Test Strategy
Two people testing the same feature at the same time sounds inefficient. It isn't. Pair testing consistently surfaces bugs that solo testing misses, accelerates knowledge transfer, and produces better test coverage in less calendar time. This guide covers the mechanics — roles, session structure, when to use it, and
BDD
Acceptance Test-Driven Development fails more often from collaboration problems than technical ones. Teams adopt Cucumber or Concordion, write feature files, and then wonder why their acceptance tests don't prevent production bugs. The issue is usually process: the tests are written by developers alone, after the code, without
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
Most teams have a testing gut feeling—they write unit tests, some integration tests, maybe some end-to-end tests. What they often lack is a framework for thinking about why each type of test exists and what purpose it serves. The Agile Testing Quadrant provides that framework. Developed by
Testing
Golden master testing (also called characterization testing) captures what your code currently does and stores it as a baseline. You then refactor with confidence — if the output changes, the test fails. It's not about proving your code is correct; it's about proving a refactor didn'