Guides, tutorials, and updates from the Diffie team on AI-powered testing, CI integration, and shipping with confidence.
Diffie QA reads every pull request, writes a test for the change, runs it in a real browser on that pull request’s own copy of Diffie, and posts the result with a recording.
Every pull request on the Diffie repo gets its own complete copy of Diffie, and our regression suite runs against it before anything merges. Here is how it works, in plain English.
What regression testing catches that other test types miss, how it differs from smoke and sanity testing, what belongs in a suite, and a worked example of a silent pricing regression caught in under two minutes.
Smoke testing answers one question — did this build come up at all. What belongs in a smoke test, how it differs from sanity and regression testing, and what a build that renders perfectly and does nothing actually looks like.
User acceptance testing verifies that software does what the business agreed it would do — not that it works. Who should own UAT, how it differs from QA testing, and the class of bug only UAT catches.
What a test case is, how it differs from a scenario and a script, the fields that earn their place in a template, four worked examples from a real checkout flow, and how to write cases that survive a redesign.
Where E2E sits against unit and integration tests, which journeys are worth covering, why E2E suites become the most expensive tests you own, and what a full checkout run actually looks like.
An honest comparison of Playwright and Cypress — architecture, browser support, parallelism, debugging and language options — plus the maintenance problem both share and what actually reduces it.
A look at how we wire Diffie's own GitHub Actions CI to run a 3-test Diffie suite on every pull request, including the actual workflow file we ship today.
A first-look lab benchmark from real Diffie-generated Playwright tests. 893 records analyzed: median generation time 3.6 minutes, 70.8% first-run pass rate, 21.2% recovery rate after first failure, with the full methodology and exclusions disclosed.
The definitive data-driven report on flaky tests in 2026 — root-cause breakdown, cost per flake, fix-time benchmarks, and the strategies high-performing teams use to eliminate flakiness.
A data-driven look at E2E testing adoption, maintenance costs, flaky test rates, AI testing growth, and what the best teams do differently — compiled from industry reports and surveys.
No dedicated QA team? Here's a practical testing strategy for solo developers and small teams that keeps your app reliable without slowing you down.
Test suites don't just cost time to write — they cost time to keep alive. Here's how to calculate the real maintenance burden and what to do about it.
A technical look at how AI test generation works under the hood — from natural language input to browser automation to self-healing assertions. No marketing fluff.
What actually changes when you stop writing test scripts by hand and let an AI agent handle it? A side-by-side look at both approaches.
You don't need 100% test coverage. You need to test the five flows that will cost you customers if they break.
Discover why automated tests fail in production—from UI changes to timing issues—and how AI self-healing tests eliminate the maintenance cycle.
You don't need to know JavaScript or CSS selectors to test your web app end-to-end. Here's how to write browser tests in plain English.
Learn what AI testing is, how it works, and how it compares to traditional test automation with Selenium, Cypress, and Playwright.
A 5-minute setup to run your Diffie test suites automatically on every pull request — no plugins, no config files, just one workflow.