Empirical study on how engineers in the automotive industry work with model-driven engineering (MDE), presented at MODELS 2012 in Innsbruck.

1. Forces and Frictions affecting MDE
An exploratory study by Adrian Kuhn,
Gail Murphy and C. Albert Thompson
University of British Columbia

2. Research partnership between General
Motors, IBM and 8 Canadian universities.
www.necsis.ca
Kuhn
etal,
MODELS
2012
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3. We
interviewed
20
People
at
GM.
Engineers and team leads.
Kuhn
etal,
MODELS
2012
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4. So
here
is
what
we
learned…
Engineers seem to prefer the linear
reading path of text over visual languages.
Complex tool chain leads to update cycles
of days rather than minutes.
Domain experts invent novel systems, but
the lack power to extend the language.
Kuhn
etal,
MODELS
2012
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5. But
first,
what
is
a
model?
Kuhn
etal,
MODELS
2012
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6. In-­‐silico
simulaKon
(physical
model)
Architecture
(high-­‐level
model)
— Class diagrams in IBM Rhapsody
Algorithm
logic
(low-­‐level
model)
— Diagrams in MathWorks Simulink
Kuhn
etal,
MODELS
2012
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7. What
is
their
MDE
process?
Roles and artifacts.
Kuhn
etal,
MODELS
2012
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8. Sketch of the modeling process, taken from
our interview notes.
Kuhn
etal,
MODELS
2012
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9. Domain experts invent novel algorithms,
creating requirements documents and
sketching software models.
Kuhn
etal,
MODELS
2012
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10. Software modelers implement algorithms,
creating, or refining, software models based
on requirements documents.
Kuhn
etal,
MODELS
2012
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11. Domain experts and modelers use ad-hoc
model patches (i.e. Powerpoint slides with
screenshots) to communicate changes!
Kuhn
etal,
MODELS
2012
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12. Code generation is automated, using code
generation rules defined by a specialized
team of code generation experts.
Kuhn
etal,
MODELS
2012
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13. Test engineers work with all artifacts, i.e.
requirements documents, software models,
generated sources and tests.
Kuhn
etal,
MODELS
2012
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14. ObservaKons
Forces and Frictions
Kuhn
etal,
MODELS
2012
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15. Force:
trigger
of
essen.al
complexity,
likely
independent
of
abstrac.on
chosen.
Fric.on:
trigger
of
accidental
complexity,
namely
complexity
introduced
by
tools
and
language.
Kuhn
etal,
MODELS
2012
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16. Force:
Teams
are
typically
working
on
mul.ple
versions
of
the
same
so?ware
model.
Fric.on:
Yet
engineers
lack
proper
tooling
to
iden.fy
and
share
differences.
Kuhn
etal,
MODELS
2012
16

17. Force:
Domain
experts
use
a
rich
set
of
visual
languages
to
invent
novel
algorithms.
Fric.on:
Yet
they
lack
tool
support
to
define
their
own
liDle
languages.
Kuhn
etal,
MODELS
2012
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18. Force:
Inven.ng
novel
algorithms
for
vehicle
control
is
an
exploratory
ac.vity.
Fric.on:
Yet
tes.ng
on
vehicles
suffers
from
lack
of
run.me
changeability.
Kuhn
etal,
MODELS
2012
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19. Force:
Engineers
o?en
need
to
navigate
between
requirements
and
models.
Fric.on:
Yet
they
there
is
no
tool
support
for
point-­‐to-­‐point
traceability.
Kuhn
etal,
MODELS
2012
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20. Findings
What we learned …
Kuhn
etal,
MODELS
2012
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21. Engineers prefer the linear reading
path of text over visual languages.
Kuhn
etal,
MODELS
2012
21

22. Complex tool chain leads to update
cycles of days rather than minutes.
Kuhn
etal,
MODELS
2012
22

23. Domain experts invent novel systems, but
lack the power to extend the language.
Kuhn
etal,
MODELS
2012
23

24. Related
work
on
user
studies
• Hutchinson
etal,
ICSE
2011
• Mohagheghi
and
Dehlen,
MODELS
2008
• Forward
and
Lethbridge,
MISE
2008
• Heijstek
and
Chaudron,
SEAA
2010
Kuhn
etal,
MODELS
2012
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25. So
here
is
what
we
learned…
Engineers seem to prefer the linear
reading path of text over visual languages.
Complex tool chain leads to update cycles
of days rather than minutes.
Domain experts invent novel systems, but
the lack power to extend the language.
Kuhn
etal,
MODELS
2012
25

26. Download our papers"
http://arxiv.org/abs/1207.0855 "
http://arxiv.org/abs/1209.5800
Kuhn
etal,
MODELS
2012
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