TestSeed Updates & Changelog

A common challenge when designing test data is complex business logic. For example: "The sum of all line items in an order must exactly match the total invoice amount." When developers encounter this, they often look for a tool that can "calculate" these values during generation. But here is the truth: Your mock data generator shouldn't be a second version of your backend. If you try to replicate complex mathematical dependencies inside your data generation tool, you are essentially building a "Shadow Business Engine" that you now have to maintain.

The "Quick and Dirty" Trap Most teams start their testing journey with one of two things: static SQL dumps or random generators like Faker.js. It feels fast. You "pay" almost nothing upfront.

Small updates. Real impact. At the beginning of January, we shipped a set of small, focused improvements to TestSeed. No flashy features. No buzzwords. Just things that make test data more reliable, more explainable, and easier to trust in CI/CD.

Flaky CI pipelines are often a test data problem. A schema-first, deterministic approach turns test data into a reliable, versioned part of your codebase.

Random test data is one of the biggest causes of flaky CI pipelines. Here’s why it happens and how deterministic, schema-driven data fixes it.

From SQL schema evolution to our new deterministic generation engine – see how TestSeed is revolutionizing test data in December.