Chaos Engineering
Kubernetes introduces a new class of failure modes that traditional testing misses: pods evicted under memory pressure, nodes drained during rolling updates, network policies blocking inter-service traffic, readiness probes misconfigured. Chaos engineering in Kubernetes targets these specifically — testing that your workloads survive the infrastructure events that Kubernetes itself causes.
Testing
Most teams run chaos experiments the wrong way. They pick a failure mode — kill a pod, inject network latency — watch things break, declare the experiment "done," and learn nothing they couldn't have predicted. The output is a Slack message saying "yep, that broke things,"
Testing
You can't ship LLM-based features without testing them. Unlike traditional software where the same input always produces the same output, LLMs are probabilistic — the same prompt can return different results across runs, models, and versions. Without structured prompt testing, you're deploying blind. This guide covers
Testing
Prompt testing is the practice of verifying that your AI product's prompts produce the intended behavior. It's harder than it sounds. Prompts are non-deterministic inputs to non-deterministic models. Testing them requires a different mindset than testing code. This guide covers practical prompt testing strategies
Chaos Engineering
Chaos engineering is the practice of deliberately introducing failures into a system to find weaknesses before they cause real outages. Netflix invented the term and the tooling (Chaos Monkey). The core idea is simple: if your system will eventually fail, find out now under controlled conditions rather than during a
Testing
Prompt injection is to LLM applications what SQL injection was to web applications in 2003. The pattern is structurally identical: untrusted user input is concatenated into a command string and executed by an interpreter that cannot distinguish between the developer's intended instructions and the attacker's injected
API Testing
Frontend team waiting on backend APIs to be built. Backend team waiting on the frontend to tell them if the API shape is right. Everyone blocked on everyone else. Prism breaks this cycle. Give it an OpenAPI spec and it spins up a fully functional mock server — returns realistic example
Testing
CDK for Terraform (CDKTF) lets you define infrastructure in TypeScript, Python, Go, or Java and synthesize it to Terraform JSON. This brings software testing practices to infrastructure: unit test your constructs, snapshot test synthesized output, and run integration tests against real cloud resources. This guide covers CDKTF testing from construct-
Testing
PractiTest and TestRail are both mature test management platforms used by enterprise QA teams. They cover the same core workflow -- test case library, test run execution, defect tracking integration, reporting -- but they make different tradeoffs in flexibility, pricing, and integration depth. Here is a direct comparison across the dimensions that
Testing
PractiTest's reporting system separates into two layers: built-in reports with fixed structures (traceability matrix, test cycle summary), and a dashboard builder where you compose custom views from widgets. Both layers pull from live execution data, so reports reflect the current state without any manual refresh. Built-In
Testing
AWS CDK has two testing approaches that serve different purposes and are commonly confused: 1. Fine-grained assertions (hasResourceProperties, hasResource, resourceCountIs) — assert specific properties of specific resources 2. Snapshot tests (toMatchSnapshot) — assert the entire synthesized CloudFormation template hasn't changed Most CDK projects use one or the other exclusively,
Testing
PractiTest is a cloud-based test management platform built around a hierarchical model: Requirements feed into Test Cases, which are grouped into Test Sets, which are executed as Test Runs. Understanding that hierarchy upfront saves a lot of confusion when you start configuring your first project. Creating a Project After