AI-powered testing guides — how to use AI to generate, run, and maintain automated tests faster.
AI Testing
Every vendor in the testing space is marketing AI. "AI-powered," "intelligent automation," "autonomous testing" — the terminology is everywhere and most of it means very little. Meanwhile, a subset of these claims are describing real capabilities that genuinely change how testing works. The challenge
Test Automation
Quality observability means using data from production — real user behavior, errors, performance metrics — as direct input for your test suite. Instead of guessing which scenarios matter most, you test what users actually do. This guide covers how to implement quality observability using health checks, production monitoring, and feedback loops that
Comparisons
Qodo and CodeRabbit are both AI-powered PR review tools. The key difference: Qodo also generates unit tests; CodeRabbit doesn't. Qodo costs $30/user/month; CodeRabbit costs $24/user/month. If your team needs test generation alongside PR review, Qodo wins. If you only need review, CodeRabbit is
Testing Tools
CodeRabbit is an AI-powered PR review tool that posts structured analysis on every pull request: summary of changes, bugs found, security issues, logic errors, and improvement suggestions. At $24/user/month, it's one of the more affordable AI code review tools available in 2026. It integrates with
Testing Tools
Qodo (formerly CodiumAI) is an AI code quality platform combining automated test generation with AI-powered PR review. Qodo 2.0, released February 2026, replaced single-pass AI review with a multi-agent architecture that achieved a 60.1% F1 score in comparative benchmarks — the highest among eight tools tested.
AI Testing
Qodo (formerly CodiumAI) generates unit tests automatically using AI. Install Qodo Gen in VS Code or JetBrains, select a function, run /test, and get a complete test file with assertions for happy paths, edge cases, and error scenarios. This guide covers setup, the test generation workflow, and what to do
AI Testing
By 2026, more than half of code committed to GitHub is generated or substantially assisted by AI tools like Claude Code, Cursor, and GitHub Copilot. AI-generated code ships faster and introduces failure modes that traditional testing approaches miss. This guide covers how to build a testing strategy that scales
AI Testing
Agentic testing means AI agents that autonomously handle steps in the testing cycle — creating tests, running them, analyzing failures, and fixing issues — without waiting for explicit human instruction at each step. This guide covers the patterns, tools, and implementation approach for teams adopting agentic testing workflows in 2026. Key Takeaways
LLM Testing
Vertex AI provides a dedicated Evaluation Service for scoring generative model outputs — no third-party judge needed.
LLM Testing
Semantic Kernel (SK) applications combine LLM calls, plugin functions, memory retrieval, and multi-step planning into a single execution graph.
LLM Testing
Retrieval-Augmented Generation (RAG) pipelines are notoriously hard to evaluate.
LLM Testing
Most LLM security testing is manual: a human tries to jailbreak the model, documents what worked, and moves on.