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An interesting article was shared in our chat yesterday.

This is a summary of the analysis of AI's influence on code quality. Some excerpts:

> The data strongly correlates “using Copilot” with “mistake code” being pushed to the repository more frequently.

> The 17% decrease in “move” operations when compared to 2021 hints at
the built-in trait of AI assistants to discourage code reuse. Instead of

refactoring and working to DRY (“Don’t Repeat Yourself”) code, they offer a one-keystroke templation to repeat existing code.

> Especially next to the decrease in “moved code,” the 11% increase in the proportion of duplicated code confirms the drop in overall code quality in 2023 when compared to 2021.

And my favorite one:

> In the absence of a CTO or VP of Engineering who actively schedules time to reduce “tech debt,” “copy/pasted code” often never gets consolidated into the appropriate component libraries.

Although, I saw this even before AI.

#ai #programming
Arc Talent Career Blog Humans do it better: GitClear analyzes 153M lines of code, finds risks of AI - Arc Talent Career Blog GitClear's study on AI code impact: code churn, duplication, and quality trends in programming.
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