This document summarizes a study that examined the relationship between functional size measurement (FSM) and processor load for software models in AUTOSAR. The study developed linear regression models to estimate processor load based on COSMIC FSM. An automation tool was created to measure FSM for AUTOSAR models to speed up the data collection process. The regression models were found to estimate processor load with 90% accuracy based on evaluation with 24 software models.

1. FUNCTIONAL SIZE MEASUREMENT
FOR PROCESSOR LOAD
ESTIMATION IN AUTOSAR
Hassan Soubra, Alain Abran, Mehdi Sehit

2. Agenda
Introduction
Research Issue
Litterature review
Overviews of COSMIC, AUTOSAR and SYMTA/S
Experimental Set-up
Results
Conclusions
1
3
4
5
7
6
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3. Introduction
• Software functional size is a key input for building
software development estimation models, effort
models, benchmark models, and quality models
• It can also be used for purposes such as processor
load estimation, network traffic estimation and
acceptance condition estimation
• Context: Autosar
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4. Litterature review
COSMIC based
guideline and
procedure for
AUTOSAR
Prediction and
verification of
timing
constraints of
embedded
software
Procedure and
tool for
Simulink
Basic timing
model to allow
application of
such timing
interfaces
Procedure and
tool for UML
Code Size
Optimizing
memory
requirements
respects real-
time
schedulability
constraints
Load balancing
mechanisms
for efficient
utilization of
CPU
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5. Overviews of COSMIC, AUTOSAR and SYMTA/S
COSMIC
measures
the
Func@onal
User
Requirements
(FUR)
of
soGware.
Func@onal
size
measured
by
COSMIC
is
designed
to
be
independent
of
any
implementa@on
decisions
embedded
in
the
opera@onal
ar@facts
of
the
soGware.
AUTOSAR
provides
a
set
of
concepts
and
a
methodology
for
design
and
implementa@on
of
automo@ve
E/E
systems.
AUTOSAR
methodology
follows
a
model-­‐driven
approach
SYMTA/S
Models
and
analyzes
real-­‐
@me
embedded
systems
in
order
to
measure
system
performance
(e.g.
Worst
Case
Execu@on
@me
-­‐WCET,
CPU
load,
end
to
end
latencies,
etc.)
while
taking
into
account
various
scheduling
constraints
and
differing
execu@on
scenarios.
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6. Overviews of COSMIC, AUTOSAR and SYMTA/S
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7. Experimental Set-up
Architecture Number
of
ECUs
in
the
architecture
Total
Number
of
AUTOSAR
models
used
in
the
architecture
Total
Number
of
Runnables
used
in
the
architecture
A 1 107 107
B 2 12 24
C 3 5 15
D 4 21 84
E 5 7 35
F 6 11 66
G 7 1 7
Total
number
used
in
all
architectures
164
338
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8. Approach
We
used
linear
regression
analysis
to
build
es@ma@on
models
of
ECU
processor
load
for
AUTOSAR
models.
Correlate
the
rela@on
between
ECU
processor
load
and
COSMIC
func@onal
size
()
from
steps
1
&2
Observe
the
processor
load
In
the
AUTOSAR
model
developed
using
SYMTA/S,
run
from
0%
(free)
to
100%
(fully
occupied).
Measure
the
func@onal
size
of
an
input
AUTOSAR
model
AGer
its
alloca@on
to
one
of
the
seven
architectures.
To
speed
up
the
measurement
process
and
reduce
the
possibility
of
human
error,
we
used
an
automated
prototype
tool
developed
in
our
study.
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9. Automation Tool
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10. Automation Tool
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11. Results
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12. Linear regression to estimate processor load
The
mean
difference
between
the
actual
data
and
the
es@mated
data
for
the
24
models
is
10.59
%.
The
accuracy
of
the
es@mates
is
approximately
90%.
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13. Conclusion
• A study of the relationship between FSM and
processor load, in AUTOSAR.
• The findings have demonstrated the dependence
of ECU processor load on COSMIC functional size.
• Automation prototype tool.
• 24 models were used to verify the accuracy of the
estimates produced by our automated approach.
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14. DO YOU HAVE
ANY
QUESTIONS?
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15. hassan.soubra@estaca.fr
CONTACTS