Simulink, patterns, extraction

1. Submodel
Pattern Extraction
for Simulink Models
James R. Cordy
Queen’s University
NECSIS Automotive Partnership Canada

2. MPE
near-miss clones
near-miss clone detection
Simone
analysis of GM models
pattern extraction
pattern evolution

3. MPE
near-miss clones
near-miss clone detection
Simone
analysis of GM models
pattern extraction
pattern evolution

4. Model Pattern Engineering
discover, catalogue and formalize
submodel patterns
emergent
domain-specific, client-specific

5. discovery
analysis, identification
methodology, techniques
classification
characterization, formalization
notation, tooling, catalogues
application
deployment, analysis
organization, documentation, use cases

6. why?
reuse in model development
standards/consistency analysis/enforcement
failure/change propagation in model maintenance
verification/test optimization
deployment variation/optimization
model product lines

7. MPE
near-miss clones
near-miss clone detection
Simone
analysis of GM models
pattern extraction
pattern evolution

8. code clones
copy-paste programming
efficient, widely used
problematic

9. code clones
type 1 - exact
bool ConfNextToken (char **p) {
// skip white space
while (1)
switch (**p) {
case 't' : // ignore
case ' ' :
(*p)++;
break;
case '0' :
return FALSE;
default :
return TRUE;
};
}
bool ConfNextToken (char **p) {
// skip white space
while (1)
switch (**p) {
case 't' : // ignore
case ' ' :
(*p)++;
break;
case '0' :
return FALSE;
default :
return TRUE;
};
}
[Roy, Cordy, Koschke SCP 2009]

10. code clones
type 1 - exact
bool ConfNextToken (char **p) {
// skip white space
while (1)
switch (**p) {
case 't' : // ignore
case ' ' :
(*p)++;
break;
case '0' :
return FALSE;
default :
return TRUE;
};
}
bool ConfNextToken (char **p)
{
while (1) switch (**p)
{
case 't': case ' ':
// just skip
(*p)++;
break;
case '0':
// eof
return FALSE;
default:
// something we want
return TRUE;
};
}
[Roy, Cordy, Koschke SCP 2009]

11. code clones
type 2 - renamed
bool ConfNextToken (char **p) {
// skip white space
while (1)
switch (**p) {
case 't' : // ignore
case ' ' :
(*p)++;
break;
case '0' :
return FALSE;
default :
return TRUE;
};
}
bool NextToken (char **bp)
{
while (1) // not really
switch (**bp)
{
case 't': case ' ':
// next
(*bp)++;
break;
case '0':
return 0;
default:
return 1;
};
}
[Roy, Cordy, Koschke SCP 2009]

12. code clones
type 3 - near miss
bool ConfNextToken (char **p) {
// skip white space
while (1)
switch (**p) {
case 't' : // ignore
case ' ' :
(*p)++;
break;
case '0' :
return FALSE;
default :
return TRUE;
};
}
bool NextToken (char **bp)
{
while (1) // not really
switch (**bp)
{
case 't':
(*bp)++
case ' ':
break;
case '0':
return 0;
default:
return 1;
}
}
[Roy, Cordy, Koschke SCP 2009]

13. 1
Fn
2/3*R*muk
1
Tfmaxk
Torque
Conversion
mus/muk
2
Tfmaxs
Ratio of static
to kinetic
model clones
type 1 - exact
1
Fn
2/3*R*muk
1
Tfmaxk
Torque
Conversion
mus/muk
2
Tfmaxs
Ratio of static
to kinetic
[Alalfi, Cordy, Dean, Stephan, Stevenson ICSM 2012] [Störrle SSM 2013]

14. model clones
type 2 - renamed
[Alalfi, Cordy, Dean, Stephan, Stevenson ICSM 2012] [Störrle SSM 2013]

15. model clones
type 3 - near miss
[Alalfi, Cordy, Dean, Stephan, Stevenson ICSM 2012] [Störrle SSM 2013]

16. MPE
near-miss clones
near-miss clone detection
Simone
analysis of GM models
pattern extraction
pattern evolution

17. ConQAT
graph-based model
clone detection
[Deissenboeck et al. IWSC 2010]

18. graph flattening ignores
hierarchical structure
problems with
near-miss

19. graph flattening ignores
hierarchical structure
problems with
near-miss

20. code-based near-miss works well
NiCad, iClones, others
mature, accurate, efficient
handles unexpected differences
threshold-based, tunable
scalable

