2 research outputs found
Property-Based Methods for Collaborative Model Development
Get PDFIndustrial
applications
of
mo del-driven
engineering
to
de-
velop
large
and
complex
systems
resulted
in
an
increasing
demand
for
collab oration
features.
However,
use
cases
such
as
mo del
di�erencing
and
merging
have
turned
out
to
b e
a
di�cult
challenge,
due
to
(i)
the
graph-
like
nature
of
mo dels,
and
(ii)
the
complexity
of
certain
op erations
(e.g.
hierarchy
refactoring)
that
are
common
to day.
In
the
pap er,
we
present
a
novel
search-based
automated
mo del
merge
approach
where
rule-based
design
space
exploration
is
used
to
search
the
space
of
solution
candi-
dates
that
represent
con�ict-free
merged
mo dels.
Our
metho d
also
allows
engineers
to
easily
incorp orate
domain-sp eci�c
knowledge
into
the
merge
pro cess
to
provide
b etter
solutions.
The
merge
pro cess
automatically
cal-
culates
multiple
merge
candidates
to
b e
presented
to
domain
exp erts
for
�nal
selection.
Furthermore,
we
prop ose
to
adopt
a
generic
synthetic
b enchmark
to
carry
out
an
initial
scalability
assessment
for
mo del
merge
with
large
mo dels
and
large
change
sets
Automated Model Merge by Design Space Exploration
Get PDFIndustrial applications of model-driven engineering to develop large and complex systems resulted in an increasing demand for collaboration features. However, use cases such as model differencing and merging have turned out to be a difficult challenge, due to (i) the graph-like nature of models, and (ii) the complexity of certain operations (e.g. hierarchy refactoring) that are common today. In the paper, we present a novel search-based automated model merge approach where rule-based design space exploration is used to search the space of solution candidates that represent conflict-free merged models. Our method also allows engineers to easily incorporate domain-specific knowledge into the merge process to provide better solutions. The merge process automatically calculates multiple merge candidates to be presented to domain experts for final selection. Furthermore, we propose to adopt a generic synthetic benchmark to carry out an initial scalability assessment for model merge with large models and large change sets
