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too vague to be directly transformed into law identifi-
cation pattern instances. But they contain additional,
relevant information about the functionality and con-
text of a system. It seems to be promising to inte-
grate this information to improve the precision of our
method.
In general, our law identification process was used
in the field of cloud computing, health-care and for
this paper in the domain of voting systems. The trans-
formation cards were only used for the latter. From
our experience, our method is usable regarding the
German law for different domains without adaption.
We also found evidence that this observation is also
true for laws from other countries as long as the law
system of the country is a statue law. For example,
Biagioli. et al (Biagioli et al., 1987) describe the very
same structure for Italian laws like we use for German
laws. For case law systems like the one of the US, our
method needs to be adapted. The use of our method
on more domains, for other countries with statue law,
and even for case law countries is under research.
We also plan to formulate rules for the reduction
of core structure candidates. At the moment, we get a
large set of core structures for some problem frames.
This makes the modeling of law identification pattern
instances tedious. Hence, we will improve the sit-
uation by giving guidance to identify irrelevant core
structures or core structures which can be merged.
Finally, we plan to speed up the method execu-
tion by providing tool support for identifying prob-
lem frames for problem diagrams, wizards and rec-
ommenders, which are based on the questionnaires,
for collection the domain knowledge, and automatic
transformation based on this information.
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