in 3 minutes is better than a perfect suite nobody runs because it takes 40 minutes.

Unit tests verify a single function or component in isolation, with dependencies mocked. They are fast (milliseconds) and great for business logic. Integration tests verify multiple components working together — an API endpoint calling a real database, for example. They are slower but catch interface mismatches. End-to-end tests drive a real browser through user flows and catch issues that only appear when the entire stack is assembled. We use all three layers strategically.

Flaky tests — tests that pass and fail non-deterministically — erode trust in the test suite faster than almost anything else. We prevent flakiness by avoiding arbitrary sleep() calls (using explicit wait conditions instead), cleaning up test data between runs, avoiding shared state, and using deterministic test data. When a flaky test appears in CI we quarantine it immediately, fix the root cause, and track flakiness metrics over time.

We use Playwright as our default E2E tool. It supports Chromium, Firefox, and WebKit, has excellent TypeScript support, built-in test retry, trace recording for debugging failures, and a visual UI mode. It is also significantly faster and more reliable than Cypress for most workloads. We use Cypress when a project already has a mature Cypress suite — migration is not worth the disruption.

Tests run on every pull request in GitHub Actions or GitLab CI. We parallelise slow test suites across multiple workers to keep feedback fast (under 5 minutes for most suites). We cache node_modules and Playwright browser binaries between runs. Failed tests block the merge. We also set up test result summaries that show which tests failed and why without requiring engineers to dig through logs.

We write integration tests that send HTTP requests to the API against a real database and verify response schemas, status codes, authentication enforcement, and edge cases. We use supertest for Express/Node APIs and the built-in Next.js test utilities for Next.js API routes. For public APIs we also write contract tests using Pact to ensure consumer expectations are not broken by provider changes.

Yes. We start by auditing the existing code to identify the highest-risk areas — most changed, most business-critical, most bug-prone. We add tests incrementally, prioritising integration and E2E tests for critical flows first (these give the fastest return on investment) and unit tests for complex business logic. We avoid the trap of writing tests just to hit a coverage number and focus on tests that would catch real bugs.

We run integration tests against a real database — either a Dockerised PostgreSQL instance in CI or the Supabase/Firebase local emulator. Test data is inserted as part of the test setup and cleaned up afterwards. We test not only happy paths but also constraint violations, race conditions for concurrent writes, and transaction rollback behaviour. Running against a real database catches issues that in-memory mocks miss.

We use factory functions (with a library like Fishery or Faker) to generate realistic test data programmatically, avoiding fixed fixtures that become stale. For integration tests each test creates its own isolated data set and cleans up after itself. We never share mutable state between tests. For E2E tests we seed a known starting state before each test run using a database migration or API call.

Yes. Automated tests are excellent at regression prevention but miss visual regressions, usability issues, and unexpected behaviour on specific devices or browsers. We perform exploratory manual QA before major releases — structured test sessions where a QA engineer actively tries to break the application. We document findings, triage by severity, and track fixes before sign-off.

We track several metrics: coverage percentage on business-critical modules, flaky test rate (tests that fail non-deterministically), mean time to detect a regression (how quickly tests catch a bug after it is introduced), and CI pipeline duration. We review these in monthly retainer reports and adjust the test strategy when coverage gaps or slowdowns emerge. A healthy test suite is an investment that compounds — each added test makes future development safer.