2 Why We Need Model-Based Testing

Systems with Finite Models 97
disabled in that state. Every transition out of a state is labeled with one of the
actions that is enabled in that state. For example, in the middle node in Figure 6.1,
two actions are enabled: SortByFirst and ShowText. The heart of the traversal
algorithm can be expressed: in each state, choose an enabled action and execute it.
By this algorithm, we can obtain the original run we used to define this FSM (from
Chapter 5, Section 5.5). We can also obtain many other runs from this same FSM by
choosing other transitions (other enabled actions), traversing different paths around
the links in the diagram (possibly repeating each cycle many times). For example,
the diagram in Figure 6.1 describes many runs that begin
ShowTitles(); SortByFirst(); SortByMostRecent(); ShowText(); ...
There are many more runs that begin
ShowTitles(); ShowText(); ShowTitles(); SortByFirst(); ...
And so on. A run obtained by traversing a graph is sometimes called a traversal or
an unwinding. A collection of related runs is called a scenario. The entire collection
of runs that can be generated from an FSM is called the scenario defined by that
FSM.
Traversing an FSM generated by exploration from a model program is our offline
test generation technique. Different traversal algorithms can achieve different
measures of behavioral coverage (Chapter 8).
6.1.2 Scenario FSMs and the true FSM
An FSM whose purpose is to define a scenario is called a scenario FSM. Itis
possible to define many different scenario FSMs for any model program; each
different scenario FSM defines a different collection of runs. There is one FSM that
can generate all of the runs of a model program: its true FSM.
When the model program has a small number of states and actions, it is possible to
write down its true FSM. Table 6.2 and Figure 6.2 show the state transition table and
the state transition diagram, respectively, for the true FSM of our newsreader model
program (Chapter 5, Figures 5.4 and 5.5). Notice that the states and transitions in
Figure 6.1 also appear in Figure 6.2 (the initial state is shaded in both diagrams).
Here in Table 6.2, we use a more compressed format than in Table 6.1. We merge
several transitions into each row by using X to indicate “don’t care” values in the
current state (where any value of particular variable is handled the same way, or is
simply ignored) and a dash (–) for “don’t change” values in the next state (where
the value of a particular variable is the same as in the current state). It turns out that
here this compression results in just one row for each action. Notice that each row
in Table 6.1 is also represented (in compressed format) in Table 6.2.
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