21. Parsing &
Potential Clone
Extraction
4
Original
Code Base
Original
Code Base
3
2
Parsing &
Potential Clone
Extraction
1
Pretty-printed
Potential Clones
Choose
(Repeat) Next
Potential Clone
as Exemplar
Choose Next
Potential Clone
as Exemplar
3
2
1
Normalized
Potential Clones
NiCad
Pretty-printed
Potential Clones
4
3
2
Renaming,
Filtering,
Normalization
4
1
Normalized
Potential Clones
3
2
1
Normalized
Potential Clones
1. Parse / Extract
2. Rename / Filter / Normalize
2. Rename / Filter / Normalize
1. Parse / Extract
4
Renaming,
4Filtering,
3
2
Normalization
1
Cluster
4
3
Comparable
2
1
Size PCs
(Repeat)
Pairwise
Cluster
Comparison
Comparable
with Exemplar
Size PCs
Comparable Size
Normalized
Potential Clone Cluster
Potential Clones
3. Clone Analysis
5.pc
12.pc
23.pc
17.pc
67.pc 15.pc
18.pc
. . . 22.pc 21.pc
63.pc
. . . 78.pc 37.pc
39.pc
. . . 97.pc
. . . 44.pc
...
5.pc
12.pc
23.pc
17.pc
67.pc 15.pc
18.pc
. . . 22.pc 21.pc
63.pc
. . . 78.pc 37.pc
39.pc
. . . 97.pc
. . . 44.pc
...
Pairwise
Comparison
with Exemplar
Comparable Size
Clone
Potential Clone Cluster
Classes
Clone
Classes
3. Clone Analysis
parse - extract - normalize - diff threshold
[Roy, Cordy ICPC 2008]

22. crazy idea:
can we use near-miss text code methods
on graphical models?
“Models are source code too”
Mark Harman, keynote at SCAM 2010
[Harman SCAM 2010]

23. MPE
near-miss clones
near-miss clone detection
Simone
analysis of GM models
pattern extraction
pattern evolution

24. Simone
Simulink near-miss clone detection
experiment
adapt NiCad near-miss code clone
detector to graphical models
validate vs. ConQAT
for types 1 & 2
hand validate type 3 (near-miss)
[Alalfi, Cordy,Dean, Stephan, Stevenson ICSM 2012]

25. Simulink
hybrid hardware/software models
widespread in industry
- automotive, aerospace, embedded systems
mature and interesting at GM

26. Simulink
hierarchical models
[Alalfi, Cordy,Dean, Stephan, Stevenson ICSM 2012]

27. Challenge #1
code methods
require text
NiCad requires
a parser
...!
System {!
Name
"onoff"!
Location
[168, 385, 668, 686]!
Open
on!
ModelBrowserVisibility off!
ModelBrowserWidth
200!
ScreenColor
"automatic"!
PaperOrientationi
"landscape"!
PaperPositionMode
"auto"!
PaperType
"usletter"!
PaperUnits
"inches"!
ZoomFactor
"100"!
AutoZoom
on!
ReportName
"simulink-default.rpt"!
Block {!
BlockType
DiscretePulseGenerator!
Name
"Discrete PulsenGenerator"!
Position
[45, 25, 75, 55]!
Amplitude
"1"!
Period
"2"!
PulseWidth
"1"!
PhaseDelay
"0"!
SampleTime
"1"!
}!
Block {!
BlockType
Product!
Name
"Product"!
Ports
[2, 1, 0, 0, 0]!
Position
[145, 67, 175, 98]!
Inputs
"2"!
SaturateOnIntegerOverflow on!
}!
...!
}!
...!
Solution:
grammar inference
on Simulink’s
internal form

28. Challenge #2
what granularity?
NiCad requires
candidates for
comparison
...!
System {!
Name
"onoff"!
Location
[168, 385, 668, 686]!
Open
on!
ModelBrowserVisibility off!
ModelBrowserWidth
200!
ScreenColor
"automatic"!
PaperOrientationi
"landscape"!
PaperPositionMode
"auto"!
PaperType
"usletter"!
PaperUnits
"inches"!
ZoomFactor
"100"!
AutoZoom
on!
ReportName
"simulink-default.rpt"!
Block {!
BlockType
DiscretePulseGenerator!
Name
"Discrete PulsenGenerator"!
Position
[45, 25, 75, 55]!
Amplitude
"1"!
Period
"2"!
PulseWidth
"1"!
PhaseDelay
"0"!
SampleTime
"1"!
}!
Block {!
BlockType
Product!
Name
"Product"!
Ports
[2, 1, 0, 0, 0]!
Position
[145, 67, 175, 98]!
Inputs
"2"!
SaturateOnIntegerOverflow on!
}!
...!
}!
...!
Simulink:
model (too big)
block (too small)
system (just right!)

