The Regression Bug Was the Most Important Result

Source: Dev.to

The Feature Wasn’t the Point

Two AI systems successfully implemented the requested UI feature. That part wasn’t interesting. What mattered was what broke outside the requested scope.

The Regression

After the change:

• Select text in the editor
• Trigger the “Replace with” contextual action
• Nothing happened. No error.

This is the most dangerous class of bug in production software.

Why This Happens with AI‑Generated Code

From an engineering perspective, this failure is predictable:

• The prompt emphasized new functionality.
• Existing behavior was implicit, not asserted.
• No test explicitly protected that interaction.
• AI systems optimize for local correctness.

Where the Implementations Differed

A closer look at the code revealed meaningful trade‑offs:

• One approach prioritized UX richness and localization.
• The other emphasized modular helpers, safer selectors, and test coverage.

A concrete example:

// Both work, but only one is designed for DOM and selector safety
encodeURIComponent(...); // general URL encoding
CSS.escape(...);         // safe for CSS selectors

These choices matter months later, not minutes after generation.

Why Unit Tests Made the Difference

Only one implementation introduced unit tests covering:

• State migration
• Regex escaping
• Replacement‑logic edge cases

Those tests didn’t just validate correctness — without them, the bug would likely ship.

Takeaway

If you evaluate AI coding tools in real projects, ask:

• What existing behavior is protected?
• What assumptions remain implicit?
• What breaks quietly?

Demos won’t answer those questions.

Final Thought

The most valuable result wasn’t the feature. It was identifying where AI coding systems still fail like junior engineers — that distinction is what matters in real‑world software.