AI-powered testing guides — how to use AI to generate, run, and maintain automated tests faster.
Testing
In 2014, Goodfellow, Shlens, and Szegedy published a result that the ML community found deeply unsettling: by adding a carefully crafted, human-imperceptible perturbation to an image of a panda, they could cause a well-trained image classifier to label it "gibbon" with 99.3% confidence. The perturbation
Test Automation
AccelQ's API testing module lets you create, execute, and maintain REST and SOAP tests without scripting. It sits in the same platform as your UI tests, which matters for end-to-end scenarios that mix API calls with browser interactions. This post covers what the module actually does,
Compliance Testing
EU AI Act compliance for high-risk AI systems is not a one-time checklist — it requires ongoing automated testing. This guide shows how to integrate bias, accuracy, robustness, audit trail, and logging tests into GitHub Actions using DeepEval and Giskard, with full YAML examples and a production monitoring setup
AI Testing
LLMs encode the biases present in their training data — and those biases surface in completions in ways that are measurable and testable. This guide covers what LLM bias looks like in practice, how to measure it with established benchmarks and DeepEval, how to build a bias test suite, and how
AI Testing
AI systems fail in ways traditional software doesn't — they drift, discriminate, and hallucinate, often silently. Risk-based testing for AI means probing bias across demographic groups, benchmarking accuracy under distribution shift, testing adversarial robustness, and measuring hallucination rates. This guide shows how to do all of it with
Compliance Testing
The EU AI Act's full obligations for high-risk AI systems take effect in August 2026. If you're building AI-powered products in hiring, credit, healthcare, or similar domains, you need a concrete testing strategy right now — not a legal strategy, an engineering one. This guide
Testing
AI chatbots fail in ways that traditional software doesn't. They misunderstand intent, lose context across turns, make up information, break character, and occasionally say things that would horrify your legal team. Testing them requires a different mental model from testing deterministic systems — you're testing behaviors under
Testing
LLMs hallucinate. They generate plausible-sounding text that is factually wrong, cite papers that don't exist, quote statistics that were never measured, and confidently describe events that never happened. If your product uses an LLM to generate content that users trust — customer support answers, medical information, legal summaries,
Testing
ML models degrade in ways that traditional software doesn't. Code doesn't spontaneously get worse — but a model trained on March data can silently degrade by June as the distribution of real-world inputs shifts. A retrained model can score higher on aggregate metrics while regressing on
Testing
A/B testing AI features is harder than testing traditional software. Changing a button color produces a measurable, binary outcome — users click or they don't. Changing an LLM prompt produces different text outputs, and measuring which output is "better" requires defining what better even means for
AI Testing
AI has changed what's possible in test automation. The traditional approach — writing code to drive browsers, maintaining brittle selectors, spending hours fixing tests when UI changes — is no longer the only option. AI-powered test automation lets you describe tests in plain language, generates test steps from user
AI Testing
Adding AI features to your SaaS is easy. Shipping them with the same quality bar as the rest of your product is hard. A traditional feature has deterministic behavior — a button either submits a form or it doesn't. An AI feature has probabilistic behavior — the same input produces