Quality Engineering & Testing — Test Automation & AI-Powered QA Services

We start with AI-powered code analysis to generate an initial set of unit tests automatically — tools like Diffblue for Java and AI-assisted generation for .NET and Python can produce hundreds of tests in days rather than months. We then review the generated tests for accuracy, add integration tests for critical paths, and establish a coverage baseline. This gives the team a safety net before any modernisation work begins, making refactoring safe from day one rather than at the end.

AI test generation tools analyse source code — the methods, their inputs, outputs, and side effects — and automatically produce test cases covering happy paths, edge cases, and error conditions. For Java, Diffblue Cover reads bytecode and generates JUnit tests without requiring source code documentation. For other languages, GitHub Copilot and similar tools generate tests from method signatures and existing code patterns. The generated tests are reviewed by engineers and refined, but the time to initial coverage is dramatically shorter than hand-writing tests.

A basic test automation framework with CI integration, a chosen tool stack, and initial test suites for critical paths typically takes 2–4 weeks. This includes tool selection, framework scaffolding, CI pipeline integration, test data management setup, and coverage reporting. The time to meaningful coverage (60-70% on critical paths) depends on the codebase size — for a typical mid-sized application, allow 6–10 weeks including AI-assisted test generation.

We use the testing pyramid as a guide: approximately 70-80% unit tests, 15-20% integration and API tests, and 5-10% end-to-end tests. Unit tests are fast and cheap — you can have thousands of them and run them in seconds. E2E tests are slow, brittle, and expensive to maintain — keep them to the critical user journeys only. The biggest mistake teams make is an inverted pyramid: too many E2E tests that run slowly and fail for arbitrary reasons, undermining trust in the entire test suite.

We build production-representative load models using available data: server access logs (even anonymised), application metrics, and business context (peak trading windows, seasonal patterns, marketing event calendars). When production data isn't available, we use traffic shaping models based on industry benchmarks for similar applications. The goal is a load test that exercises the same bottlenecks production traffic would — not a synthetic benchmark that stresses something irrelevant.

Yes. We integrate three layers of security testing into CI/CD: SAST (static analysis with SonarQube or Snyk Code) runs on every commit and fails PRs with critical findings; SCA (dependency scanning with Snyk or OWASP Dependency-Check) blocks vulnerable dependency versions from merging; and DAST (dynamic scanning with OWASP ZAP) runs against a deployed staging environment on every merge to main. We configure severity thresholds so low-severity findings don't block development while critical vulnerabilities are blocking gates.