2 Why We Need Model-Based Testing

218 Testing Systems with Complex State
weight associated with each action that is initially some positive number. When an
action is selected the corresponding weight is decremented by one. If, in a given state,
the enabled actions are a1,...,ak with weights w1,...,wk, then an action ai is not
selected if wi = 0, ai is selected with probability wi/(w1 +···+wk), otherwise.
A useful analogy is with a box of colored marbles. Each action corresponds to
a particular color and selecting an action corresponds to removing one randomly
chosen marble from the box. This continues until the box is empty. This is a powerful
and computationally cheap technique to produce test runs where weights are chosen
in a way that matches a certain operational profile.
Using groupings for pruning. A further technique that is a powerful way to deal
with complex state is to use state or transition groupings. Suppose that each model
state is mapped to an abstract value called a group label that characterizes some interesting
properties of that state. The grouping function can, for example, be a coverage
function. The grouping function may also be predicate abstraction in which case the
group label is a sequence of Boolean values, or it may be a function that takes a sequence
to a set. The idea is to dynamically keep track of all group labels encountered
during test execution; an action that leads to a new group is prioritized over an action
that does not. Here the driving factor is coverage of new groups, and the search
space is pruned accordingly. The same idea can be applied to transitions.
12.6 Exercises
1. Correct the faulty bag implementation and rerun ct on it.
2. The custom strategy shown in Section 12.4 chooses an action with maximum
reward. Implement another SelectAction method that chooses an action with
probability that is proportional to the reward.
3. Implement a strategy for decrementing action weights discussed in Section
12.5.4. In order to assign initial weights to actions, use state properties.
4. Implement a strategy that records for each transition (s1,a,s2) an abstract
transition (g(s1),g(a),g(s2)), where g is a grouping function; use GetHashCode
for g. If in a given state, there are enabled actions that transition to states in
new groups, select one of those actions randomly, otherwise choose any action
randomly.
*5. A state s is partially explored if there exists an unexplored action that is enabled
in s. A state s is interesting either if it is partially explored or if there exists
an explored path from s to a partially explored state. Implement a strategy that
gives priority to actions that lead to interesting states.
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