1. Software Evolution Visualization
Status, Challenges, and Possible Research Directions
Prof. Manoel Mendonça
http://fpc.dcc.ufba.br http://les.dcc.ufba.br
+55 (71) 3283-6311 manoel.mendonca@ufba.br

2. Outline
 Introduction
 Information and Software Visualization
 Software Evolution Visualization (SEV)
 A Critical View of the Area
 SEV@UFBA
 Conclusion
2

3. Outline
 Introduction
 Information and Software Visualization
 Software Evolution Visualization (SEV)
 A Critical View of the Area
 SEV@UFBA
 Conclusion
3

4. Software Visualization (SoftVis)
 It is the use of visual paradigms (visual resources
– animation and graphical design) to facilitate the
comprehension and effective use of software.
 Price, Baecker and Small, “An Introduction to Software
Visualization”
4

5. Software is Abstract
 Software is inherently:
 “invisible”
 intangible
 has no physical format
5

6. Representing Software
 Software can be represented in many ways
 The most common way is the textual format, especially
its source code
6

7. Source Code at a Low Level of Abstraction
Address Label Source Code Object Code
.begin
.org 2048
a_start .equ 3000
2048 ld length,%
2064 be done 00000010 10000000 00000000 00000110
2068 addcc %r1,-4,%r1 10000010 10000000 01111111 11111100
2072 addcc %r1,%r2,%r4 10001000 10000000 01000000 00000010
2076 ld %r4,%r5 11001010 00000001 00000000 00000000
2080 ba loop 00010000 10111111 11111111 11111011
2084 addcc %r3,%r5,%r3 10000110 10000000 11000000 00000101
2088 done: jmpl %r15+4,%r0 10000001 11000011 11100000 00000100
2092 length: 20 00000000 00000000 00000000 00010100
2096 address: a_start 00000000 00000000 00001011 10111000
.org a_start
3000 a:
Murdocca, Miles J.; Vincent P. Heuring (2000). Principles of Computer Architecture.
Prentice-Hall. ISBN 0-201-43664-7.
7

8. Representing Software
 Can visualization techniques be used to represent
software?
 Yes! It is already being used in some way or another
since the 70’s
8

9. Visualization in a Modern IDEs
pretty printing
hierarchical
structure
iconographic
representation
syntax based
coloring
9

10. Is this Enough?
 Not Quite
 Size and Complexity have sky rocked
 We lack mechanisms to visualize and explore software
in the large
 Information Visualization techniques and paradigms can
be used to deal with this issue
10

11. Outline
 Introduction
 Information and Software Visualization
 Software Evolution Visualization (SEV)
 A Critical View of the Area
 SEV@UFBA
 Conclusion
14

12. SoftVis is InfoVis
15

13. Information Visualization (InfoVis)
“InfoVis is the study of (interactive) visual representations
of abstract data to reinforce human cognition.”
.
16

14. Visualization is about Perception
 Taking the input from our senses (in this case Vision)
and turning it into something that has meaning for us
 We “see” (perceive) with our brains!
19

15. A Data Pattern in Tabular Format
Y 0 0.259 0.5 0.707 0.866 0.966 1 0.966 0.866 0.707
X 12 13 14 15 16 17 18 19 20 21
Y 0.5 0.259 0 -0.259 -0.5 -0.707 -0.866 -0.966 -1 -0.966
X 22 23 24 25 26 27 28 29 30 31
Y -0.866 -0.707 -0.5 -0.259 0 .0259 0.5 0.707 0.866 0.966
X 32 33 34 35 36 37 38 39 40 41
Requires Perception and Reasoning
20

16. Same Pattern in Visual Format
1
0,5
0
-0,5
-1
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
Requires only Perception
21

17. InfoVis requires Interaction
 Computer-based InfoVis requires human computer
interaction.
 There is no InfoVis without good interaction
mechanisms.
26

18. InfoVis Interaction Mantra
Gain a global view, zoom and filter, and then
detail on demand
SHNEIDERMAN, B. The eyes have it: A task by data type taxonomy for
information visualization.
In VL’96: Proceedings of the 1996 IEEE Symposium on Visual
Languages (1996), IEEE CS Press, pp. 336–343.
27

