institut f¨ur informatik - PST Thesis Management Interface - LMU

4.1. Realization of GAUTOSAR
Originally the first two of the described GAUTOSAR realization steps have only been conducted
manually. These steps require a domain expert to simultaneously browse and compare
the structure of multiple AUTOSAR metamodels and determine changes among them (step
one), then build a generic metamodel (step two) based on its desired properties (which will
be reflected in the API) and the types of changes that occurred.
The advantage of the approach where the first two steps are conducted manually is that
a human domain expert can instantly recognize change and its influence on the surrounding.
This is due to a human’s ability to implicitly understand context [WSA02]. Furthermore, a
human expert has an understanding of the semantics behind the data structure - in this case
the principles governing model-based and object-oriented software development. An algorithm
can essentially only base its decisions on the syntax of a data structure, which makes
its understanding of context limited. Therefore, creating a generic metamodel is a relatively
easy task for a human expert. The disadvantage of the manual approach is that the current
AUTOSAR metamodels contain between 600 and 900 classes, which makes manual analysis
an extremely complicated and time-consuming task. Moreover, each time a new AUTOSAR
metamodel becomes available and also becomes supported in an Artop release, the process
has to be repeated with consideration of all metamodels that are supported in the particular
Artop release. This includes some or all of the previously supported metamodels and the
new metamodel. However, a fully automated process might not produce results as good as
the manual process due to the semantic gap in algorithmic data processing. The ideal approach
will combine fast automated processing with the contextual knowledge of the human
expert. This semi-automatic approach combines the advantages of both man and machine
while excluding the disadvantages on both sides to the furthest possible extent.
4.1.2. Automation of the GAUTOSAR realization process
The first step of the GAUTOSAR realization process - comparison of version-specific AU-
TOSAR metamodels - was automated in the DiffMap project - a preceding thesis at BMW
Car IT. The results of the DiffMap project including improvements that were contributed
in this thesis are presented in section 4.2. Automation of the second step - creation of the
GAUTOSAR metamodel - is the subject of this thesis and is presented in section 4.3.
The quality of the metamodel comparison result directly influences the quality of the created
generic metamodel. The extent of the generic metamodel then determines the usability
extent of the generic API. As explained in the previous section, automatic comparison cannot
detect all types of changes that can occur between version-specific metamodels due to
a semantic gap. Similarly, some types of changes cannot be automatically resolved by the
engine that uses comparison information to create the generic metamodel (see section 2.3.2).
In order to resolve such issues nevertheless and improve the quality of the generic API, the
comparison result and the generic metamodel should include a possibility for manual intervention
by a domain expert. The comparison result and the generic metamodel are both
Ecore models (see section 2.2.1) that can be manipulated by hand in Eclipse.
The third step is already automated. It involves using an EMF generator that has been
customized for Artop with special JET templates (see section 2.2.3) to generate the desired
result. The generator processes the GAUTOSAR metamodel that is enriched with
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