29. powerwindow03/power_window_control_system/validate_driver
neutral
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powerwindow03/power_window_control_system/validate_driver
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even with raw text, find some subsystem clones
/Users/manaralalfi/Desktop/NiCad 3.2 Simulink/Simulink/Publicmodel/automotive/powerwindow03.mdl
printed 13 Apr 2012 14:56
/Users/manaralalfi/Desktop/NiCad 3.2 Simulink/Simulink/Publicmodel/automotive/powerwindow03.mdl
page 1/1 13 Apr 2012 14:56
printed
but:
90% irrelevant Simulink internal “formatting” systems
some identical systems only 70% same
entirely missed exact copies displayed differently
page 1/1

30. Challenge #3
problems with
“noise”
solution:
“agile parsing” to
filter out irrelevant
elements
...!
System {!
Name
"onoff"!
Location
[168, 385, 668, 686]!
Open
on!
ModelBrowserVisibility off!
ModelBrowserWidth
200!
ScreenColor
"automatic"!
PaperOrientationi
"landscape"!
PaperPositionMode
"auto"!
PaperType
"usletter"!
PaperUnits
"inches"!
ZoomFactor
"100"!
AutoZoom
on!
ReportName
"simulink-default.rpt"!
Block {!
BlockType
DiscretePulseGenerator!
Name
"Discrete PulsenGenerator"!
Position
[45, 25, 75, 55]!
Amplitude
"1"!
Period
"2"!
PulseWidth
"1"!
PhaseDelay
"0"!
SampleTime
"1"!
}!
Block {!
BlockType
Product!
Name
"Product"!
Ports
[2, 1, 0, 0, 0]!
Position
[145, 67, 175, 98]!
Inputs
"2"!
SaturateOnIntegerOverflow on!
}!
...!
}!
...!
[Dean, Cordy, Malton, Schneider JASE 2003]

31. filtering
...!
System {!
Name
Block {!
BlockType
Name
Amplitude
Period
PulseWidth
PhaseDelay
SampleTime
}!
Block {!
BlockType
Name
Ports
Inputs
}!
...!
}!
...!
removes more
than 300 kinds
of irrelevant
elements and blocks
increases
signal-to-noise
ratio in text
"onoff”!
DiscretePulseGenerator!
"Discrete PulsenGenerator”!
"1"!
"2"!
"1"!
"0"!
"1"!
Product!
"Product"!
[2, 1, 0, 0, 0]!
"2”!

32. filtering significantly improved performance
precision - 10x fewer false positives
hand validation of results
recall - many fewer false negatives
fewer missed clones
much larger clones
but:
some clones we could clearly see by hand
still not detected - why?

33. Challenge #4
no linear order of model elements

34. Challenge #4
solution: topological sort by block, line, port, branch

35. sorting
increases recall, to find many more clones
powerwindow03/power_window_control_system/validate_driver
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/Users/manaralalfi/Desktop/NiCad 3.2 Simulink/Simulink/Publicmodel/automotive/powerwindow03.mdl
printed 13 Apr 2012 14:56
page 1/1
page 1/1

36. sorting
increases recall, to find many more clones
powerwindow03/power_window_control_system/validate_driver
neutral
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up
powerwindow03/power_window_control_system/validate_driver
[reset]
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action
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mutually_exclusive
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printed 13 Apr 2012 14:56
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powerwindow03/power_window_control_system/validate_driver
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page 1/1
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/Users/manaralalfi/Desktop/NiCad 3.2 Simulink/Simulink/Publicmodel/automotive/powerwindow03.mdl
printed 13 Apr 2012 14:56
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printed 13 Apr 2012 14:56
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37. Challenge #5
finding type 2
(renamed) requires
anonymization
...!
System {!
Name
Block {!
BlockType
Name
Amplitude
Period
PulseWidth
PhaseDelay
SampleTime
}!
Block {!
BlockType
Name
Ports
Inputs
}!
...!
}!
...!
names in Simulink
not like other
languages
solution:
context-dependent
anonymizer
"onoff”!
DiscretePulseGenerator!
"Discrete PulsenGenerator”!
"1"!
"2"!
"1"!
"0"!
"1"!
Product!
"Product"!
[2, 1, 0, 0, 0]!
"2”!

