GitClear in the Press 📸

A GitClear study found AI-generated code rife with duplication, indicating productivity gains could disappear amid the growing use of coding assistants.

Before we became productivity-obsessed, there were some foundational practices that the software industry found to be highly valuable. One of these is refactoring. You build more reliable systems when you continuously revisit your code to keep components loosely coupled and to make sure you define concepts just once.

Last year, I read and loved the 2024 GitClear AI report on the downward pressure that AI has put on code quality—and the corresponding increase in code complexity. I wrote on this topic last year in my newsletter.

This year, the 2025 GitClear AI Code Quality Research report analyzed 211 million lines of code across five years. This report shows that as AI usage has increased this last year, the issues with the developed code in large and complex systems have continued to increase as well.

GitClear’s second-annual AI Copilot Code Quality research analyzed 211 million changed lines of code from 2020 to 2024 across a combined dataset of anonymized private repositories and 25 of the largest open-source projects. It found multiple signatures of declining code quality – sounding the alarm around the long-term repercussions of quick wins with AI.

Is there a "deeper lexicon" that could better condense the way commits are represented? Alloy.dev finds some basic changes can radically cut the number of lines that coders need to review.

You should recognize them by their commits: GitClear analyzes data from over 850,000 developer years and evaluates the impact of AI on productivity.

According to our research at GitClear, the oldest tool most developers are still actively using—more than an hour per week—hasn't changed since before the Berlin Wall came down.

The same generative AI tools that are supercharging the work of both skilled and novice coders can also produce flawed, potentially dangerous code.

GitClear analyzes AI’s influence on code quality, examining over 153 million lines of code from 2020 to 2023. Highlighting key shifts in code churn, duplication, and age, it explores the impact of AI tools like GitHub Copilot on programming practices.

Ben and Ryan are joined by Bill Harding, CEO of GitClear, for a discussion of AI-generated code quality and its impact on productivity. GitClear’s research has highlighted the fact that while AI can suggest valid code, it can’t necessarily reuse and modify existing code—a recipe for long-term challenges in maintainability and test coverage if devs are too dependent on AI code-gen tools.

The emergence of generative AI has permanently altered how code gets written — and due to the massive productivity boost, there’s no going back.

A new study by GitClear CTO Matthew Kloster and Alloy.dev Research CEO William Harding has aimed to investigate the impact of the use of AI on code development, and found potential issues around churn and tendency not to reuse existing code.

The "Coding on Copilot" whitepaper from GitClear seeks to investigate the quality and maintainability of AI-assisted code compared to what would have been written by a human.

AI software development has grown rapidly in popularity, but how is code quality impacted? Not well, according to new research.

While AI may boost production, it could also be detrimental to overall code quality, according to a new research project from GitClear, a developer analytics tool built in Seattle.

GitClear is CliffsNotes™ for GitHub. We digest all your repository’s commits into a quantified data stream that lets managers and engineers get the gist of their code faster.