This document discusses modeling software history and evolution. It presents several techniques for visualizing and analyzing changes in software over time, including matrices to show class evolution, metrics to detect design flaws, and strategies that consider historical context. Evolution information can provide insights into patterns of change, but modeling history poses challenges due to the large amount of data involved.

1. Modeling History
to Understand Software Evolution
Tudor Gîrba
www.tudorgirba.com

2. Modeling History
to Understand Software Evolution
Inauguraldissertation der
Philosophisch-naturwissenschaftlichen
Fakultät der Universität Bern
vorgelegt von
Tudor Gîrba
von Rumänien
Leiter der Arbeit:
Prof. Dr. Stéphane Ducasse
Prof. Dr. Oscar Nierstrasz
Institut für Informatik und
angewandte Mathematik

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7. Most often time is put on the horizontal
and a property on the vertical axis.
Lehman etal, 2001

8. Evolution Matrix shows how classes evolve.
Time is still on the horizontal axis.
Lanza, Ducasse, 2002

9. Co-change analysis recovers hidden dependencies.
Time is the lines.
Gall etal, 2003

10. Evolution information can be
mapped on structural information.
Eick etal, 2002

11. Eick etal, 2002
Lehman etal, 2001
...
Lanza, Ducasse, 2002
Gall etal, 2003

12. Eick etal, 2002
es?
niqu
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Lehman etal, 2001 ese
ll th
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om mo
we acc
can
ow
H
...
Lanza, Ducasse, 2002
Gall etal, 2003

13. sh
ort
int
er
me
zzo
What is a model?

14. sh
ort
int
er
me
zzo
A model is a simplification of the subject,
and its purpose is to answer some particular
questions aimed towards the subject.
Bezivin, Gerbe, 2001

15. sh
ort
int
er
me
zzo
what is a meta-model?

16. sh
ort
int
er
me
zzo
a meta-model is
a model that makes statements about
what can be expressed in valid models.
Seidewitz, 2003

17. sh
ort
int
er
me
zzo
a good meta-model
allows for succinct
expression of analyses.

18. Eick etal, 2002
Lehman etal, 2001
...
Lanza, Ducasse, 2002
Gall etal, 2003

19. Eick etal, 2002
es?
niqu
tech
Lehman etal, 2001 ese
ll th
da te a
om mo
we acc
can
ow
H
...
Lanza, Ducasse, 2002
Gall etal, 2003

20. Evolution Matrix shows class changes.
Lanza, Ducasse, 2004
Idle
class
Pulsar
class
Supernova
class
attributes
White dwarf
class methods Class

21. Evolution Matrix shows class changes.
Lanza, Ducasse, 2004
Idle
class
Pulsar
class
Supernova
class
attributes
White dwarf
class methods Class

22. Evolution Matrix shows class changes.
Lanza, Ducasse, 2004
Idle
class
entity.
st c lass
Pulsar
da s fir
class
mo dele
to be
needs
Supernova tio n
class Evolu
attributes
White dwarf
class methods Class

23. Evolution Matrix shows class changes.
Lanza, Ducasse, 2004
Idle
class
Pulsar
class
Supernova
class
attributes
White dwarf
class methods Class

24. Evolution Matrix shows class changes.
Idle
class history
Pulsar
class history
Supernova
class history
ClassHistory
White dwarf isPulsar
class history isIdle
...

25. System
Version
Class
Version

26. System
Version
Class Class
History Version

27. System System
History Version
Class Class
History Version

28. System System
History Version
Class Class
History Version

29. System System
History Version
en tity.
st c lass 200
5
ya s fir Gir ba,
tor
l s his
m ode
H ismo
Class Class
History Version

30. Measuring Yesterday’s Time-based
history Weather Detection Strategies
1 2 3
4 5 6
Visualizing the evolution Detecting How developers
of hierarchies co-change patterns drive evolution

31. Measuring history
1

32. What changed? When did it change? ...
2 4 3 5 7
2 2 3 4 9
2 2 1 2 3
2 2 2 2 2
1 5 3 4 4

33. Evolution of
Number of Methods LENOM(C) = ∑ |NOMi(C)-NOMi-1(C)| 2i-n
LENOM(C) = 4 + 2 + 1 + 0 = 7
1 5 3 4 4

34. Latest Evolution of
Number of Methods LENOM(C) = ∑ |NOMi(C)-NOMi-1(C)| 2i-n
Earliest Evolution of
Number of Methods EENOM(C) = ∑ |NOMi(C)-NOMi-1(C)| 22-i
-3 -2 -1 0
LENOM(C) = 42 + 22 + 12 + 02 = 1.5
1 5 3 4 4
EENOM(C) = 4 20 + 2 2-1 + 1 2-2 + 0 2-3 = 5.25

