Code readability problems in AI-generated code increase by more than 3 times, with notable issues in naming and formatting inconsistencies.

January 1, 2026

Code readability problems in AI-generated code increase by more than 3 times, with notable issues in naming and formatting inconsistencies. — This cybersecurity statistic was published by CodeRabbit in January 2026. It covers topics including AI Code Generation, Software Quality. The original data appears in State of the AI vs. Human Code Generation Report . For the full methodology and detailed findings, refer to the original report.

Source

View Original Report

Published on 12/17/2025

Share or Copy this stat

Frequently Asked Questions

What does this statistic say?

Code readability problems in AI-generated code increase by more than 3 times, with notable issues in naming and formatting inconsistencies. This data was published by CodeRabbit and covers AI Code Generation, Software Quality.

Where does this data come from?

This statistic comes from State of the AI vs. Human Code Generation Report , published by CodeRabbit on January 1, 2026. You can view the original report at https://www.coderabbit.ai/whitepapers/state-of-AI-vs-human-code-generation-report.

What cybersecurity topics does this cover?

This statistic relates to AI Code Generation, Software Quality. Browse more statistics on AI Code Generation or from CodeRabbit.

CYBERSTATS

Code readability problems in AI-generated code increase by more than 3 times, with notable issues in naming and formatting inconsistencies.

Source

Share or Copy this stat

Frequently Asked Questions

Want More Statistics Like This?

Related Statistics

Performance inefficiencies, such as excessive I/O, appear nearly 8 times more often in AI-generated code compared to human-written code.

Security vulnerabilities in AI-generated code increase by 1.5 to 2 times, particularly in areas such as improper password handling and insecure object references.

AI-generated pull requests contain approximately 1.7 times more issues on average than human-written pull requests based on an analysis of 470 open-source GitHub pull requests.

Over 90% of developers report using AI coding tools to enhance productivity and manage routine tasks.

Critical and major defects are up to 1.7 times higher in AI-authored changes compared to human-authored changes.

Logic and correctness issues increase by 75% in AI-generated code, including business logic errors, misconfigurations, and unsafe control flow.

Browse by Topic

Stay Ahead of Cyber Threats