Localizing design problems in a code city, by mapping the results of detection strategies. Presented at Softvis 2008 (Herrsching, Germany), September 2008.

1. Visually Localizing Design Problems with
Disharmony Maps
Richard Wettel and Michele Lanza
REVEAL @ Faculty of Informatics,
University of Lugano, Switzerland

2. Overview
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 2

3. Overview
Software
design
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 2

4. Overview
Software Code
design cities
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 2

5. Overview
Software Code Disharmony
design cities maps
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 2

6. Overview
Software Code Disharmony Tool
design cities maps support
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 2

7. On software design

8. Research around software design
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 4

9. Research around software design
Concepts
heuristics [Riel 2000], principles [Martin 2002], patterns [Gamma et al.
1995], design harmony [Lanza & Marinescu 2006]
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 4

10. Research around software design
Concepts
heuristics [Riel 2000], principles [Martin 2002], patterns [Gamma et al.
1995], design harmony [Lanza & Marinescu 2006]
bad smells [Fowler et al. 1998], design flaws [Marinescu 2002]
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 4

11. Research around software design
Concepts
heuristics [Riel 2000], principles [Martin 2002], patterns [Gamma et al.
1995], design harmony [Lanza & Marinescu 2006]
bad smells [Fowler et al. 1998], design flaws [Marinescu 2002]
Techniques
detection strategies [Marinescu 2002, Lanza & Marinescu 2006]
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 4

12. Research around software design
Concepts
heuristics [Riel 2000], principles [Martin 2002], patterns [Gamma et al.
1995], design harmony [Lanza & Marinescu 2006]
bad smells [Fowler et al. 1998], design flaws [Marinescu 2002]
Techniques
detection strategies [Marinescu 2002, Lanza & Marinescu 2006]
Visualizations
quality [Panas et al. 2005], principle violations [Langelier et al. 2005]
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 4

13. Design disharmony
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 5

14. Design disharmony God Class
Brain Class
1.Identity
Data Class
How do I
define Brain Method
myself?
Feature Envy
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 5

15. Intensive Coupling
Design disharmony God Class
Dispersive Coupling
Brain Class
1.Identity Shotgun Surgery
Data Class
How do I
2.Collaboration interact with Brain Method
others?
Feature Envy
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 5

16. Intensive Coupling
Design disharmony God Class
Dispersive Coupling
Brain Class
1.Identity Shotgun Surgery
How do I define Data Class
2.Collaboration myself with respect Futile Hierarchy
to my ancestors andBrain Method
descendants? Tradition Breaker
3.Classification Feature Envy
Refused Parent Bequest
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 5

17. Intensive Coupling
Design disharmony God Class
Dispersive Coupling
Brain Class
Shotgun Surgery
Data Class
Futile Hierarchy
Brain Method
Tradition Breaker
Feature Envy
Refused Parent Bequest
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 5

18. Definition of God Class
“In a good object-oriented design
the intelligence of a system is
uniformly distributed among the
top-level classes.”
[Riel 1996]
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19. Characteristics of a God Class
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 7

20. Characteristics of a God Class
Heavily accesses data
of other “lightweight”
classes,either
directly or using
accessor
methods.
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 7

21. Characteristics of a God Class
Heavily accesses data
of other “lightweight”
classes,either
directly or using Is large
accessor
methods.
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 7

22. Characteristics of a God Class
Heavily accesses data
of other “lightweight”
classes,either
directly or using Is large
accessor
methods.
Has a lot of
non-communicative
behavior
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 7

23. Characteristics of a God Class
Heavily accesses data
of other “lightweight”
classes,either
directly or using Is large
accessor
methods. God
Class
Has a lot of
non-communicative
behavior
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 7

24. The God Class detection strategy
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 8

25. Software systems as cities

26. The city metaphor
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27. The city metaphor
domain mapping
class building
package district
system city
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 10

28. The city metaphor
domain mapping
class building
package district
system city
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 10

29. The city metaphor
domain mapping
class building
package district
system city
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 10

30. The city metaphor
domain mapping
class building
package district
system city
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 10

31. The city metaphor
domain mapping
class building
package district
system city
number of methods (NOM) height
number of attributes (NOA) base size
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 10

32. The city metaphor
domain mapping
class building
package district
system city
nesting level color
number of methods (NOM) height
number of attributes (NOA) base size
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 10

33. The city metaphor
domain mapping
class building
package district
system city
nesting level color
number of methods (NOM) height
[Wettel & Lanza, ICPC 2007]
[Wettel & Lanza, VISSOFT 2007] number of attributes (NOA) base size
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 10

34. “Reading” a code city (ArgoUML)
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 11

35. “Reading” a code city (ArgoUML)
skyscrapers
(NOM, NOA)
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 11

36. “Reading” a code city (ArgoUML)
skyscrapers
(NOM, NOA)
parking lots
(NOM, NOA)
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 11

37. “Reading” a code city (ArgoUML)
skyscrapers
(NOM, NOA)
office buildings
parking lots
(NOM, NOA)
(NOM, NOA)
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 11

