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Artie Shevchenko Discusses AI's Role in Code Reviews and Maintaining Intellectual Control

🔄 Updated 1h ago
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Key points

  • AI checks can handle low-risk code reviews.
  • Manual spikes are needed for complex code changes.
  • AI amplifies cognitive debt if not managed.
  • Human intellectual control over code is essential.

AI in Code Review Workflows

Artie Shevchenko, speaking at the Craft conference, proposed a system for code reviews that combines AI checks with targeted manual reviews. This approach involves using AI for mandatory checks on code changes and implementing 'manual spikes' for more complex alterations. The goal is to maintain developer proficiency while potentially increasing review velocity.

Balancing Velocity and Understanding

Shevchenko suggested that teams could accelerate development by allowing AI to approve low-risk pull requests, particularly when most developers are code owners and teams are small. However, he emphasized that AI's token generation creates an 'illusion of understanding,' making human intervention necessary to prevent a 'broken knowledge feedback loop'.

Addressing Cognitive Debt

Cognitive debt, defined as the erosion of shared understanding within a team about their system, is a concern amplified by AI, according to Shevchenko. He stated that AI can increase review volume, exacerbating this problem. To counter the code review bottleneck, he advocated for strategies beyond just developer experience improvements.

Maintaining Intellectual Control

Shevchenko stressed the importance of maintaining intellectual control over codebases, especially with the increased complexity introduced by AI. He proposed an approach where developers first code a change, then use AI to suggest improvements and fixes. This method allows developers to review individual AI-proposed changes from a position of deep understanding of the initial solution.

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Reporting from

Artie Shevchenko presented at Craft conference on integrating AI into code reviews, suggesting mandatory AI checks with manual spikes for complex changes. He argued that while AI can increase velocity by handling low-risk reviews, human oversight is crucial to prevent cognitive debt and maintain intellectual control over codebases.