First-time evaluator - what metrics should I be measuring besides precision/recall?

I'm currently conducting a formal evaluation of AI-powered code review tools for a mid-sized engineering organization (approx. 150 developers, polyglot environment leaning heavily on Go and TypeScript). While the academic literature and many vendor whitepapers focus heavily on precision and recall for defect detection—and we are certainly tracking those—I find these metrics insufficient for a holistic production deployment decision. Precision/recall gives you a snapshot of correctness, but tells you little about the tool's impact on developer workflow, long-term codebase health, or operational overhead.

Beyond the binary "was the finding correct?", I am measuring the following dimensions, and I'm keen to hear what others in the community are tracking.

**1. Signal-to-Noise Ratio & Review Friction**
This is the primary developer experience metric. A tool with 90% precision can still be unusable if it generates hundreds of trivial comments per pull request.
* **Mean Time to Triage:** The average time a developer spends assessing an AI-generated comment to determine if it's actionable, ignorable, or false. We're logging this via a custom browser extension.
* **Actionable Comment Rate:** The percentage of all tool-generated comments that result in a code change. This filters out style nits already covered by linters, overly pedantic suggestions, and correct but irrelevant observations.
* **Contextual Accuracy:** Does the tool understand the *intent* of the code block? We've seen tools correctly flag a potential nil pointer dereference in a block that is only reachable after a prior nil check—a failure of contextual analysis.

**2. Pedagogical Value & Long-Term Impact**
A good tool should not just find bugs; it should improve the developer.
* **Learning Curve Attenuation:** Are the same categories of issues being flagged for the same developers over time? We are tracking issue categories by developer cohort to see if the tool effectively reduces recurring anti-patterns.
* **Explanation Quality:** Scoring (1-5) the utility of the explanation provided with each finding. A score of 5 includes code examples, links to internal style guides, and references to specific CVEs or performance regressions. A score of 1 is a generic "This might be a bug."

**3. Integration & Operational Cost**
* **Pipeline Latency Introduced:** The delta in median CI pipeline duration with the tool enabled vs. disabled. Some tools add seconds, others add minutes.
* **Configuration Debt:** Measured in lines of YAML/JSON needed to suppress false positives, define custom rules, and align with internal standards. A tool requiring 5000 lines of ignore patterns is a non-starter.
```yaml
# Example of configuration we want to minimize
tool-xyz:
ignores:
- path: "**/*test*.go"
rule: "potential-sql-injection" # False positive in test fixtures
- pattern: "TODO.*"
rule: "comment-content" # We manage TODOs elsewhere
```
* **API Stability & Incident Correlation:** Frequency of changes to integration endpoints or output formats that break our CI, and any correlation between tool outages/delays and our own deployment blocker rates.

**4. Coverage Breadth vs. Depth**
* **Architectural Insight:** Can the tool flag issues spanning multiple files or services? For example, detecting a violation of a internal graphQL data-loader pattern, or a missing circuit breaker in a new service-to-service call.
* **Security vs. Performance vs. Correctness:** Breakdown of findings by category. A tool that only finds security issues is a niche scanner, not a general review assistant.

My hypothesis is that the "best" tool will have a strong positive regression coefficient when plotting **Actionable Comment Rate** against **Learning Curve Attenuation**, while maintaining a near-zero slope for **Pipeline Latency Introduced**. I'm preparing a detailed write-up of our methodology.

What other quantitative or qualitative metrics are you all capturing? I am particularly interested in attempts to measure the second-order effects on code review culture and PR cycle time.

-- alex