AI-Generated Code Ships Without Developer Understanding
Developers accept AI-generated code without verifying it against acceptance criteria, and functional bugs and security vulnerabilities reach production unchallenged.
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These symptoms indicate problems with your testing strategy. Unreliable or slow tests erode confidence and slow delivery. Each page describes what you are seeing and links to the anti-patterns most likely causing it.
Start with the symptom that matches what your team experiences. Each symptom page explains what you are seeing, identifies the most likely root causes (anti-patterns), and provides diagnostic questions to narrow down which cause applies to your situation. Follow the anti-pattern link to find concrete fix steps.
Related anti-pattern categories: Testing Anti-Patterns, Pipeline Anti-Patterns
Related guide: Testing Fundamentals
Developers accept AI-generated code without verifying it against acceptance criteria, and functional bugs and security vulnerabilities reach production unchallenged.
Tests pass locally but fail in CI, or pass in CI but fail in staging. Environment differences cause unpredictable failures.
Test coverage numbers look healthy but defects still reach production.
Zero test coverage in a production system being actively modified. Nobody is confident enough to change the code safely.
Internal code changes that do not alter behavior cause widespread test failures.
The team cannot run the full regression suite on every change because resetting the test environment and database takes too long.
The test suite takes 30 minutes or more. Developers stop running it locally and push without verifying.
Tests share mutable state in a common database. Results vary by run order, making failures unreliable signals of real bugs.
The pipeline fails, the developer reruns it without changing anything, and it passes.