Python Debugging Method: Reproduce, Inspect, Fix, Verify

This guide explains debugging method: reproduce, inspect, fix, verify through a focused Python example. You will learn the behavior to expect, why it matters in a real program, and how to recognize the common mistakes before applying it in your own project.

What You Will Learn

A reliable debugging cycle is reproduce the defect, inspect the relevant values, make a focused fix and verify both the failing case and a normal case.

When and Why to Use It

The following example focuses on a practical use of Debugging Method: Reproduce, Inspect, Fix, Verify.

Step-by-Step Example

def percentage(part, whole):
    if whole == 0:
        raise ValueError("Whole cannot be zero")
    return part / whole * 100

assert percentage(25, 100) == 25
assert percentage(0, 100) == 0
print("Checks passed")

Understanding the Example

An earlier implementation might have divided whole by part, reversing the calculation. These assertions verify two expected outcomes after correction.

Expected Output

Checks passed

Important Behavior and Edge Cases

Do not change several unrelated sections at once; without a reproducible failing example you cannot know what fixed the defect.

Try It Yourself

Write a failing test for a wrong discount function, fix it and rerun both tests.

Common Beginner Mistakes

• Do not change several unrelated sections at once; without a reproducible failing example you cannot know what fixed the defect.
• Handling the symptom rather than identifying the failing operation, or letting a broad exception handler hide defects.
• Skipping the practice step and assuming the example covers every input. Test at least one normal case and one edge case relevant to this topic.

Key Takeaways

• A reliable debugging cycle is reproduce the defect, inspect the relevant values, make a focused fix and verify both the failing case and a normal case.
• An earlier implementation might have divided whole by part, reversing the calculation.
• Apply the technique deliberately, checking its documented input requirements and failure behavior before integrating it into a larger project.