Sanity Testing
Sanity testing is the most misunderstood testing type in the QA vocabulary. Teams confuse it with smoke testing, use the terms interchangeably, or skip it entirely in favor of running the full regression suite. All of these are mistakes. This guide explains what sanity testing actually is, when it'
Testing
A digital twin is a real-time virtual model of a physical system that receives the same sensor data as the physical asset and can predict behavior, simulate failures, and serve as a test environment for control logic changes. For testing purposes, digital twins remove the most dangerous constraint in
Testing
Sanity Studio has three testable layers: schema definitions (TypeScript/JavaScript), GROQ queries (data fetching logic), and content pipelines (transformations). Testing all three gives you confidence that schema changes don't break queries, queries return the expected shape, and your transformation code handles all content states correctly. Key Takeaways Test
Test Automation
When you rewrite a component, migrate to a new library, or refactor a complex algorithm, the hardest question is: "Did I break anything?" Your test suite only catches what it explicitly tests. Differential testing asks a different question: on the same inputs, does the new code produce the
Test Automation
Diffblue Cover can generate hundreds of unit tests in minutes. Manual tests take hours or days. That speed advantage seems decisive — until you look at what you actually get. This post breaks down the real tradeoffs so you can decide where AI generation adds value and where you still need
Backend Testing
Saleor is an open-source headless commerce platform built on Django and GraphQL. It powers storefronts for mid-size to enterprise retailers who need full control over their commerce stack. Because Saleor exposes everything through a GraphQL API, testing requires understanding both Django's test tooling and GraphQL-specific
Test Automation
The Saga pattern is how you implement long-running business transactions across multiple microservices without distributed transactions. Instead of locking resources across services (which doesn't scale and creates tight coupling), a saga breaks the transaction into a sequence of local transactions — each with a compensating transaction to undo
Test Automation
Spring Boot applications have consistent, well-understood patterns: @Service classes orchestrate business logic, @Repository interfaces handle persistence, and @RestController classes expose HTTP endpoints. Diffblue Cover recognizes these patterns and applies appropriate mocking and assertion strategies for each. Here's what to expect from each layer and how to get
Test Automation
Legacy Java codebases are the most compelling use case for Diffblue Cover. These are systems that have been in production for years, with no unit tests, where every change carries risk because there's no safety net. Adding tests manually is slow and risky — touching legacy code to make
Microservices
Distributed transactions are one of the hardest problems in microservices. Two-phase commit works in theory but fails in practice — it's slow, fragile, and locks resources across service boundaries. The saga pattern solves this with a sequence of local transactions with compensating actions. But testing sagas is just
Accessibility Testing
A SaaS application has specific accessibility challenges that generic checklists don't address: authenticated dashboards, complex data tables, modals triggered by user actions, real-time updates, and multi-step workflows. This checklist is organized by what to check at different stages of development and before release. Why SaaS Products
Test Automation
Writing unit tests is one of the most time-consuming parts of Java development — and one of the most frequently skipped. Diffblue Cover addresses this by generating JUnit tests automatically using AI. This guide walks through installation, first run, and what to do with the tests it produces. What Diffblue