Clean Code in Practice: Lessons from Building Optimization Software

When writing about clean code, the conversation often circles around the same mantras: short functions, no duplication, comment everything. But when you're building complex systems - like optimization engines or simulation platforms - not all practices matter equally. After more than a decade of bringing Operations Research (OR) models into production, I've learned which clean code habits actually pay off, and which are mostly noise.

What Really Matters

1. Express Intent Clearly

The true goal of naming and structure is that code tells its own story. A reader should understand what a piece of code is doing without having to dive into how. For example, optimize_production_plan() communicates far better than run_algorithm().

2. Simplicity Wins Every Time

The KISS principle (“Keep It Simple, Stupid”) is underrated. Clever abstractions or over-engineered patterns may look smart, but simple, straightforward solutions almost always scale better. A heuristic written in 40 clear lines is worth more than a tangle of nested design patterns.

3. Consistency Across the Codebase

Whether it's naming, formatting, or error handling - consistency is more important than the particular choice. A consistent codebase lets developers focus on algorithms and logic, not on deciphering someone else's style.

4. No Hidden Side Effects

A function should do exactly what it says - nothing more. Hidden state changes or dependencies (like assuming another function was called first) create fragile systems. In optimization software, where algorithms interact in complex and iterative ways, this principle is crucial.

5. Keep Function Arguments to a Minimum

If a function needs more than two parameters, it often hints at missing structure. Group related parameters into objects (vehicle, product, ProblemInstance) instead of passing long argument lists. This reduces errors and makes code easier to extend later.

What Matters Less (Than People Think)

1. Always Making Functions Short

Yes, functions should do one thing. But forcing a 20-line heuristic into five micro-methods can obscure the logic. Sometimes, a slightly longer, cohesive function is easier to read.

2. Comment Every Line

If your code needs a comment for every line, the code is the problem. Self-explanatory naming and clear structure reduce the need for excessive commentary.

3. Over-Abstracting Everything

Abstract factories for problems, strategies, and solutions may sound elegant, but they often add unnecessary complexity. Abstractions should evolve out of recurring patterns - not be introduced preemptively.

4. Following Every Rule Blindly

Rules like “avoid all duplication” or “never use break/continue” are not universal truths. In optimization, duplication sometimes improves clarity, and breaking out of a loop can be the most readable solution.

The Bottom Line

Clean code isn't about following every guideline in a book. It's about choosing practices that reduce friction in collaboration, debugging, and scaling.

In the end, clean code is just another form of optimization: it's about making your future work - and the work of your colleagues - more efficient while delivering robust software to customers.