Output Metrics — How to Accurately Track Code Throughput & Quality when Using AI

AI tools can help developers write code faster, suggest tests, and even automate parts of code review. But speed alone isn’t enough. Without measuring output quality, faster code could just mean more bugs, rework, or hidden technical debt. In this article, we explore practical techniques to track engineering output metrics, ensuring that AI actually improves productivity without compromising maintainability.

PR cycle time is the duration from PR creation to merge. AI can reduce manual coding effort, but faster PRs are only beneficial if quality remains high.

How to track it effectively:

• Measure both median and 95th percentile cycle times to capture typical and edge-case delays.

• Compare AI-assisted PRs against a control group of non-AI PRs to see true impact.

• Break down cycle time into code writing, review, and merge stages to understand where AI adds the most value.

Throughput measures the number of PRs merged per developer or team per time unit. High throughput may indicate efficiency gains, but only when normalised for complexity.

Normalisation techniques:

• Use PR complexity scores (lines of code changed, number of files, cyclomatic complexity) to avoid counting trivial PRs as “wins.”

• Consider story points for feature work to account for business impact.

• Track team-level throughput trends over time rather than focusing solely on individual performance.

Even if AI accelerates coding, it may introduce bugs or regressions. Tracking how often PRs are reverted or require urgent fixes is crucial.

Key tips:

• Calculate the revert rate per 100 PRs to normalise for team size.

• Monitor root causes — e.g., AI-generated code, misunderstood requirements, or lack of test coverage.

• Combine with review feedback to identify patterns of AI-related errors.

Measure how many bugs are reported relative to new features delivered. A rising bug-to-feature ratio signals declining output quality, even if throughput is increasing.

Tracking strategy:

• Integrate issue tracking tools (Jira, GitHub Issues) with PR metadata.

• Categorise bugs by origin: AI-generated vs. manual changes.

• Use trend analysis to detect early warning signs of declining quality.

AI tools often help generate tests, but the quality of tests and maintainability of code matter most.

Metrics to track:

• Code coverage percentage (unit, integration, E2E) over time.

• Complexity metrics: cyclomatic complexity, cognitive complexity, and function/class size.

• Documentation coverage: comments, README updates, and inline docs.

• Technical debt indicators: static analysis warnings, linter violations.

AI’s impact should show higher or stable maintainability with faster throughput, not just more lines of code.

No metric should be interpreted in isolation:

• Combine throughput, cycle time, revert rates, and coverage to see true productivity gains.

• Compare AI-assisted metrics with a baseline period before AI adoption.

• Track metrics over multiple release cycles to capture long-term effects.

Remember: Speed without quality is false productivity. Output metrics help ensure AI improves delivery safely and sustainably.

Once you’ve measured AI adoption and output metrics, the next step is to track developer experience and long-term team health — our Layer 3. This ensures AI not only helps code ship faster but also supports sustainable developer growth and satisfaction.