Compare a Quick Patch vs a Root-Cause Fix
Results
Visualization
How It Works
Each problem type carries assumed success rates: a patch scores decently because it removes the immediate symptom, while a root-cause fix scores higher because it prevents recurrence. Severity scales both rates slightly upward for high-severity cases where more effort is justified. Side effects are described qualitatively: a patch is low risk but can hide the real bug, while a root-cause change is broader and needs code review and testing. The bar chart shows the two success rates, and the recommendation weighs severity against effort.
What Should You Do?
For production incidents, a quick patch to stop the bleeding is fine as a first response, but always follow with a root-cause fix before declaring done. For low-severity or prototype code, a patch may be the pragmatic choice that saves time. Track patched issues in a backlog so they are not forgotten, because unaddressed workarounds accumulate as technical debt. When you do the root-cause fix, add a test that fails without it so the bug cannot quietly return.
Frequently Asked Questions
Are these success rates real?
No. They are illustrative assumptions based on common engineering patterns, not measured for your specific codebase.
When is a patch the right call?
For low-severity issues, prototypes, or as an immediate stopgap during an incident before the proper fix ships.
Why does root-cause win for high severity?
High-severity bugs in production tend to recur and cause more damage, so the higher effort of a real fix is usually worth it.
What is the downside of patching?
A patch masks the symptom and can hide the real defect, letting similar failures appear elsewhere later.
How do I justify the choice to my team?
Use the shown success and side-effect tradeoff plus severity to explain why a patch or root-cause fit the situation and deadline.