AI Code Quality Metrics: What 2,561 Commits Reveal About AI-Generated Code

The Setup

The most common objection to AI-assisted software development is quality. The argument is simple: if you ship faster, you ship worse. Engineering leaders have internalized this tradeoff for decades — speed and quality sit on opposite ends of a seesaw, and pushing one down raises the other. The industry data supports the assumption. Rollbar's developer survey shows that 26% of developers spend more than 50% of their time fixing bugs, and another 38% spend at least 25% of their time on bug fixes. Stripe's Developer Coefficient study found that developers spend an average of 17.3 hours per week on maintenance and technical debt. Coralogix reports that in the worst cases, 75% of developer time goes to debugging.

GitClear's analysis of code quality trends found that AI-assisted code contributions show elevated churn rates — code that is written and then rewritten shortly after — raising concerns that AI tools generate more throwaway code than human developers. The DORA State of DevOps metrics framework measures deployment frequency, lead time for changes, mean time to recovery, and change failure rate as the four key indicators of software delivery performance, and conventional wisdom holds that teams pushing for higher deployment frequency tend to see change failure rates climb. Capers Jones' defect benchmarks by methodology show that the average developer creates 70 bugs per 1,000 lines of code, with 15 bugs per 1,000 lines reaching customers.

The question is whether AI-assisted development at high output rates produces code that meets production quality standards — or whether it generates volume at the expense of reliability. The only way to answer that question is with data from a real production portfolio, not from synthetic benchmarks or isolated experiments.

What the Data Shows

The industry baseline for acceptable defect rates is the "80/20 rule" — 80% of developer time on new features, 20% on bug fixes. In practice, most teams fall short. Rollbar and Coralogix data consistently show that 20-50% of developer time goes to fixing defects, with many teams spending far more. McConnell's Code Complete benchmarks place industry defects at 15-50 per 1,000 lines of code.

Against that baseline, one production portfolio — ten systems, 2,561 commits across 596,903 lines of code, built between October 2025 and February 2026 — recorded a 12.1% product defect rate. That is half to one-fifth of the industry norm. And it was achieved at 4.6x the industry-standard output rate (CS14, Feb 2026).

The breakdown across all 2,561 tracked work units:

• New features and core development: 76.3%
• Product bugs (actual defects): 12.1%
• Design iteration (cosmetic, refinement): 6.9%
• Learning overhead (deployment, infrastructure): 3.4%
• Integration friction (API wiring): 1.1%
• Reverts: 0.2%

The 76.3% net-new development ratio sits just below the industry's 80% target — achieved by an operator with no prior software engineering experience building ten production systems simultaneously. The 11.6% of rework that was not bugs — design iteration, learning, integration — represents normal execution overhead: the equivalent of adjusting layout formatting or learning a new tool's workflow. It is not defect-related. It is the cost of building (CS14, CEM_Timeline).

The quality data breaks down further by project, and the variation reveals a pattern:

• PRJ-10: 3.7% defect rate
• PRJ-08: 3.8% defect rate
• PRJ-09: 3.9% defect rate
• PRJ-11: 11.3% defect rate
• PRJ-04: 16.1% defect rate
• PRJ-06: 16.8% defect rate (7 integrations, 2 breakages)
• PRJ-07: 26.4% defect rate
• PRJ-05: 26.8% defect rate
• PRJ-01: 31.3% defect rate (most complex system — 135 database tables, 104 controllers, 20 integrations)
• PRJ-03: 43.2% defect rate (fastest learning curve)

The pattern is clear. Products built on shared, proven foundations — the PRJ-08/PRJ-09/PRJ-10/PRJ-11 cluster — achieved 3.7-3.9% defect rates, an order of magnitude better than the industry average. Complex, integration-heavy products (PRJ-01, PRJ-06) had higher rates but still fell within or below industry norms. The quality floor remained high even in the worst cases (CS14).

