AI Testing
AI Testing Capabilities Mapped to AI Code Risks
AI code risk
AI testing capability
AI code risk
Coverage asymmetry
Your test suite covers paths you anticipated. AI-generated code introduces new branches your existing tests never reach.
AI testing capability
Intelligent coverage analysis
Scans newly generated functions, identifies untested code paths, and surfaces gaps before code reaches CI.
AI code risk
Confidence miscalibration
LLM output looks authoritative. Developers review it less rigorously - suppressing the instinct to test thoroughly.
AI testing capability
AI-assisted test case generation
Analyzes generated code, infers intended behavior, and suggests test cases covering the most likely failure points - independent of developer review.
AI code risk
Brittleness at integration
AI-generated code works in isolation but breaks at integration points. Unit tests do not catch this - E2E coverage is where the gap surfaces.
AI testing capability
Behavioral validation
Validates whether code behaves correctly under real conditions - not just whether it compiles. Operates at the logic layer, not just the syntax layer.
AI code risk
Hallucinated logic
LLMs generate plausible but incorrect logic for business-specific rules with no training data equivalent. Passes review - only a targeted test finds it.
AI testing capability
Self-healing test maintenance
Adapts tests automatically as AI-generated code is iterated on rapidly - keeping coverage viable at development pace without manual upkeep.
Key principle: Each AI code risk maps to a distinct testing capability. The goal is not to slow down development - it is to match the intelligence of testing to the intelligence of code generation, so speed and quality move together.