Understanding software systems is hampered by their sheer size and complexity. Software visualization encodes the data found in these systems into pictures and enables the human eye to interpret it. In this lecture we present the concepts of software visualization and we show several examples of how visualizations can help in understanding software systems.

1. Software in pictures
Tudor Gîrba
www.tudorgirba.com

2. Software is complex.
29% Succeeded
18% Failed
53% Challenged
The Standish Group, 2004

3. How large is your project?

4. How large is your project?
1’000’000 lines of code

5. How large is your project?
1’000’000 lines of code
* 2 = 2’000’000 seconds

6. How large is your project?
1’000’000 lines of code
* 2 = 2’000’000 seconds
/ 3600 = 560 hours

7. How large is your project?
1’000’000 lines of code
* 2 = 2’000’000 seconds
/ 3600 = 560 hours
/ 8 = 70 days

8. How large is your project?
1’000’000 lines of code
* 2 = 2’000’000 seconds
/ 3600 = 560 hours
/ 8 = 70 days
/ 20 = 3 months

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11. McCabe = 21
NOM 0
= 102
3 ,00
75
=
C
LO
Metrics Queries Visualizations ...
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12. McCabe = 21
NOM 0
= 102
3 ,00
75
=
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LO
Metrics Queries Visualizations ...
{ {
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13. What are software metrics?

14. Youcannot control
what you cannot measure.
Tom de Marco

15. Software metrics are measurements which
relate to software systems, processes or
related documents

16. Metrics compress the system into numbers.

17. Examples of size metrics
NOM - number of methods
NOA - number of attributes
LOC - number of lines of code
NOS - number of statements
NOC - number of children
Lorentz, Kidd, 1994
Chidamber, 1994

18. McCabe cyclomatic complexity counts
the number of possible paths through the code of a
function.
McCabe, 1977

19. McCabe = 21
NOM 0
= 102
3 ,00
75
=
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LO
Metrics Queries Visualizations ...
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20. McCabe = 21
NOM 0
= 102
3 ,00
75
=
C
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Metrics Queries Visualizations ...
{ {
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21. Queries reduce the analysis space.

22. Detection Strategies are metric-based queries to
detect design flaws. Lanza, Marinescu 2006
Rule 1
METRIC 1 > Threshold 1
AND Quality problem
Rule 2
METRIC 2 < Threshold 2

23. Example: a God Class centralizes too much
intelligence in the system. Lanza, Marinescu 2006
Class uses directly more than a
few attributes of other classes
ATFD > FEW
Functional complexity of the
class is very high
AND GodClass
WMC ! VERY HIGH
Class cohesion is low
TCC < ONE THIRD

24. McCabe = 21
NOM 0
= 102
3 ,00
75
=
C
LO
Metrics Queries Visualizations ...
{ {
{ {
}
}
}
} { }

25. McCabe = 21
NOM 0
= 102
3 ,00
75
=
C
LO
Metrics Queries Visualizations ...
{ {
{ {
}
}
}
} { }

26. Software in pictures
Tudor Gîrba
www.tudorgirba.com

27. 1946

28. 1956

29. 1956

30. 1956

31. 1956 2006

32. 1956 2006

33. 1956 2006

34. 1956 2006
?

35. Visualization compresses the system into pictures.

36. 1854,
London,
cholera
epidemic

37. 1854,
London,
cholera
epidemic

38. A picture is worth
a thousand words.

40. UML took it literally :)

41. We are
visual
beings.

42. How many groups do you see?

43. How many groups do you see?

44. How many groups do you see?

45. How many groups do you see?

46. Gestalt principles
proximity similarity
enclosure connectivity

47. More Gestalt principles
closure continuity

48. We are
visual
beings.

49. A picture is worth
a thousand words.

51. UML took it literally :)

52. Example: what is ?

58. Polymetric views show up to 5 metrics.
Lanza, 2003
Width metric
Height metric
Position metrics
Color
metric

59. System Complexity shows class hierarchies.
Lanza, Ducasse, 2003
attributes
methods lines

60. Class Blueprint shows class internals.
Lanza, Ducasse, 2005
Initialize Interface Internal Accessor Attribute
invocation and access direction

