Node.js
c8 (coverage using V8) measures code coverage by reading the coverage data that Node.js's V8 engine generates natively. Unlike Istanbul/nyc, it requires no code transformation or instrumentation — your source runs as-is, making it compatible with ES modules and faster to execute. How c8 Differs from Istanbul/
Node.js
AVA is a Node.js test runner that runs tests concurrently by default. Each test file runs in its own process, which means no shared state between test files and faster feedback when you have many test files. Installing AVA npm install --save-dev ava Copy Configure in package.json: { "
MLOps
Weights & Biases (W&B) tracks ML experiments — but it also provides the infrastructure to assert training quality, detect regressions, and gate deployments on metric thresholds. This post covers how to use W&B as a testing tool, not just a logging tool. W&B as a
MLOps
OpenAI Evals is a framework for evaluating large language model outputs. It lets you define test cases (called "evals") that measure whether a model produces correct, consistent, or appropriately formatted responses — and run them as automated checks in your development pipeline. What Evals Test Evals measure properties of
React
React Cosmos is a sandbox for developing React components in isolation. Unlike Storybook and Ladle, Cosmos focuses on interactive development: you can manipulate component state in real time, inspect props changes, and mock context providers. It also integrates with testing tools so fixtures (Cosmos's name for stories) can
Vue
Histoire is a component story tool for Vue 3 (and experimentally Svelte). Where Storybook adapted its React-first design to Vue, Histoire was built with Vue 3 in mind from the start. It uses Vite natively, integrates with the Vue SFC format naturally, and renders stories in an iframe-less environment that
MLOps
Evidently AI is an open-source framework for evaluating and monitoring machine learning models. It generates reports and test suites that check data drift, model quality, and dataset statistics — making it a core tool for MLOps testing pipelines. What Evidently Tests Evidently organizes checks into three categories: * Data quality — missing values,
MLOps
Seldon Core and KServe deploy machine learning models as Kubernetes-native inference services. Testing these deployments means verifying the inference API, the Kubernetes resource state, canary rollouts, and model-level correctness — before and after going to production. What to Test Layer What to verify Kubernetes resources InferenceService is Ready, replicas running Inference
MLOps
BentoML packages machine learning models as production-ready API services. Testing BentoML services means verifying the model logic, the API layer, and the deployment — each at a different level. BentoML Service Structure A BentoML service wraps a model and exposes it as an HTTP API: # service.py import bentoml import numpy
JavaScript
Storybook 8 expanded from a documentation tool into a component testing platform. The key additions are play functions (interaction tests that run inside the browser), portable stories (importing stories into Vitest or Jest), and the Storybook test runner that executes all play functions in CI. Together they let you write
React
Ladle is a fast, lightweight alternative to Storybook for developing and documenting React components. It uses Vite instead of Webpack, which means near-instant startup and hot module replacement measured in milliseconds rather than seconds. Ladle is compatible with the CSF (Component Story Format) that Storybook uses, so migrating existing stories
Testing
Argos CI is a visual testing platform that compares screenshots between your main branch and pull requests. When a PR changes the visual appearance of a component or page, Argos shows the before/after diff in GitHub and can block the PR until a team member approves the change. It