Testing
Most software bugs originate at the design level, not the implementation level. You misunderstood how two subsystems interact, or you forgot that a particular state combination was possible. By the time you discover this in a test, you've built the wrong thing for days or weeks. Alloy is
Testing
All-pairs testing is often presented as the answer to combinatorial testing problems. Cover every pair, ship faster, done. But all-pairs is a point on a spectrum — and teams that stop there without understanding the full picture end up with blind spots they don't know about. This
Testing
Akka Streams introduces a different testing challenge from regular actors. Streams are lazy, composable, and backpressure-aware — properties that make them powerful but also make naive testing approaches fail. The akka-stream-testkit module provides the right primitives for verifying stream behavior without races or flakiness. Setup Add the testkit
Testing
The TaskFlow API (introduced in Airflow 2.0) transforms DAG authoring: instead of operator-heavy boilerplate, you write plain Python functions decorated with @task. This makes Airflow code significantly more testable — task logic is just a function, and functions can be unit tested. This post covers testing patterns specific to
Testing
Red-team exercises produce findings. Findings produce mitigations. Mitigations are shipped in code changes. Code changes can revert, regress, or silently undo previous safety work. Without a continuous testing pipeline, the safety work you did in Q1 may be undone by a routine model update in Q3, and you will
QA
Two years ago, the question was "should we outsource QA or hire in-house?" Today, there's a third option that's changed the economics significantly: AI-powered QA services that automate test creation, execution, and monitoring without a team of human testers. This isn'
Test Automation
Pixel-by-pixel comparison is the foundation of visual regression testing, but it has a fundamental problem: it's too precise. Two screenshots that look identical to a human can differ by thousands of pixels due to font rendering, antialiasing, subpixel differences, or minor layout shifts from non-deterministic
AI Testing
Model regression testing is the practice of verifying that an LLM upgrade, prompt change, or fine-tune didn't make things worse. It sounds obvious. Most teams don't do it. The typical pattern: team upgrades from GPT-4 to GPT-4o to save costs. Everything seems fine
AI Testing
Hallucination is when an LLM presents false information as true. The model doesn't know it's lying — it's pattern-matching to produce plausible-sounding text, and sometimes plausible diverges from factual. For most applications, hallucinations aren't a curiosity — they're a production
AI Testing
Every AI coding tool — Cursor, Copilot, Amazon Q, Claude, Gemini Code Assist — generates code that looks correct. The syntax is clean, the names make sense, and the logic passes a casual read. What none of them can guarantee is that the code is correct. This isn't a criticism.
Testing
AI coding assistants have fundamentally changed how developers write software. They've also created a new question nobody had to answer before: when your AI writes the code, who makes sure the code actually works? This comparison covers the four most popular AI coding tools — Cursor, GitHub Copilot, Claude
Testing
Most test strategies are built around tooling: "we use Jest for unit tests, Cypress for E2E, and k6 for load testing." The problem with tool-centric strategies is that tools tell you what you're running, not what you're covering. You can have a fully