AI-powered testing platform. Write tests in plain English, run them everywhere.
Load Testing
Artillery is a load testing tool built for developers. You write YAML, you run a command, you get results. No GUI to learn, no Java heap to tune, no XML to wrestle. If you can write a curl command, you can write an Artillery test. This post covers installation, the
Testing
"Just mock it" is the most common piece of testing advice—and one of the most misused. Mocks and stubs both replace real dependencies in tests, but they have fundamentally different purposes. Using one when you need the other leads to tests that pass while hiding real problems.
Load Testing
Running load tests manually before releases is better than not running them. Running them automatically on every pull request is better still. Blocking a merge when performance regresses is the goal. This post covers integrating Artillery into GitHub Actions and GitLab CI, setting thresholds that actually gate deployments, and strategies
Testing
Running Artillery tests locally gives you confidence. Running them in CI turns that confidence into a deployment gate — your pipeline fails if performance degrades, before users notice. This guide covers integrating Artillery into GitHub Actions, GitLab CI, and Jenkins, along with threshold configuration, report generation, and patterns for different pipeline
Testing
Artillery covers the basics well — YAML config, HTTP scenarios, simple ramp patterns. The advanced features are where it earns its place for complex load testing: custom JavaScript processors, stateful sessions, WebSocket testing, multi-environment configs, and plugin integration. This guide covers them all. Custom Processors Custom processors let you write
Accessibility Testing
Simple semantic HTML is enough for most UI elements — buttons, links, forms, headings. But complex interactive patterns like modals, tabs, accordions, and disclosure menus don't have native HTML equivalents. These require ARIA roles and attributes to communicate structure and state to assistive technology. The ARIA Authoring Practices Guide
Kubernetes Testing
ArgoCD manages Kubernetes deployments from Git. When it works, it's invisible — changes merge, ArgoCD syncs, applications update. When it breaks, it can be subtle: a sync that appears healthy but isn't applying changes, a health check that's too permissive, or a rollout that pauses
Security Testing
Container security is different from traditional application security. Your attack surface includes the base image, installed packages, runtime configuration, Kubernetes RBAC, network policies, and more — not just your application code. Aqua Security is purpose-built for this environment. This guide covers what Aqua does, how to integrate it into your
Testing
Aqua ALM and PractiTest are both enterprise test management platforms that go beyond simple test case storage. Both integrate with Jira. Both offer requirements traceability and reporting dashboards. Choosing between them comes down to deployment model, how deeply you need requirements management, and how your team scales. This comparison covers
Testing
Aqua ALM is an enterprise test management platform built around one idea that most tools ignore: requirements and tests belong together. Where tools like TestRail or Zephyr treat test cases as standalone records, Aqua ALM ties every test case back to a requirement, giving you traceability from business need to
Testing
Requirements traceability answers one question: if this requirement fails, which tests will catch it? Without a formal traceability model, the answer is usually "hope so." Aqua ALM's traceability system lets you answer it precisely -- linking each requirement to the test cases that verify it, tracking execution
Testing
Most QA teams live in two tools simultaneously: a test management platform and Jira. Without integration, that means manual copy-paste of defects, status updates that drift out of sync, and no clear link between a Jira story and the tests that verify it. Aqua ALM's Jira connector