2 Why We Need Model-Based Testing

Advanced Topics 271
action. When executing an action, the strategy may record history. The selection of
actions is always performed on controllable actions, because the strategy is really
the tester strategy. By knowing that certain actions are observable, the strategy can
learn from the behavior of the IUT and use that knowledge to improve its selection
of controllable actions. The following two situations are particularly interesting.
IUT is determinisitic. A typical situation is that the behavior of the IUT is known to
be deterministic. This means that if an observable action happens in a certain state,
then the same observable action will happen again when that same state is visited.
Assume that the model program is written in such a way that its states correspond
to the states of the IUT so that there are no “hidden” states. The model program
may still allow multiple choices, because it is not known what the particular implementation
is, only that it is deterministic. Suppose also that all states of the model
program are either passive or active.
The strategy can record in a map P for each reached passive state the observable
action that occurred in that state. Say that a state is partially explored if it is active and
there exists an unexplored controllable action that is enabled in that state. Suppose
that the goal of the strategy is to maximize transition coverage. When computing
what controllable action to select, a possible algorithm could be to select an action
that has not been explored or to select an action that takes the strategy closer to a
partially explored state. Such an algorithm should be incremental. In this calculation,
P is used to determine what transitions take place from passive states.
Suppose, for example, that the bag implementation in Figure 12.1 is extended so
that it implements the draw operation deterministically by picking the lexicographically
least element from the bag. This would imply that, during test execution, each
time state 6 is visited in Figure 16.3, the observable action is Draw Finish("a").
IUT is random. Another situation is that the behavior of the IUT is random. In this
case the notion of partially explored states can be applied to all states, including
passive states. If the probabilities of the different observable actions are known, the
strategy can select a controllable action that is either unexplored or leads, with high
probability, closer to a partially explored state.
Suppose, for example, that the bag implementation in Figure 12.1 is extended so
that it implements the draw operation in such a way that each element in the bag is
equally likely to be drawn. This would imply that, during test execution, each time
state 6 is visited in Figure 16.3(a), both Draw Finish("a") and Draw Finish("b") are
equally likely to occur. If state 6 is visited multiple times, it is very unlikely that the
same element is chosen each time. In fact, it follows under the given assumptions
that the probability that both "a" and "b" where chosen at least once at some point
grows exponentially in the number of times that state 6 is visited.
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