Optimal Transfer Trees and Distinguishing Trees for Testing Observable Nondeterministic Finite-State Machines
Issue No. 01 - January (2003 vol. 29)
<p><b>Abstract</b>—The fault-state detection approach for blackbox testing consists of two phases. The first is to bring the system under test (SUT) from its initial state to a targeted state <em>t</em> and the second is to check various specified properties of the SUT at <em>t</em>. This paper investigates the first phase for testing systems specified as observable nondeterministic finite-state machines with probabilistic and weighted transitions. This phase involves two steps. The first step transfers the SUT to some state <i>t'</i> and the second step identifies whether <em>t'</em> is indeed the targeted state <em>t</em> or not. State transfer is achieved by moving the SUT along one of the paths of a transfer tree (TT) and state identification is realized by using diagnosis trees (DT). A theoretical foundation for the existence and characterization of TT and DT with minimum weighted height or minimum average weight is presented. Algorithms for their computation are proposed.</p>
Average weight, distinguishing tree, nondeterministic finite-state machine, testing, transfer tree, weighted height.
F. Zhang and T. Cheung, "Optimal Transfer Trees and Distinguishing Trees for Testing Observable Nondeterministic Finite-State Machines," in IEEE Transactions on Software Engineering, vol. 29, no. , pp. 1-14, 2003.