19. Revisiting our SoftVis Definition
SoftVis is the effective mapping of software entities
and its attributes into graphical structures that are
easy explore and understand by software engineers …
to facilitate the comprehension and effective use of
software.
28

20. Effective Mapping (1)
 Good visual metaphors
 graphical structure matches the nature of the information
 use perception efficient representations and visual attributes
 Good views (instantiations of metaphors)
 intuitive mapping of real attributes into visual attributes
29

21. Effective Mapping (2)
 Due to its complexity software cannot be described by
just one view
Multiple views are need to assemble an analysis
perspective
 These views must be consistent and integrated among
themselves and with the rest of the IDE
30

22. Effective Mapping in Modern IDEs
Multiple views assemble
an analysis perspective
Multiple visual
Metaphors
Syntax-based
coloring
Visual attribute
=> Real attribute
All is integrated !
31

23. Easy to Explore and Undertsnad
 The goal is to facilitate the comprehension of the software
(the graphics is a means to an end).
 We need good interaction mechanisms
 Easy to use widgets and interaction controls
 We need a comprehensive data exploration functionality
to support Shneiderman’s mantra
32

24. There is a lot to be done here
33

25. Easy to Explore and Undertsnad
 Good interaction mechanisms
 Easy to use widgets and interaction controls
Comprehensive data exploration functionality to support
Shneiderman’s mantra
 Global views, conceptual zoom, attribute based
brushing and filtering, dynamic mapping between real
and visual attributes, navigation and consistent marking,
and details on demand.
 Integration of the views with these functionalities.
34

26. SourceMiner
(www.sourceminer.org)
35

27. (A) (E)
(C) (D)
(B)
36

28. There is much still to be visualized
in the large
37

29. SoftVis Areas
 Static Software Visualization (no need to run the code)
 Static representations of control and data flows
 Visualization of modules (structures and dependencies)
 Static aspects of architectures
 Dynamic Software Visualization (running the code)
 Animated Algorithms
 Test coverage and debugging
 Memory usage and code traces
 Dynamic aspects of software architectures
38

30. SoftVis Areas
 Visualizing data about the software
 Contributions, authorship and usage of modules and components
 Bug and issues information
 Discussions, opinions, and people roles
 Software Evolution Visualization
 Changes in structure
 Dependency evolution
 Change in metrics
 Evolution of data about the software (all of the above)
39

31. SoftVis Areas
 Visualizing data about the software
 Contributions, authorship and usage of modules and components
 Bug and issues information
 Discussions, opinions, and people roles
 Software Evolution Visualization
 Changes in structure
 Dependency evolution
 Change in metrics
 Evolution of data about the software (all of the above)
40

32. Outline
 Introduction
 Information and Software Visualization
 Software Evolution Visualization (SEV)
 A Critical View of the Area
 SEV@UFBA
 Conclusion
41

33. Software systems must be continually adapted or they become
progressively less satisfactory (LEHMAN, 1978)(LEHMAN, 1980)
42

34. Evolution (Lehman, 78)
Complexity Comprehension
(Jay et al.,
2009)
Costs (Erlikh, 2000)
43

35. For some systems, the cost devoted to evolution and
maintenance now accounts for more than 90% of total
software costs (ERLIKH, 2000)
Software maintainers spend approximately 50%
of their time in the process of understanding
the code (FJELDSTA e HAMLEN, 1983)
44

36. So, what can we do to facilitate software
comprehension “in the large”?
45

37. Software evolution visualization
Software evolution analysis Software visualization
requires can summarize the data
lots of data in meaningful contexts
46

38. Software evolution visualization
 SEV is around since the 90’s
 There is a sizeable body of work in the literature
47

39. SEV of What?
 Behavior  Mail List
 Code Clones  Metrics
 Data structures  Requirements
 Defects  Software Architecture
 Developer Activities  Source code changes
 Developer Dependency  Comment changes
 Exception handling  Source code dependency
 Ecosystem change  System dependency
 Features  Topics
48

