Write-only mode: refactoring is down 70% while code duplication is up 81%.
Four years of commit data show AI-era throughput climbing while the structural habits that keep codebases maintainable — reuse, refactoring, legacy upkeep — erode across every signal we track.
How AI is reshaping the code we ship
Code quality signal metrics, tracked weekly across the largest dataset of LLM-attributed commits in the industry. We compare every major code-generating model against the all-developer baseline.
Obfuscations per 1,000 lines changed
Defensive idioms like rescue, safe-navigation operators, null checks, and mock guards. Higher generally signals more cautious code.
Duplicate lines per 1,000 changed
Block-level duplication detected within the same commit or across the recent codebase. A leading indicator of maintenance debt.
AI-assisted commit percent
Percent of commits with evidence of AI authorship (commit author, AI API stats, etc).
Copy/paste percent
Fingerprint-detected reuse — text either repositioned without modification or duplicated from elsewhere in the repo.
Moved lines percent
Share of authored lines that were moved. A proxy for refactoring activity.
Method calls per 1,000 lines changed
Function invocations within newly authored code. Higher indicates greater connectivity between new and existing code.
Could GitClear improve your AI ROI measurement?
As AI reshapes what teams ship each quarter, measuring code quality and velocity has never mattered more — and the analytics market is crowded with capable tools. Each cell below names what a competitor does well, then how GitClear answers in the same lane. Click through for the full side-by-side.
Engineering management platform with deep Jira-centric portfolio reporting and headline DORA dashboards.
GitClear publishes the same DORA-style headline metrics — without the six-figure annual contract or the multi-week implementation.
Survey-driven developer experience platform measuring perceived productivity and DevEx friction.
GitClear pairs team surveys with per-commit, per-line AI attribution. Combining qualitative with quantitative data facilitates high-fidelity AI ROI measurement.
Workflow automation and cycle-time analytics, billed per contributor plus credit-metered automations.
GitClear delivers the same cycle-time and PR-throughput reads on a flat per-contributor price — no automation credit pool, no overage invoice. Plus broader Claude Code metrics.
The original GitPrime engineering analytics platform, with a long catalog of git-derived activity charts.
GitClear ships the same git-derived activity charts, plus AI attribution and Diff Delta — all the AI ROI tools GitPrime would have built.
Mature-code update percent
Share of changed lines that updated code older than one year. Higher signals more changes to load-bearing, long-lived code.
Churn percent
Share of authored lines that were subsequently rewritten or removed within two weeks. A proxy for defect or rework rate.
Throwaway percent
Share of authored lines that were rewritten or removed within one week
Methodology
Since 2020, GitClear has been iterating on a code change interpretation engine (Commit Cruncher) that weaves together the tapestry of changes that a line undergoes over time. Commit Cruncher keeps lines connected from initial inception, through possible file moves & find/replaces, up to its current-day content. By labeling all the human-recognized code change idioms, we can deduce the share of consequential code changes (which constitute around 3% of the "Lines change count" reported by conventional git stats) relative to the count of other emergent quantities of interest.
Each signal above is computed weekly across hundreds of millions of changed lines, segmented by the LLM fingerprint we attribute to the commit (Claude, GPT, Gemini, Copilot, Cursor, Augment, Grok, and a human-or-unattributed baseline). Rates are normalized per 1,000 changed lines or expressed as a percent of authored lines so that providers with different commit volumes remain comparable.
For the full methodology — fingerprinting heuristics, attribution confidence thresholds, normalization formulas, the dataset's repo composition, and how each signal is defined and validated — see The Maintainability Gap, our research paper detailing the four-year longitudinal study behind these charts.
GitClear's past AI research has been analyzed and written about by industry-leading journalists and analysts, including MIT Technology Review.