Regression Testing - and Why It Is the Most Important Defense Against System Degradation

In the previous post we understood what regression is: a change that was meant to improve things - but in practice hurts performance, stability, or quality.

Now comes the natural question: how do we prevent regressions?

This is where regression tests come in.

What Are Regression Tests?

Regression tests are a fixed set of tests designed to verify:

that any change in code, version, model, or configuration - did not cause the system’s behavior to degrade compared to what it was before.

In other words: they check that the system hasn’t “gone backwards.”

How Do Regression Tests Work in Practice?

The idea is simple:

1. There’s a reference - behavior considered “correct.”
2. The same operation is run on the new version.
3. The results are compared.
4. If there’s a significant deviation - there’s a regression.
5. If the results are identical or better - you can proceed with confidence.

What Exactly Gets Compared?

It depends on the system.

1. Performance Regression Tests

Measured metrics:

• Runtime
• FPS
• Memory usage
• Throughput
• Latency
• Hardware resource consumption

Any small change in these metrics could indicate a regression.

2. Output Regression Tests

Comparing new output against:

• Golden Output - a standard result saved in advance
• Reference Labels - known labels
• Bitwise Output - in systems requiring bit-identical output

If the output differs materially - there’s a problem.

3. Functional Regression Tests

Checking:

• Whether the API still works
• Whether existing functions still return valid values
• Whether the same number of objects is returned
• Whether relevant parts of the system haven’t broken

Why Are Regression Tests Especially Critical in ML Systems?

ML systems differ from regular software systems:

• Outputs are sensitive to every small change
• Dependencies exist between layers and configurations
• Optimizations can affect other parts unexpectedly
• Behavior is influenced by the entire pipeline, not just a single line of code
• Tests are about “quality,” not just strict “correctness”
• Inference code runs on dedicated hardware and can slow down/speed up unpredictably

That’s why in ML systems - regressions happen very easily, and are hard to detect without proper tooling.

How Do You Build a Good Set of Regression Tests?

1. Use fixed data Same inputs → same tests → precise comparison.

2. Keep standard references Golden outputs, metrics, runtimes, configurations.

3. Set deviation thresholds For example: “accuracy drop above 5% → regression.”

4. Run the tests on every change In CI, in PRs, or before a release.

5. Write tests that cover both performance and output Combining both aspects catches nearly every type of regression.

A Simple Example

Suppose we have a YOLO model.

Reference: on a certain image - 5 objects should be detected, and processing time should be 12ms.

In the new version:

• The model detects only 3 objects, or
• The model runs at 25ms

Both results indicate a regression. Regression tests will flag this immediately - before the change merges into the main codebase or ships to customers.

An Analogy

Think of a running test with fixed parameters:

• A route
• A target time
• A number of stops

If every new training session checks the exact same parameters, you can tell whether the runner is improving - or regressing.

Regression tests are the same test - just for the world of software and ML.

Summary

Regression tests are the protective layer that ensures every change to a system:

• Doesn’t break functions
• Doesn’t hurt performance
• Doesn’t lower model quality
• Doesn’t change outputs unexpectedly
• And doesn’t send the system backwards

In the world of ML - where it’s very easy to create regressions - regression tests are a necessity, not a recommendation.