Testing
Desktop application testing is harder than web testing. Applications have richer state, native controls that don't map cleanly to HTML, OS-level interactions, and file system dependencies. Without deliberate architectural decisions, desktop test suites become fragile and slow. These patterns address the most common failure modes. The Three-Layer Testing
Testing
Phoenix LiveView enables rich, real-time web interfaces without writing JavaScript. Testing LiveView requires a different approach than traditional controller tests because LiveView components are stateful, event-driven, and update asynchronously. The Phoenix.LiveViewTest module provides the tools to test these interactions deterministically. How LiveView Testing Works Phoenix.LiveViewTest mounts a LiveView
Testing
Unit tests check specific examples: "given input X, the output is Y." Property-based testing asks a different question: "for ALL inputs with this shape, does this property always hold?" StreamData brings this paradigm to Elixir, integrating with ExUnit to automatically generate hundreds of test cases from
Testing
Elixir ships with a built-in testing framework called ExUnit. Unlike many languages where testing frameworks are third-party add-ons, ExUnit is part of the Elixir standard library and is the default choice for testing Elixir applications. This guide covers everything you need to write effective tests from day one. Setting Up
Testing
Testing code that depends on external services, databases, or complex collaborators requires isolation — you want to test one unit at a time. Mockito-Scala brings Mockito's mocking capabilities to Scala with an idiomatic API that handles Scala-specific features like default arguments, final classes, and type inference. Why Mockito-Scala Over
Testing
Testing code that calls external services — payment processors, email providers, third-party APIs — presents a challenge. You don't want real network calls in your test suite, but you also don't want to lose confidence in how your code handles those interactions. Mox, Elixir's standard mocking
Testing
Flaky tests are rarely random. In most codebases, the cause is isolation failure: test A leaves state that breaks test B. When test A runs last in one execution order and first in another, B succeeds sometimes and fails other times. Teams call these tests "flaky" and treat
Testing
WebSocket testing is one of those areas where developers often wing it — manually opening a browser console, firing a few messages, and calling it done. That works until it doesn't: a race condition in reconnection logic, a dropped message during high load, or a broken event sequence that
Testing
Integration tests live and die by their data. A test that creates an order needs a product to exist. That product needs a category. That category has constraints. Building this state in every test is tedious and produces tests that are tightly coupled to unrelated domain objects. Getting it wrong
Testing
Integration tests that run against a shared staging environment are a liability. Two PRs land simultaneously, one migrates the database schema, and suddenly the other PR's test suite is failing against an incompatible schema it didn't create. Teams work around this with test locks, serialized merge
Testing
Infrastructure code breaks. An S3 bucket gets configured with public ACLs. A security group rule opens port 22 to the world. A load balancer misconfiguration sends traffic to the wrong target group. These aren't hypothetical — they are the kinds of bugs that make it to production because the
Testing
Every engineering team has experienced the same painful scenario: a test suite passes flawlessly on one developer's laptop, fails on another's, and produces yet different results in CI. The root cause is almost always environment drift — different Node versions, mismatched library versions, missing system dependencies, or