perf-theory-tester

A performance test validates a hypothesis by comparing new metrics against baseline measurements after applying a single change. It requires an explicit test plan, baseline data, and clear success criteria to produce a reliable verdict on system behavior.

Baseline tracking in performance experiments is the process of capturing consistent baseline measurements before applying a change. These baseline metrics serve as the control data required to evaluate whether a new configuration improves or degrades system behavior.

To measure the impact of a new caching strategy on latency, run a single controlled test comparing latency metrics against a baseline. You must define a threshold for improvement beforehand to determine if the change is successful.

Yes, you need an explicit test plan before starting performance validation. A defined test plan with baseline measurements and clear success criteria ensures the experiment is controlled, reproducible, and yields reliable verdicts.

The best way to ensure reproducible results in performance testing is to run single, controlled experiments with explicit test plans, consistent baseline tracking, and clear success criteria. Applying one change at a time within a defined scope guarantees reliable comparisons.