AI-Generated Code Drives a Wedge Between Maintainability and Change Confidence

According to a 2026 Q2 State of AI Impact report covering over 500 companies, code maintainability improved by 3.8% from the first to the second quarter. [1] During this same timeframe, developers' confidence that their changes will not break things dropped by 6.1%. [1] Historically, code maintainability and change confidence have moved together. [1]

While documentation quality and production debugging showed improvements, incremental delivery fell sharply and pull requests continued to increase in size. [1] To contextualize these conflicting metrics, researchers referenced a five-part definition of quality called TRUCE, which was developed in 2020 through a survey of 131 developers and 34 engineering managers at Microsoft. [1]

The divergence of code maintainability and change confidence suggests that AI is generating code that looks clean but behaves unpredictably. While developers might find it easier to read AI-assisted documentation or debug production issues, the growing size of pull requests and slower review turnaround indicate that evaluating machine-generated logic remains a significant bottleneck. The reliance on the 2020 TRUCE framework underscores that modern engineering teams may need to decouple their traditional quality metrics, as AI tools artificially inflate structural readability without guaranteeing functional reliability. What the sources don't address: whether specific types of AI coding assistants or programming languages are disproportionately responsible for this drop in change confidence.