What Is Regression - and Why Is It a Critical Concept in Software Testing and Benchmarking?

When improving a software system, an AI model, or a performance component, the goal is always the same: to get better.

But sometimes, a change meant to improve things actually causes a drop in performance or quality. This phenomenon is called regression.

What Is Regression?

Regression is a situation where:

An action, change, update, or new version causes the system to behave worse than it did before.

In simple terms: we upgraded → we made it worse.

Examples of Regression

1. Performance Regression

A new version of a model:

• Runs slower
• Consumes more memory
• Produces fewer FPS
• Generates less stable output

While the previous version was faster.

2. Functional Regression

New code that:

• Breaks a capability that used to work
• Returns different results than before
• Misses cases the old version handled correctly

3. Model Quality Regression

A new model:

• Detects fewer objects
• Misses labels
• Shows lower accuracy

Even though it was supposed to improve.

Why Does Regression Happen?

• Code changes that weren’t tested from every angle
• An optimization that harmed something else
• A logic change that had a negative effect
• Small errors that accumulate into poor performance

Complex systems are highly sensitive to change.

Why Is It Important to Detect Regressions?

Because without detecting them:

• A system can become “worse” without anyone noticing
• Performance can degrade gradually
• Models can lose accuracy over time
• Teams will struggle to understand why “something isn’t working like before”

This is exactly why we have:

• Regression tests
• Reference numbers
• Repeated benchmarks
• Golden output comparisons

All of it exists to make sure - we haven’t gone backwards without noticing.

An Analogy

Think of a runner trying to improve their time. They change their training method, but suddenly become slower. That’s a regression.