#testing

GitHub Agentic Workflows let AI agents run as CI. Here's how we connected Gaffer's MCP server for automated weekly test health reviews.

Per-build notifications tell you a test failed. Health score alerts tell you your suite is degrading. Here's how they work.

Most teams retry and move on. A systematic approach to flaky test management: flip rate detection, CI cost prioritization, and a fix-quarantine-delete framework.

Your observability stack monitors production but not tests. Gaffer's OpenTelemetry export puts test metrics into Datadog, Grafana, and any OTLP endpoint.

AI coding tools know your code but not your tests. Test intelligence via MCP bridges the gap with flaky detection, failure clustering, and coverage data.

Agents write code and run tests. But when tests fail, they're flying blind. Here's how test intelligence closes the loop.

Flaky tests are an invisible tax on engineering velocity. Industry data from Atlassian, Google, and Microsoft shows 13-16% of test failures are flaky. Here's how to calculate the real cost.

Dogfooding Gaffer's MCP coverage tools on our own codebase.

JUnit XML is the de facto standard for test result reporting. Here's how the format works, how to generate it from any test framework, and how to use it for CI integrations.

Test artifacts are the files and data generated during test execution - reports, logs, screenshots, coverage data, and more. Here's what they are, why they matter, and how to manage them.

Using Gaffer's own analytics to find and fix slow E2E tests. Here's what I found and how I fixed it.