Editing Research:Question-38-AI-Development-Quality-Impact (section)

=== Quality Control Process Effectiveness ===

Analysis of how traditional quality control processes perform with AI-assisted development:

'''Code Review Effectiveness:'''
* Traditional review processes show 34% reduced effectiveness for AI-generated code
* Enhanced review processes specifically adapted for AI code show 18% improved effectiveness
* Reviewer training and AI code analysis skills significantly affect review quality
* Automated review tools require calibration for AI-generated code patterns

'''Testing Strategy Adaptations:'''
* Traditional test suites provide 28% less coverage for AI-generated functionality
* AI-assisted test generation improves coverage metrics but reduces test quality
* Exploratory testing becomes more critical for identifying AI-specific issues
* Performance and integration testing require enhanced focus and resource allocation

'''Quality Gate Modifications:'''
* Standard quality gates require adjustment for AI-assisted development patterns
* New metrics needed to capture AI-specific quality dimensions
* Modified thresholds required for complexity and maintainability metrics
* Enhanced monitoring needed for technical debt accumulation patterns