61. Class Blueprint has a rich vocabulary.
internal access
Access
external
Attribute
access Invocation
Regular Constant
invocations
Overriding Delegating
lines Method
Extending Setter
Abstract Getter

62. Class Blueprint shows class internals.

63. Distribution Map shows properties over
structure. Ducasse etal, 2006
31 parts, 394 elements and 9 properties

64. Code City shows where your code lives.
Wettel, Lanza, 2007
classes are buildings grouped in quarters of packages

65. Object Flow shows how objects move.
Lienhard etal, 2007
objects flowing
through other objects

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67. {
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reverse engineering
fo
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68. Spectographs show change activity.
Wu etal, 2004
commit
time

69. Evolution Matrix shows changes in classes.
Lanza, Ducasse, 2002
Idle class
Pulsar class
Supernova class
White dwarf class

70. Evolution Matrix shows changes in classes

71. What happens with inheritance?
A A A A A
B C B C B C B B
D D D E
ver .1 ver. 2 ver. 3 ver. 4 ver. 5

72. History contains too much data.
A A A A A A A A A A A A A A A A A A A A
B C B C B C B B B C B C B C B B B C B C B C B B B C B C B C B B
D D D E D D D E D D D E D D D E
ver .1 ver. 2 ver. 3 ver. 4 ver. 5 ver .1 ver. 2 ver. 3 ver. 4 ver. 5 ver .1 ver. 2 ver. 3 ver. 4 ver. 5 ver .1 ver. 2 ver. 3 ver. 4 ver. 5
A A A A A A A A A A A A A A A A A A A A
B C B C B C B B B C B C B C B B B C B C B C B B B C B C B C B B
D D D E D D D E D D D E D D D E
ver .1 ver. 2 ver. 3 ver. 4 ver. 5 ver .1 ver. 2 ver. 3 ver. 4 ver. 5 ver .1 ver. 2 ver. 3 ver. 4 ver. 5 ver .1 ver. 2 ver. 3 ver. 4 ver. 5
A A A A A A A A A A A A A A A A A A A A
B C B C B C B B B C B C B C B B B C B C B C B B B C B C B C B B
D D D E D D D E D D D E D D D E
ver .1 ver. 2 ver. 3 ver. 4 ver. 5 ver .1 ver. 2 ver. 3 ver. 4 ver. 5 ver .1 ver. 2 ver. 3 ver. 4 ver. 5 ver .1 ver. 2 ver. 3 ver. 4 ver. 5
A A A A A A A A A A A A A A A A A A A A
B C B C B C B B B C B C B C B B B C B C B C B B B C B C B C B B
D D D E D D D E D D D E D D D E
ver .1 ver. 2 ver. 3 ver. 4 ver. 5 ver .1 ver. 2 ver. 3 ver. 4 ver. 5 ver .1 ver. 2 ver. 3 ver. 4 ver. 5 ver .1 ver. 2 ver. 3 ver. 4 ver. 5

73. A A A A A
B C B C B C B B
D D D E
ver .1 ver. 2 ver. 3 ver. 4 ver. 5
A is persistent, B is stable, C was removed, E is newborn ...

74. Hierarchy Evolution encapsulates time.
Girba etal, 2005
A
changed
methods
changed
age
lines
C B
Removed
Removed
D E
A is persistent, B is stable, C was removed, E is newborn ...