DORA's State of DevOps research has consistently shown that elite-performing teams achieve both high deployment frequency and low change failure rates — suggesting the speed/quality tradeoff is not a law of nature but an artifact of process. This portfolio's data corroborates that finding at the individual-operator scale.

How It Works

Three operational mechanisms explain how quality survived — and improved — at high output rates.

First, continuous awareness during execution. Rather than relying on quality checks at the end of a build cycle, the operator maintains a running sense of whether current output matches intended direction. When output drifts from the target, the drift is caught in minutes — not discovered in a testing phase weeks later. This is the difference between catching a typo while writing a sentence and discovering it during a final proofread of a 200-page document. Early detection means smaller fixes.

Second, proven foundations propagate quality. When 95%+ of a new product's infrastructure comes from components that have already been built, tested, and debugged in previous projects, the quality of those components carries forward automatically. The PRJ-08/PRJ-09/PRJ-10/PRJ-11 cluster's 3.7-3.9% defect rates are not the result of extraordinary care on those particular projects. They are the result of inheriting a clean scaffold. Authentication, database patterns, UI templates — all inherited from previous builds where the bugs had already been found and fixed. Quality effort shifts from "fix everything" to "fix only what is new."

Third, speed and quality improve together over time. The portfolio data shows quality and speed moving in the same direction, not against each other:

• October (foundation phase): Low output, higher defects — building new patterns
• November (iterative phase): Medium output, stabilizing defects
• December (acceleration): High output, declining defects
• January (peak): Highest output, lowest rework phases

By January, the operator was assembling proven components at the highest rate in the portfolio — and producing the cleanest output. The faster the operator shipped, the cleaner the results became, because speed came from assembling vetted infrastructure, not from writing untested code under time pressure (CS14, CEM_Timeline).

What This Means for Engineering Leaders Evaluating AI-Assisted Development

The data challenges the default assumption that AI-assisted code is lower quality than human-written code. Across 2,561 commits and 596,903 lines of production code, the defect rate was 12.1% — materially better than the 20-50% industry norm documented by Rollbar, Stripe, and McConnell's benchmarks. The portfolio's best projects hit 3.7% defects. Even the worst cases (complex, integration-heavy systems built during the operator's steepest learning curve) stayed within the range that conventional teams regularly produce.

For organizations measuring code quality, the implication is that AI-assisted development is not inherently a quality risk. It is a quality risk when applied without accumulated infrastructure and without real-time awareness of output health. When those two conditions are met — proven foundations and continuous drift detection — the data shows that AI-assisted development can deliver production-grade quality at multiples of conventional output rates. The speed/quality tradeoff is an artifact of how software has been built, not a law of nature. When projects build on proven foundations, pushing faster means assembling more proven components per unit of time — and quality improves as a consequence.

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Related: Spoke #7 (Cost to Build Software with AI) | Spoke #10 (Agile/Scrum and AI Development) | Spoke #11 (Measuring AI Development Productivity)

References

1. GitClear (2024). "Code Quality Analysis." Code churn, move, and copy rate trends in AI-assisted codebases.
2. Google (2024). "DORA State of DevOps Metrics." Deployment frequency, lead time, MTTR, and change failure rate benchmarks.
3. Capers Jones. Software defect benchmarks by methodology (70 bugs per 1,000 LOC created, 15 reaching customers).
4. McConnell, S. (2004). Code Complete, 2nd ed. Microsoft Press. Industry defect density benchmarks (15-50 defects per KLOC).
5. Rollbar (2024). "Developer Survey." Bug-fixing time allocation (26% of developers spend 50%+ of time on bugs).
6. Stripe (2024). "Developer Coefficient Study." Developer time spent on maintenance and technical debt (17.3 hours/week average).
7. Coralogix (2024). "Developer Time Analysis." Debugging time allocation showing up to 75% in worst cases.
8. Keating, M.G. (2026). "Case Study: Quality at Speed." Stealth Labz. Read case study