Every dev team manager and CTO should have access to objective, reliable data before adopting coding assistants across their organization. A data-driven approach means moving beyond assumptions like “Development feels faster” to concrete statements: “Our team’s cycle time dropped from 3 business days to 2”.
GitClear has conducted extensive research on this topic, analyzing five years of data -- the largest dataset to date, with 211 million lines of code changed -- to surface the trends and effects GenAI has on codebases:
The same tools used in these research studies are available to GitClear users, delivering clear, signal-rich metrics that assess the impact of GenAI on development velocity and code quality:
linkDevelopment Velocity Metrics
GitClear takes a comprehensive approach to measuring development velocity and tracking its trends over time.
There's no silver bullet metric that fully captures a team's performance. Therefore, to accurately evaluate the benefits of adopting AI tools, it is best practice to gather data from a set of measurements across the SDLC.
linkDiff Delta Velocity (aka the rate of meaningful change added to the codebase)
Diff Delta Per-Contributor Stats is the best place to start for a bird’s-eye view of your team’s delivery performance.
The Per-Contributor graphs break down development output to the average contributor in a team, giving you an apples-to-apples comparison of how AI-assisted contributors perform in contrast to other teams.
linkCycle Times
The next set of metrics one should evaluate focuses on the time-saving opportunities GenAI can provide to your teams. In this area, GitClear offers extensive tracking and visibility over the relevant cycle times across the SDLC:
linkRelease Frequency (DORA)
Release Frequency, alongside Lead Time, has the DORA seal of approval as one of the best metrics correlated with high performance.
GitClear offers out-of-the-box tracking for these metrics, which you can customize down to the individual repo level.
linkCode Quality Metrics
Data from recent research indicates a downward trend in code quality as GenAI adoption increases. Metrics such as code duplication and churn have risen significantly over the past years, while code rework is decreasing.
GitClear has integrated a suite of tools that help users monitor and prevent crippling tech debt from overtaking their codebase.
linkCode Duplication
Code duplication is widely regarded as a principal factor contributing to code unmaintainability and technical debt. To monitor the long-term health of the codebase, GitClear enables users to track the percentage of Copy/Paste work added to their repos over time, and comparing their progress against top-performing and median-level peers.
Additionally, users can track the Duplicated Blocks Line Count in their analysis to gauge how duplicated block content evolves, including blocks already present in the codebase.
linkCode Churn
Churn is defined as a code change that is revised within 2 to 4 weeks.
In our 2025 AI Code Quality research, we observed a general upward trend in short-term code churn, for both new code lines and non-test lines, which suggests that churn is a key metric to monitor when adopting AI-assisted coding.
With this in mind, GitClear has added the Churned Line Percent graph to keep you informed about potential efficiency bottlenecks within your team.
linkBug Work and Defects
Bug Work Percent measures the symptoms of poor coding practices. When bugs and defects and introduced in the codebase, developers must spend time and energy fixing them. This graph gives you an overview of how much effort your team has invested in resolving bugs and how that compares to industry benchmarks.
linkTests and Documentation
Finally, tests and documentation are essential considerations when evaluating development quality. The Quality Cornerstones graph shows the amount of work invested in maintaining robust testing and up-to-date documentation. Users can compare their results to the industry to better understand how much effort their peers dedicate to maintaining high standards for codebase health.