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AI & Machine Learning

Regression testing (AI systems)

Checking that changes to models or pipelines do not unintentionally degrade existing behaviour.

Also known as: AI regression testing, Non-regression testing

Definition

Regression testing for AI systems is the practice of running a fixed set of evaluation queries against a system after any change — model update, prompt revision, knowledge base expansion, configuration change — to verify that the change did not unintentionally degrade existing behaviour. Unlike traditional software regression testing, which checks for binary pass/fail outcomes, AI regression testing must detect subtle quality degradation: slightly worse relevance rankings, minor drops in citation accuracy, or shifts in confidence calibration that individually seem small but cumulatively erode system quality.

Why it matters

  • Change safety — every system change carries risk; regression testing catches unintended degradation before it reaches users, enabling confident iteration
  • Quality baseline — maintaining a fixed evaluation set creates a stable reference point against which all changes are measured, preventing gradual quality erosion that might not be noticed without systematic measurement
  • Interaction effects — a prompt change that improves answers in one topic area may unexpectedly degrade answers in another; regression testing across the full evaluation set catches these cross-domain effects
  • Compliance evidence — demonstrating that system updates maintain quality levels provides regulatory evidence for EU AI Act compliance, which requires ongoing monitoring throughout the system lifecycle

How it works

AI regression testing operates through a structured process:

Baseline establishment — the current system’s performance is measured across a comprehensive evaluation dataset, producing baseline scores for all tracked metrics (accuracy, precision, recall, faithfulness, calibration). These baselines represent the quality floor that must be maintained.

Change application — a modification is made to the system: a new model version, an updated prompt, additional documents in the knowledge base, or a configuration change.

Re-evaluation — the same evaluation dataset is run against the modified system, producing a new set of metric scores.

Comparison — new scores are compared against baselines, both in aggregate (overall accuracy) and disaggregated (accuracy by topic, by query type, by difficulty level). Statistically significant degradation in any category triggers investigation.

Decision — if no regressions are detected, the change is approved for production. If regressions are detected, they are investigated: is the degradation expected and acceptable (a trade-off for improvements elsewhere)? Is it a genuine bug? Does it affect critical use cases? The team decides whether to deploy, fix, or roll back.

Key practices for effective AI regression testing include:

  • Stratified evaluation sets that cover all important dimensions (topics, jurisdictions, question types, difficulty levels) to catch localised regressions
  • Statistical significance testing to distinguish real regressions from normal variance in model outputs
  • Automated execution integrated into the deployment pipeline, blocking changes that fail regression thresholds
  • Version tracking that links each test run to the specific system state, enabling root cause analysis when regressions are detected

Common questions

Q: How is AI regression testing different from software regression testing?

A: Software regression tests check deterministic, binary outcomes (the function returns the correct value or it does not). AI regression tests deal with probabilistic, graded outcomes — a slightly worse ranking or a marginally less accurate answer. This requires statistical analysis and threshold-based decisions rather than simple pass/fail checks.

Q: How often should regression tests run?

A: After every system change that could affect output quality. For actively developed systems, this means daily or per-deployment. Automated integration into the CI/CD pipeline ensures no change is deployed without regression verification.

References