38. Cities of the Java case studies
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 12

39. Cities of the Java case studies
System NOP NOC LOC
ArgoUML 144 2,542 137,000
JHotDraw 72 998 30,000
iText 149 1,250 80,000
Jmol 105 1,032 85,000
JDK 1.5 137 4,715 160,000
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 12

40. Cities of the Java case studies
System NOP NOC LOC
ArgoUML 144 2,542 137,000
JHotDraw 72 998 30,000
iText 149 1,250 80,000
Jmol 105 1,032 85,000
JDK 1.5 137 4,715 160,000
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 12

41. Cities of the Java case studies
System NOP NOC LOC
ArgoUML 144 2,542 137,000
JHotDraw 72 998 30,000
iText 149 1,250 80,000
Jmol 105 1,032 85,000
JDK 1.5 137 4,715 160,000
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 12

42. Cities of the Java case studies
System NOP NOC LOC
ArgoUML 144 2,542 137,000
JHotDraw 72 998 30,000
iText 149 1,250 80,000
Jmol 105 1,032 85,000
JDK 1.5 137 4,715 160,000
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 12

43. Cities of the Java case studies
System NOP NOC LOC
ArgoUML 144 2,542 137,000
JHotDraw 72 998 30,000
iText 149 1,250 80,000
Jmol 105 1,032 85,000
JDK 1.5 137 4,715 160,000
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 12

44. Cities of the Java case studies
System NOP NOC LOC
ArgoUML 144 2,542 137,000
JHotDraw 72 998 30,000
iText 149 1,250 80,000
Jmol 105 1,032 85,000
JDK 1.5 137 4,715 160,000
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 12

45. Disharmony maps

46. Inspired by health maps
World distribution of Myxobolus cerebralis
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47. Disharmony map: focus+context
God classes
of JDK 1.5
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 15

48. JDK’s disharmony map
12 Brain classes
16
65 God classes
109 Data classes
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 16

49. JDK’s disharmony map
Component
NOA 88, NOM 280
12 Brain classes
16
65 God classes
109 Data classes
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 16

50. JDK’s disharmony map
Security
NOA 3, NOM 30
12 Brain classes
16
65 God classes
109 Data classes
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 16

51. JDK’s disharmony map
12 Brain classes
16
65 God classes
109 Data classes
java.awt.event
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 16

52. JDK’s disharmony map
12 Brain classes
16
65 God classes KeyEvent
NOA 205, NOM 18
109 Data classes
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 16

53. JDK’s disharmony map
InputEvent
12 Brain classes NOA 21, NOM 14
16
65 God classes
109 Data classes
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 16

54. JDK’s disharmony map
java.awt.geom
12 Brain classes
16
65 God classes
109 Data classes
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 16

55. ArgoUML’s disharmony map
8 Brain classes
9
24 God classes
17 Data classes
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 17

56. ArgoUML’s disharmony map
8 Brain classes FacadeMDRImpl Facade
9 NOA 3, NOM 349 NOA 1, NOM 337
24 God classes
17 Data classes
GeneratorPHP4
NOA 4, NOM 33
GeneratorCPP
NOA 34, NOM 100 JavaRecognizer
NOA 79, NOM 176
CPPParser
NOA 85, NOM 204 Modeller
NOA 15, NOM 52
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57. Granularity of representation
class C
NOM = 7
2
A=
NOA
NO
=2
coarse
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58. Granularity of representation
class C
NOM = 7
2
A=
NOA
NO
=2
coarse fine-grained
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 18

59. Granularity of representation
class C
NOM = 7
las sC
c
2
A=
NOA
NO
=2
coarse fine-grained
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60. Granularity of representation
class C
m6
NOM = 7
m5 m7
m2
m1
m3
m4
las sC
c
2
A=
NOA
NO
=2
coarse fine-grained
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 18

61. Feature envy map of Jmol
Feature envy
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 19

62. Feature envy map of Jmol
Feature envy
1,500 methods (25 %)
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 19

63. Shotgun surgery map of ArgoUML
Facade AggregationKind VisibilityKind
NOM 140/337 NOM 3/3 NOM 4/4
PseudostateKind
NOM 6/6
Model
NOM 28/44
Shotgun surgery
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64. Tool support

65. The toolchain
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 22

66. The toolchain
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 22

67. The toolchain
Moose
parsing
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 22

68. The toolchain
iPlasma
parsing
Moose
parsing
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 22

69. The toolchain
model
iPlasma
exchange
parsing
Moose
parsing
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 22

70. The toolchain
model
iPlasma
exchange
parsing
Moose
parsing
OpenGL rendering
CodeCity
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71. Demo time!
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 23

72. Closing remarks
Design disharmonies overview actual design
problems in context.
www.inf.unisi.ch/phd/wettel/codecity.html
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 24

73. Closing remarks
+ =
Design disharmonies overview actual design
problems in context.
www.inf.unisi.ch/phd/wettel/codecity.html
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 24

74. Closing remarks
+ =
Design disharmonies overview actual design
problems in context.
Acknowledgments:
www.inf.unisi.ch/phd/wettel/codecity.html
Richard Wettel and Michele Lanza Visually Localizing Design Problems with Disharmony Maps 24

75. Richard Wettel
http://www.inf.unisi.ch/phd/wettel
http://creativecommons.org/licenses/by/3.0/