40. SEV for What?
 Change Comprehension  Quality improvement
 Change Control Enhancement  Process Improvement
 Change Prediction  Powerful and scalable
 Contribution Analysis visualization
 Defect Classification and  Re-documentation
Analysis  Reverse engineering
 Development communication  Software reuse
 Identification of anomalies
 Dependency among projects
49

41. Strategies of Analysis
Defines the strategy for the visual presentation of
software artifacts.
 Temporal Overview
 Temporal Snapshot
 Temporal Cumulative
 Differential Relative
 Differential Absolute
50

42. A visualization for software project
Example 1 awareness and evolution, VISSOFT 2007
RM Ripley, A Sarma, A van der Hoek
of What: Developer activity over the code
for What: contribution analysis and change comprehension
51

43. Example 2
of What: logical
coupling between
modules
Animated visualization of
software history using evolution
storyboards, WCRE 2006
D Beyer, AE Hassan
for What: change prediction, reverse engineering, identification of
anomalies
52

44. Example 3
Exploring software evolution using
spectrographs, WCRE 2004
of What: files by version J Wu, RC Holt, AE Hassan
by recent changes
for What: change prediction (and contribution analysis)
53

45. Temporal Strategies
 Temporal Overview
 Temporal Snapshot
 Temporal Cumulative
54

46. Outline
 Introduction
 Information and Software Visualization
 Software Evolution Visualization (SEV)
 A Critical View of the Area
 SEV@UFBA
 Conclusion
55

47. Mapping Study
Search String
("software" OR "system")
AND
("visualization" OR "visual" OR "Visualisation")
AND
("evolution" OR "evolving")
56

48. SEV Data Sources
Type Data sources
Source Code CVS SubVersion GIT
Management
Bug Tracking Mantis BugZilla Trac
System
Models
Requirements Features
Other Types mapping Trace
Forums mapping
57

49. Most used data sources
60
49
50
41.78% use more than one data
40
34 source
29
30
20
10
10
5
3 2 2 2
1 1 1 1 1 1 1 1 1 1
0
58

50. Most used metrics
78.08% of the authors use more
60
56
than one metric
50
40
36
30 28
22
20
20
15
10
5
0
Coupling (any) # commit # authors Complexity Logical LOC Cohesion
Coupling
59

51. Most used perspectives
54,79% of the studies use
more than one perspective
60

52. Most used strategies
Only 23.97% use combination
of strategies
61

53. Goals
62

54. Papers per type of validation
50
19.18% 72.60% 0.68% 7.53%
45
44
Feasibility study
40 36
35
30 28
25
20
15 12
10
10
4 4 4
5 2 1 1
0
63

55. Outline
 Introduction
 Information and Software Visualization
 Software Evolution Visualization (SEV)
 A Critical View of the Area
 SEV@UFBA
 Conclusion
64

56. Goals of our SoftVis Group
 To help developers to build successful InfoVis tools
 To help system analysts to use InfoVis tools to
achieve their maintenance goals
 To help researchers to evaluate InfoVis tools from a
software engineering point of view
65

57. Our Approach
66

58. 67

59. EXAMPLE 1
Differential Strategies with
SourceMiner Evolution
68

60. Differential Strategies
 Differencial Absolute
 Differencial Relative
69

61. Using Colors to Represent Evolution Attributes
 From one version to the other,
elements that
 Appeared are painted in blue
 Disappeared are painted in gray
 No change are painted in white
 Elements that decreased or increase are painted in a
colour scale that ranges from bright green (decreased a lot)
to bright red (increased a lot)
70

62. Adapting SourceMiner’s Views and Perspectivas
71

63. Differential Strategies
 Differencial Absolute
 Differencial Relative
72

64. Implementing and evaluating the Relative
Differential Analysis
 Goal: Identify software erosion
 Tasks (questions):
 What are the hot spots of the source code?
 What are the God Classes, long methods? Etc.
 Experimental object:
 8 versions of MobileMedia (open source)
 Subjects: 14 Grad Students
 NOVAIS, R. L., CARNEIRO, G. F., SIMOES JUNIOR, P. R. M., MENDONÇA
NETO, M. G. On the Use of Software Visualization to Analyze Software
Evolution - An Interactive Differential Approach. In ICEIS 2011.
73

