Code That “Works” Is Not the Same as Code That Is Correct

Most production bugs don’t come from bad algorithms.

They come from unstated assumptions.

Consider this function:

def average(nums):

    return sum(nums) / len(nums)

It’s logically correct.
It will pass unit tests written for the “happy path.”
It may even pass an interview.

And yet, it is fragile.

average([])

Now you have a runtime failure — not because the math is wrong, but because the contract was never defined.

The Real Issue Isn’t the Error

The issue is that the function:

• Assumes non-empty input
• Delegates responsibility to the caller
• Fails implicitly rather than intentionally

In small codebases, this is survivable.
In large systems, this becomes technical debt.

A More Explicit Contract

def average(nums):

    if not nums:

        raise ValueError("Input list cannot be empty")

    return sum(nums) / len(nums)

This version:

• Defines its boundary
• Fails fast and predictably
• Communicates intent to future readers (and future you)
• 
  

Engineering Perspective

At scale, software fails at interfaces, not implementations.

Clear contracts:

• Reduce ambiguity
• Localize failures
• Make systems easier to reason about

This is not defensive programming —
this is professional programming.

Takeaway

If a function relies on an assumption,
make that assumption explicit in code.

Correctness is not just about logic.
It’s about guarantees.

#SoftwareEngineering #CleanCode #SystemDesign #Backend #Python #ProfessionalDevelopment