38. validation - Simone vs. ConQAT
on Matlab Central public model systems
finds all type1(exact) and type 2 (renamed)
clones found by ConQAT
finds many new type 3 (near-miss)
clones not found by ConQAT
finds larger clones and larger clone classes
[Alalfi, Cordy,Dean, Stephan, Stevenson ICSM 2012]

39. Simone vs. ConQAT

40. Simone near-miss clones
in Simulink public automotive model variants
Total nontrivial
subsystems
357
Extractor only
Filtered
Filtered & Sorted
Filtered, Sorted &
Renamed
Clone Type
Type 1
Type 3-1
@30%
Type 1
Type 3-1
@30%
Type 1
Type 3-1
@30%
Type 2
Type 3-2
@30%
Clone Pairs
116 / 10*
364 / 164*
204
204
303
181
279
1938
Clone Classes
8 / 4*
57 / 56*
44
55
45
52
48
24
Clone Coverage
8% / 3%
52% / 46%
37%
48%
42%
45%
49%
75%
[Alalfi, Cordy,Dean, Stephan, Stevenson ICSM 2012]

41. Simone near-miss pattern mining
in Simulink models
!"#$%&%'()(*+&$%'(
,+-.".$%-$(/%-01"-2(
3#"-'(/%-01"-2(
*4.$%1.(
56$&07$%'(
,#+-%.(
,#0..%.(
,#+-%.(
,#0..%.(
,#+-%.(
,#0..%.(
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+,-&
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,/+&
,.&
-0,&
,1&
2345$3)6&7(48&
1/9:&
;0-&
.,&
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1.;0&
--&
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--0&
1+.&
-0&
;;;&
-0&
-,.&
-,&
89('+$)::(
099&
;11&
;.&
;90&
;0&
/::&
;:&
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42. MPE
near-miss clones
near-miss clone detection
Simone
analysis of GM models
pattern extraction
pattern evolution

43. Case study
GM fuel system models
SimGraph visualization
understanding Simulink model subsystem
similarity

44. GM Fuel System Models
Subsystem similarity overview
X models
(blue)
Small
subsystems
Y models
(green)
Large
subsystems
Z models
(red)
Midsize
subsystems

45. GM Fuel System Models
Subsystem similarity overview
Many subsystems unique not similar to any others in
these models

46. GM Fuel System Models
Remove unique subsystems
Similar subsystems
“near-miss clones”
both within and
between models
Connecting lines represent subsystem similarity thick lines, 90-100% similar
thin lines, 70-80% similar

47. GM Fuel System Models
Rearrange to cluster similar subsystems
Clusters reveal groups of
similar subsystems “clone classes”

48. GM Fuel System Models
Infer common subsystem patterns
Patterns characterize
common repeated similar
subsystem paradigms
Small groups of
relatively large
similar subsystems
both within and
across models
Large groups of small
to mid-sized similar
subsystems across
models

49. MPE
near-miss clones
near-miss clone detection
Simone
analysis of GM models
pattern extraction
pattern evolution

50. using patterns
SimNav
exemplar sets as patterns
SimPat
modeling variance

51. SimNav
presenting and integrating results in Simulink
exemplar sets as patterns

52. SimNav
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54. SimPat
characterizing and representing subsystem
patterns
modeling variance

55. SimPat

56. !901*7.*9+17.*/66A7
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57. !901*7.*9+17.*/66A7
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58. !901*7.*9+17.*/66A7
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59. %&$
near-miss clones
&($
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MPE
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near-miss clone detection
Simone
analysis of GM models
pattern extraction
pattern evolution
&)$
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60. model pattern evolution
SimCCT
evolution of patterns
across versions
pattern variance in two dimensions
instance, time
[Stephan, Alalfi, Cordy, Stevenson ME 2013]

61. model pattern
MCC - model clone class
elements of a model pattern
MCI - model clone instance
evolution of patterns
migration of MCIs between MCCs across
versions of the system

62. evolution of patterns
1-1
pattern is stable across versions
1-1*
pattern exists, but loses or gains MCIs
1-many
pattern splits into multiple patterns
1-many*
pattern splits, losing or gaining MCIs
1-0
pattern unifies or disappears

63. SimCCT

64. SimCCT - Power Window MCC 3
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65. SimCCT - Power Window MCC 2
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66. SimCCT - AVS MCC 7
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67. MPE
near-miss clones
near-miss clone detection
Simone
analysis of GM models
pattern extraction
pattern evolution

68. current work
Stateflow models
deployment at GM
analysis of more systems

69. Thank you!
Joseph d’Ambrosio
Cheryl Williams
Manar H. Alalfi
Thomas R. Dean
Matthew Stephan
Andrew Stevenson

70. Submodel
Pattern Extraction
for Simulink Models
James R. Cordy
Queen’s University
NECSIS Automotive Partnership Canada