65. 74

66. Demo
SourceMiner Evolution
75

67. Differential Strategies
 Differencial Absolute
 Differencial Relative
76

68. Implementing and evaluating the Absolute
Differential Analysis
 Goal: Understand how features are evolving
 Tasks (questions):
 Tasks related to feature evolution, feature tangling, and feature dependency
analysis. Etc.
 Experimental object:
 5 versions of an oil company software in Brazil
 Subjects: 20 analysts from two cities
 NOVAIS, R., NUNES, C., LIMA, C., CIRILO, E., DANTAS, F., GARCIA, A.,
MENDONÇA, M. On the Proactive and Interactive Visualization for Feature
Evolution Comprehension: An Industrial Investigation. In ICSE’12.
79

69. Colour Represent Concerns
80

70. Colour Represent the Evolution of Concerns
81

71. EXAMPLE 2
Cumulative Temporal Evolution
&
Augmented Reality with
SkyscrapAR
84

72. 3D Software Visualization
3D representations for software
visualization, SoftVis’03
A Marcus, L Feng, JI Maletic
“The inclusion of aesthetically appealing elements such as 3D graphics
and animation not only increases the design’s appeal,
intuitiveness, and memorability of a visualization but also eases
perception of the human visual system” (Teyseyre, 2009)
85

73. SEV in 3D – CodeCity’s Example
Building height: number of methods
Building base (width and length):
number of attributes
Program comprehension through software habitability
R Wettel, M Lanza
86

74. Problems with 3D Visualization
 Oclusion X Interaction
 Navigation in a 3D world with a 2D mouse
87

75. Augmented Reality: A Cheap Solution
88

76. How it Works
89

77. SkyscrapAR
 Code-city metaphor
 Using a temporal cumulative strategy
to analyze code evolution
 In augmented reality
90

78. SkyscrapAR Building Metaphor
(2)
(3)
(1)
(1) Green area – class max LOC in history
(2) Building base – class LOC at current version
(3) Building height – class code churn in history
91

79. SkyscrapAR
Code-city Metaphor
92

80. 93 93

81. 94 94

82. 95 95

83. 96 96

84. 97 97

85. SkyscrapAR - Other info
98

86. SkyscrapAR - Cumulative Temporal Snapshot
 Accumulated Code Churn
 Animated transitions
ΔLOC
Vn
Vn-1
99

87. SkyscrapAR - Augmented Reality
(a) (b) (c)
(a) the camera captures a scene containing a marker;
(b) the image is transformed to black and white, the marker is detected
and its local coordinate system is computed;
(c) the 3D model is aligned with the coordinate axes.
100 100

88. Demo
SkyscrapAR
101

89. Outline
 Introduction
 Information and Software Visualization
 Software Evolution Visualization (SEV)
 A Critical View of the Area
 SEV@UFBA
 Conclusion
102

90. Summing up
 As software evolution comprehension is complex, there is
a need for multi-metrics, multi-perspectives, multi-data
SEV environments to address the many existing
engineering needs associated with software evolution.
103

91. Summarizing the mapping results
 The actual adoption of SEV in industrial environments
is very low
 There is very little evaluation of the proposed
approaches
 Very few with multi-strategies
104

92. Looking ahead
 We need to build good SEV Models to
 guide researchers in proposing new SEV approaches
 both from the SE and the InfoVis perspectives
 guide developers in building SEV tools
 guide system analysts in using such tools
 guide experimentalists in evaluating such tools
105

93. SoftVis group – http://softvis.dcc.ufba.br
106

94. Software Design and Evolution Group
http://aside.dcc.ufba.br
107

95. Software Evolution Visualization
Status, Challenges, and Possible Research Directions
Prof. Manoel Mendonça
http://fpc.dcc.ufba.br http://les.dcc.ufba.br
108 +55 (71) 3283-6311 manoel.mendonca@ufba.br