If QI can identify the smallest action that materially improves confidence, assurance can become adaptive instead of regression-first.

The hypothesis

A Quality Intelligence system can choose a better next assurance action by estimating which action will reduce the most decision-relevant uncertainty for the lowest cost and time.

The traditional default

When risk is uncertain, quality teams often add more testing: run a larger regression suite, execute more scenarios or extend the validation window.

More evidence can help. But not all evidence changes the decision equally.

The experiment question

Given the current risk and confidence position, which single assurance action is most likely to improve the quality decision?

Candidate actions

  • run a targeted integration test;
  • execute broader regression;
  • inspect a high-change dependency;
  • run a production-like performance scenario;
  • retrieve historical incidents;
  • perform an AI robustness evaluation;
  • collect additional runtime telemetry;
  • request human domain review.

What the system needs to estimate

Decision relevance

How directly does the action address the uncertainty that currently matters?

Expected information gain

How likely is the action to produce evidence that changes the confidence position?

Cost

What engineering effort or infrastructure is required?

Elapsed time

How quickly can the evidence arrive?

Consequence

How important is the risk being reduced?

Experiment design

Select a set of historical or simulated release situations where the eventual outcome is known.

At an intermediate point in each scenario, hide the later evidence and ask three strategies to choose the next assurance action:

  • traditional regression-first;
  • human expert selection;
  • QI information-value selection.

Then reveal the result of the selected action and measure how much the quality decision improved.

What we measure

  • time to a defensible decision;
  • cost of assurance performed;
  • confidence improvement;
  • high-impact risks discovered;
  • unnecessary execution avoided;
  • agreement with expert judgment.

What would falsify the hypothesis?

The approach fails if the selected “high-value” action regularly misses material risk, if estimating information value is more expensive than executing the tests, or if experts consistently outperform the model for reasons the system cannot capture.

Expected failure modes

  • the model prefers cheap actions that produce little useful evidence;
  • historical data biases selection toward familiar failure modes;
  • uncertainty is understated;
  • actions interact, making single-action value difficult to estimate;
  • business consequence is poorly represented.

The next experiment

Allow the system to select a sequence of assurance actions, recalculating confidence after each one.

That would move QI from static prioritization toward adaptive assurance planning.

The future of regression may be less about deciding what to run first—and more about deciding what evidence is worth buying next.