Lecture Notes in Computer Science 3472

278 Part III. Model-Based Test Case Generation
Basically, this is achieved by redundancy: two models (state machines, temporal
formulae, etc.) are built, and detection of a discrepancy among them points at
potential difficulties.
If an artifact that is to be checked is of a purely mathematical nature, then
infinite input domains, or infinite runs, can technologically be coped with by
means of finite representations. It is hence possible to argue about infinite behaviors.
If, on the other hand, this artifact is part of the real world, and includes
sensors, actuators, operating systems, and legacy systems, then these arguments
cannot be applied. The reason is that in general, reasoning about systems involves
reasoning about states, and when hardware is involved, there is no definite
knowledge about these states. The consequence is that the comparison between
an actual system and a model is incomplete, both for complexity and principal
reasons.
Overview This motivates the structure of four chapters in this third part of the
book.
Chapter 10 provides a brief overview of methodological issues in model-based
testing. Pretschner and Philipps discuss both the need for abstraction in practical
applications of model-based testing and different scenarios of model-based
testing.
The selection of a few traces of the model can be done w.r.t. functional,
structural, and stochastic criteria. The first category seems to be amenable to
methodology rather than to technology. In Chapter 11, Gaston and Seifert have a
thorough look at the two other criteria, structural and stochastic criteria. While
structural criteria have been used for finite state machines (transition coverage,
for instance) in previous chapters of the book, they look at coverage criteria at
the level of programming languages. This does not contradict the title of this
part: models may well be specified by means of programming languages, and
code is often used to specify guards and assignments in respective modeling
languages. The authors then have a look at stochastic criteria and review the
discussion on comparing their fault detecting power with testing that is based
on partition the input domain.
In the third Chapter 12 of this part, Lúcio and Samer go one step further.
They assume a selection criterion, be it functional, structural, or stochastic, to
be given. The selection criterion and the model are then used to generate test
cases. The generation process is the subject of their article. Consequently, they
investigate the use of model checking, symbolic execution, theorem proving, and
logic programming. Tools that rely on these and other mechanisms—on-the-fly
testing, in particular—are discussed later in this book, in Chapter 14.
Finally, the fourth Chapter 13 is concerned with a particularly complex class
of systems, namely real-time and mixed discrete-continuous, or hybrid, systems.
Berkenkötter and Kirner first define the two classes of systems. After showing
how real-time systems can be modeled with different formalisms, they show how
test cases can be generated for them. As far as hybrid systems are concerned,
hybrid statecharts and hybrid variants of process algebras are introduced. Because
of the very large state spaces of these classes of systems, they discuss ways