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For measuring the effectiveness of an AI-powered code review system, we’re monitoring three-tier metrics that provide both leading indicators for system health and lagging indicators for business value.
Tier 1: Core Operational Metrics (Leading Indicators)
These real-time metrics provide immediate insights into system performance and help identify issues quickly:
- Issue Detection Rate: Percentage of analyzed PRs where potential risks are identified based on historical incidents. Too high suggests false positives and can make developers have bot fatigue, they can ignore real alert. Too low then maybe training data is not enough or false negative.
- Distinct Incident Coverage: Number of unique historical incidents referenced in analysis. This can tell if the analysis can easily be skewed to an incident, this tells data training quality.
- Repository Coverage
- Knowledge Base Growth Rate: Tracks the continuous expansion of our incident database, ensuring the system’s learning capacity improves over time.
Tier 2: Developer Feedback (Direct Indicators)
Based on GitHub emoji reactions, developers can react to the analysis to give feedback. A daily automated workflow collects reactions from the past 7 days, storing detailed metrics in our analytics database for trend analysis.
Tier 3: Business Impact (Lagging Indicators)
Long-term metrics that demonstrate ROI and organizational value:
- Incident Rate Reduction: Tracking whether the incident rate trend decreases over time across repositories using the bot.
