I used this set of slides as support for the Software Visualization lecture I gave at the University of Berne on 23 Oct 2008.

1. Software visualization
Tudor Gîrba
www.tudorgirba.com

2. Software Visualization is the use of
typography, graphic design, animation, and
cinematography with human-computer
interaction and computer graphics to facilitate
both the human understanding and effective
use of software.
3
Price etal. 199

3. Why visualization?

4. A picture is worth a thousand words.
Ano nymous

5. 1854,
London,
cholera
epidemic

6. 1854,
London,
cholera
epidemic

7. 1946

8. A picture is worth a thousand words.
Ano nymous

9. We are vis ual beings

10. How many groups do you see?

11. How many groups do you see?

12. How many groups do you see?

13. How many groups do you see?

14. lt prin ciples
Gesta
proximity similarity
enclosure connectivity

15. lt principles
More Gesta
closure continuity

17. Iconic
memory
< 1 second
very fast
automatic
subconscious
preattentive

18. Iconic Short-term
memory memory
< 1 second couple of seconds
very fast 3-9 chunks
automatic
subconscious
preattentive

19. Orientation Line Length Line Width Size
Shape Curvature Added Marks Enclosure

20. Line Length Line Width Size
Shape Curvature Added Marks Enclosure

21. Line Width Size
Shape Curvature Added Marks Enclosure

22. Size
Shape Curvature Added Marks Enclosure

23. Shape Curvature Added Marks Enclosure

24. Curvature Added Marks Enclosure

25. Added Marks Enclosure

26. Enclosure

28. How many times does 5 appear?
8789364082376403128764532984732984732094873290845
389274-0329874-32874-23198475098340983409832409832
049823-0984903281453209481-0839393947896587436598

29. How many times does 5 appear?
8789364082376403128764532984732984732094873290845
389274-0329874-32874-23198475098340983409832409832
049823-0984903281453209481-0839393947896587436598

30. ternal input
7 0% o f all ex
ough t he eye
com es thr

31. A picture is worth a thousand words.
Ano nymous

33. it lite rally :)
UM L took

34. Example: what is ?

40. What to visualize?
How to visualize?

41. What to visualize?

42. What to visualize?
Software structure

45. System Complexity shows class hierarchies
Ducas se 2003
Lanza,

46. Polymetric views show up to 5 metrics
Width metric
Height metric
Position metrics
Color
metric
Lan za 2003

47. 003
, Du casse 2
Lanza

48. Distribution Map shows properties over structure
6
casse etal 200
Du

49. Package Map shows who owns what classes
7
Gre evy 200

50. 992
Shneid erman 1

51. What to visualize?
Software structure
Software relationships

52. Class Blueprint shows class internals
Initialize Interface Internal Accessor Attribute
invocation and access direction
se, Lan za 2005
Ducas

53. Class Blueprint has a rich vocabulary
internal access
Access
external
Attribute
access Invocation
Regular Constant
invocations
Overriding Delegating
lines Method
Extending Setter
Abstract Getter
se, Lan za 2005
Ducas

54. se, Lan za 2005
Ducas

55. 06
Holten 20

56. Hierarchical edge bundles clarify dependencies
6
Hol ten 200

57. 08
Holten 20

58. Correlation Matrix reveals correlations
user, run, load, message, file, buffer, util
property, AWT, edit, show, update, sp, set
start, buffer, end, text, length, line, count
action, box, component, event, button, layout, GUI
start, length, integer, end, number, pre, count
XML, dispatch, microstar, reader, XE, register, receive
current, buffer, idx, review, archive, endr, TAR
BSH, simple, invocation, assign, untype, general, arbitrary
maximum, label, link, item, code, put, vector
Kuhn e tal 2006

59. Arc diagrams show duplications
00 2
Watte nberg 2

60. What to visualize?
Software structure
Software relationships
Metaphors

61. Software Map reveals software geography
8
Kuhn etal 200

62. CodeCity reveals where software lives
7
We ttel 200

63. 0 8
Wettel 20

64. What to visualize?
Software structure
Software relationships
Metaphors
Interaction

65. Softwarenaut discovers architecture
tal 2006
Lungu e

66. What to visualize?
How to visualize?

67. nderst anding
t guara ntee u
ation does no
V isualiz

69. Minimize non-data ink
Tuf te, 1990

70. Excel 2004
default

71. Excel 2004
default
Excel 2004
default
+
6 actions

72. 6
casse etal 200
Du

73. Maximize data ink
Tuf te, 1990

76. InfoBug is cute and condensed
00 6
Chuah , Eick, 2

77. s a la ngu age
provide
visual ization
Each learnt
s to be
th at need

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

79. A small experiment

80. 1, 13, 27, 4, 96

81. What were the numbers?

82. Easy!

83. What’s the last advertisement you saw?

84. Not so easy!

85. s a la ngu age
provide
visual ization
Each learnt
s to be
th at need

86. tion is a
rt, too
V isualiza

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