This article reviews a class of adaptive group testing procedures that operate under a probabilistic model assumption as follows. Consider a set of $N$ items, where item $i$ has the probability $p$ ($p_i$ in the generalized group testing) to be defective, and the probability $1-p$ to be non-defective independent from the other items. A group test applied to any subset of size $n$ is a binary test with two possible outcomes, positive or negative. The outcome is negative if all $n$ items are non-defective, whereas the outcome is positive if at least one item among the $n$ items is defective. The goal is complete identification of all $N$ items with the minimum expected number of tests.