75. Hierarchy Evolution reveals patterns.
Girba etal, 2005

76. Evolution Radar shows co-change relationships.
D’Ambros, Lanza 2006
one package and its co-change relationships

77. McCabe = 21
NOM 0
= 102
3 ,00
75
=
C
LO
Metrics Queries Visualizations ...
{ {
{ {
}
}
}
} { }

78. McCabe = 21
NOM 0
= 102
3 ,00
75
=
C
LO
Metrics Queries Visualizations ...
{ {
{ {
}
}
}
} { }

79. moose.unibe.ch

80. McCabe = 21
NOM 0
= 102
3 ,00
75
=
C
LO
Metrics Queries Visualizations ...
{ {
{ {
}
}
}
} { }

81. But, what actually happens
behind the scene?

82. System Complexity
1 node type
1 edge type
3 metrics
Class Blueprint
3 node types
2 edge types
3 metrics
8 properties

83. A small experiment

84. 1, 13, 27, 4, 96

85. What were the numbers?

86. Easy!

87. What was the last advertisement you saw?

88. Not so easy!

89. Each visualization provides a language that
needs to be learnt.

90. So, what does happen
behind the scene?

91. Principle: Minimize chart junk
Tufte, 1990

92. Excel default

93. Excel default
Excel default
+
6 actions

94. Distribution Map shows properties.
Ducasse etal, 2006

95. Principle: Maximize data ink
Tufte, 1990
Tufte, 1990

96. InfoBug is cute and condensed.
Chuah, Eick, 2006

97. So, what does happen
behind the scene?

98. Example 1: CVS shows activity.

99. Who did this?

100. Alphabetical order is no order.

101. Ownership Map orders histories to reveal
patterns. Girba etal, 2006

102. Example 2: Who copied from whom?
(john 23.06.03) public boolean stillValid (ToDoItem I, Designer dsgr) {
(bill 09.01.05) if (!isActive()) {
(bill 09.01.05) return false
(bill 09.01.05) }
(steve 16.02.05) List offs = i.getOffenders();
(john 23.06.03) Object dm = offs.firstElement();
(steve 16.02.05) ListSet newOffs = computeOffenders(dm);
(john 23.06.03) boolean res = offs.equals(newOffs);
(john 23.06.03) return res;
(george 13.02.05) public boolean stillValid (ToDoItem I, Designer dsgr) {
(bill 11.13.05) if (!isActive()) {
(bill 11.13.05) return false
(bill 11.13.05) }
(steve 16.02.05) List offs = i.getOffenders();
(george 13.02.05) Object dm = offs.firstElement();
(steve 16.02.05) ListSet newOffs = computeOffenders(dm);
(george 13.02.05) boolean res = offs.equals(newOffs);
(george 13.02.05) return res;

103. What is useless?
(john 23.06.03) public boolean stillValid (ToDoItem I, Designer dsgr) {
(bill 09.01.05) if (!isActive()) {
(bill 09.01.05) return false
(bill 09.01.05) }
(steve 16.02.05) List offs = i.getOffenders();
(john 23.06.03) Object dm = offs.firstElement();
(steve 16.02.05) ListSet newOffs = computeOffenders(dm);
(john 23.06.03) boolean res = offs.equals(newOffs);
(john 23.06.03) return res;
(george 13.02.05) public boolean stillValid (ToDoItem I, Designer dsgr) {
(bill 11.13.05) if (!isActive()) {
(bill 11.13.05) return false
(bill 11.13.05) }
(steve 16.02.05) List offs = i.getOffenders();
(george 13.02.05) Object dm = offs.firstElement();
(steve 16.02.05) ListSet newOffs = computeOffenders(dm);
(george 13.02.05) boolean res = offs.equals(newOffs);
(george 13.02.05) return res;

104. When did changes happen?
23.06.03 public boolean stillValid (ToDoItem I, Designer dsgr) {
09.01.05 if (!isActive()) {
09.01.05 return false
09.01.05 }
16.02.05 List offs = i.getOffenders();
23.06.03 Object dm = offs.firstElement();
16.02.05 ListSet newOffs = computeOffenders(dm);
23.06.03 boolean res = offs.equals(newOffs);
23.06.03 return res;
13.02.05 public boolean stillValid (ToDoItem I, Designer dsgr) {
11.13.05 if (!isActive()) {
11.13.05 return false
11.13.05 }
16.02.05 List offs = i.getOffenders();
13.02.05 Object dm = offs.firstElement();
16.02.05 ListSet newOffs = computeOffenders(dm);
13.02.05 boolean res = offs.equals(newOffs);
13.02.05 return res;

105. Clone Evolution shows how developers copy.
Balint etal, 2006

106. Not every picture is worth a thousand words.

107. One more thing ...

108. Visualization is art, too.

111. Tudor Gîrba
www.tudorgirba.com
creativecommons.org/licenses/by/3.0/