Canonical List of Data-Backed AI Developer Productivity and Code Quality Research

GitClear's first-party research has been cited by numerous reputable sources, including MIT Technology Review, TechCrunch and The New Stack. But we don't have the monopoly on great, data-backed research. This page collects widely-cited third-party research on AI developer productivity and code quality between 2024 and 2026. If you have a link to AI research that has changed your thinking, let us know?

	Research Title & Source	Publication Date	Key Takeaways	Link
	Coding Tools Attract Top Performers – But Do They Create Them? GitClear Research	January 2026	Heavy AI users are generating 4-10x more durable code than non-AI users Those same users also generated 9x more code churn than non-AI users The 4x jump in productivity reflects truths about who is using AI most, as of early 2026	Read AI Coding Tools Attract Top Performers – But Do They Create Them?
	AI Copilot Code Quality: Evaluating 2025's Increased Defect Rate with Data GitClear Research	2025	Analyzed 211 million lines of code from 2021 to 2025 Finds that percent of moved/refactored code plummeted from 25% in 2021 to less than 10% in 2025 Over the same period, copy/pasted (duplicated) code rose from 8% of changes to 18%	AI Copilot Code Quality: Evaluating Increased Defect Rate with Data
	Google DORA AI Capabilities Model Report Google Research	2025	Most “lead time” is still waiting, not building: ~21% flow efficiency. In the value-stream example: ~24.5 hours active work vs ~92.5 hours waiting (total ~117 hours lead time) → ~21% flow efficiency. Estimated impact of AI on "Software Delivery Instability" is 0.1x, second only to the increase in "Individual Effectiveness" (at 0.17x) Using AI to resolve a microservice can take less than 5 minutes of process time, but still incur a 5 day cycle time due to review waits	Read Stack Overflow 2025 Developer Survey on AI
	Stack Overflow 2025 Developer Survey on AI Stack Overflow	2025	Daily AI use is common: 47.1% use AI tools daily (plus 17.7% weekly and 13.7% monthly/infrequently) Professional developers are even more “daily”: 51% of professional developers use AI tools daily. Trust is the constraint: only 3.1% “highly trust” AI accuracy; overall 33% trust vs 46% distrust AI outputs.	Read Stack Overflow 2025 Developer Survey on AI
	Data on AI Impact: Changes to Developer Productivity from 2022 to 2025 GitClear Research	2025	Analyzes 70,000 developer-years of data to quantify the impact of AI tools on developer productivity Median developer productivity has increased 9% from 2022 to 2025 Among developers averaging 500+ annual commits, the median productivity increase vs 2022 is 14.1%	Read AI Tool Impact on Developer Productive Output from 2022 to 2025