Insights on API testing, shift-left, no-code automation, and CI/CD. Guides and best practices for modern QA teams.
Automate API testing in GitHub Actions with reusable workflows, quality gates, and PR status checks. Step-by-step guide from workflow YAML to production deployment gates.
Automate API testing in GitHub Actions with reusable workflows, quality gates, and PR status checks. Step-by-step guide from workflow YAML to production deployment gates.
Configure automated API testing in Azure DevOps pipelines with quality gates, JUnit reporting, and coverage tracking. Step-by-step guide from YAML pipeline setup to deployment gates.
Set up automated API testing in Jenkins pipelines with plugins, Jenkinsfile configuration, and quality gates. Complete guide from plugin installation to production-ready API quality enforcement.
OpenAPI 3.1 introduced full JSON Schema compatibility, webhooks support, and simplified nullable handling. Learn how these changes impact your API test generation, contract validation, and migration strategy.
Schema-first API development designs the API contract before writing implementation code. Learn how this approach enables parallel development, automated test generation, and contract-driven quality gates.
Migrating from Swagger 2.0 to OpenAPI 3.x unlocks richer test generation, better schema validation, and modern tooling. This step-by-step guide covers spec conversion, test suite updates, and CI/CD pipeline adjustments.
Compare the best OpenAPI testing tools for automated test generation, contract validation, and CI/CD integration. Side-by-side analysis of Total Shift Left, Schemathesis, Dredd, Postman, and more.
API quality gates are automated checkpoints in a CI/CD pipeline that evaluate test results -- pass rate, endpoint coverage, schema compliance, and response time -- against predefined thresholds. If results fall below the threshold, the pipeline blocks the deployment, preventing broken APIs from reaching production.
API regression testing is the practice of re-running existing API tests after every code change to verify that previously working functionality still works correctly. It catches unintended side effects -- a fix in one endpoint breaking another, a schema change invalidating consumers, or a performance degradation introduced by new code -- before those regressions reach production.
