Which practice is essential for improving automation performance?

• A. Testing and calibration.
• B. Ignoring results.
• C. Reducing data collection.
• D. Randomly adjusting parameters.

Answer: (A)

To improve automation performance, you need a reliable feedback loop built around testing and calibration. Testing exposes how the automation behaves across typical and edge-case scenarios, measuring outputs against desired targets. Calibration then adjusts the system's parameters, thresholds, or models so the results align more closely with those targets. Repeating this cycle—test, measure, adjust, re-test—lets you quantify improvements, catch drift, and steadily raise performance over time.

Ignoring results removes the essential feedback that guides improvement. Reducing data collection hides important signals you rely on to tune the system. Randomly adjusting parameters introduces instability and makes it hard to learn what actually improves performance.