#Balanced Coupling

“Words should be weighed, not counted.” — A Yiddish Saying

Static code analysis tools count dependencies, calculate ratios, and assign neat scores. Yet in my experience, chasing those metrics never really made the design more modular. It’s so easy to game the scores without any improvement to the design itself. Let’s see why, and what you can do instead.

The Counting Approach

Most static analysis tools evaluate coupling by counting dependencies. Afferent coupling (Ca) counts how many components depend on yours. Efferent coupling (Ce) counts how many components yours depends on. A popular instability metric combines them into a neat formula:

I = Ce / (Ce + Ca)

The higher the ratio of outgoing to total dependencies, the more “unstable” a component is considered. Simple. Automatable. And deeply misleading.

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Every other post on my feed celebrates how AI lets us write code faster: whole apps built in a matter of a few hours, 99% AI-generated codebases, hundred-fold productivity gains, and on and on. But does writing code faster actually make us more productive?

A Quick Detour Through a Factory

The Theory of Constraints says that every system’s throughput is limited by a single constraint: its bottleneck. What makes a system more effective? Improving the bottleneck. What makes a system less efficient? Improving anything else.

I know, that second part is counterintuitive. Here’s the thing: if you speed up a non-bottleneck, you don’t improve the system; you produce more work-in-progress that piles up in front of the bottleneck! More inventory. More cost. More waste. The system becomes more expensive to operate, not more productive.

This is a well-established principle in manufacturing, and software manufacturing is no exception.

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