The cost of fixing a bug grows exponentially throughout the development lifecycle. A requirement flaw caught during design costs minutes to correct. The same issue discovered in production may require emergency rollbacks, data correction scripts, and customer notifications.
Our QA involvement begins during requirements review. We write test cases before implementation begins, ensuring acceptance criteria are unambiguous. Automation suites run against every pull request, catching regressions within minutes. Performance baselines track response times across releases. Security scans integrate into CI pipelines — not as an afterthought before launch.
Manual testing involves human execution of test cases — ideal for exploratory scenarios, usability evaluation, and short-term projects. Automated testing scripts run without human intervention, suited for regression suites, load testing, and repeated validation. Most effective QA strategies combine both approaches.
We target 80% code coverage for critical paths with diminishing returns beyond that. Coverage percentage matters less than what is covered. We prioritize business logic, data transformations, and integration points over trivial getters/setters or configuration loading.
We design resilient selectors and component-level tests that survive UI changes. Page Object Model patterns centralize locators. Automated smoke tests run on every deployment to catch catastrophic failures. Full regression suites run nightly or on demand before release candidates.
Yes. We integrate with Jenkins, GitHub Actions, GitLab CI, CircleCI, or Azure DevOps. Our automation suites produce JUnit XML reports, test execution videos, and performance metrics that your pipeline can consume — failing builds on critical test failures.