35. ENOM LENOM EENOM
2 4 3 5 7 7 3.5 3.25
2 2 3 4 9 7 5.75 1.37
2 2 1 2 3 3 1 2
2 2 2 2 2 0 0 0
1 5 3 4 4 7 1.25 5.25

36. ENOM LENOM EENOM
balanced changer 7 3.5 3.25
late changer 7 5.75 1.37
3 1 2
dead stable 0 0 0
early changer 7 1.25 5.25

37. ENOM LENOM EENOM
balanced changer 7 3.5 3.25
late changer 7 tity 5.75 nts. 1.37
en ureme
lass eas
st c gh m
s fir rou 3
y a th 1 2
r
H isto rison
co mpa
deadsstable
ena ble 0 0 0
early changer 7 1.25 5.25

38. History can be measured in many ways.
Evolution Number of Methods
Stability Number of Lines of Code
Historical Max of Cyclomatic Complexity
Growth Trend Number of Modules
... ...

39. Yesterday’s weather
2

40. The recently changed parts are likely to change in the
near future.
Common wisdom

41. The recently changed parts are likely to change in the
near future.
ally?
Common wisdom
re
re they
A

42. 30% 90%

45. present

46. past
present

47. past future
present

48. past future
present

49. past future
present

50. past future
prediction hit present

51. past future
YesterdayWeatherHit(present):
past:=histories.topLENOM(start, present)
future:=histories.topEENOM(present, end)
past.intersectWith(future).notEmpty()
prediction hit present

52. Overall Yesterday’s Weather shows the localization of
changed in time. Girba etal, 2004
hit hit hit
YW = 3 / 8 = 37%
hit hit hit hit hit hit hit
YW = 7 / 8 = 87%

53. Time-based
Detection Strategies
3

54. 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

55. Example: a God Class centralizes too much
intelligence in the system.
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

56. Example: a God Class centralizes too much
intelligence in the system.
Class uses directly more than a
few attributes of other classes
ATFD > FEW
tab le?
f it is s
wh
Functional complexity of the at i
ut,
class is very high
B AND GodClass
WMC ! VERY HIGH
Class cohesion is low
TCC < ONE THIRD

57. History-based Detection Strategies take evolution
into account. Ratiu etal, 2004
God Class
in the last version
isGodClass(last)
AND Harmless God Class
Stable throughout
the history
Stability > 90%

58. History-based Detection Strategies take evolution
into account. Ratiu etal, 2004
me.
God Class
in the last version
sa
ed the
isGodClass(last)
eat
e tr
pace ar
an ds AND Harmless God Class
e
Tim
Stable throughout
the history
Stability > 90%

59. Visualizing the evolution
of hierarchies
4

60. 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

61. 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

62. System System
History Version
Class Class
History Version

63. System System
History Version
Inheritance
Version
Class Class
History Version

64. System System
History Version
Inheritance Inheritance
History Version
Class Class
History Version

65. 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 ...

66. Hierarchy Evolution View 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 ...

67. Hierarchy Evolution View reveals patterns.
Girba etal, 2005

68. Hierarchy Evolution View reveals patterns.
Girba etal, 2005
ntity .
ss e aph
t cla a gr
firs to
as ing
tory mapp
His les
e nab

69. Identifying co-change
patterns
5

70. 1 2 3 4 5 6
A
A
B
B E
C
D
E C D
Gall etal, ‘98

71. Co-change patterns are n-ary relationships.

72. 1 2 3 4 5 6
A
B
C
D
Version
E

73. 1 2 3 4 5 6
A
B
C
D
Version
E changed

74. 1 2 3 4 5 6
A History
changed(i)
B
C
D
Version
E changed

75. What is Concept Analysis?

76. 1 2 3 4 5 6
A
B
C
D
E

77. {A, B, C, D, E}
1 2 3 4 5 6 Ø
A {A, D, B} {A, E, C, D}
{2} {6}
B
{D, B} {A, D} {A, E, C}
C FCA
{2, 4} {2, 6} {5, 6}
D
{D} {A} {C}
E {2, 4, 6} {2, 5, 6} {3, 5, 6}
Ø
{1, 2, 3, 4, 5, 6}

78. {A, B, C, D, E}
1 2 3 4 5 6 Ø
A {A, D, B} {A, E, C, D}
{2} {6}
B
{D, B} {A, D} {A, E, C}
C FCA
{2, 4} {2, 6} {5, 6}
D
{D} {A} {C}
E {2, 4, 6} {2, 5, 6} {3, 5, 6}
Ø
{1, 2, 3, 4, 5, 6}
Girba etal, 2007

79. Parallel Inheritance
add simultaneously children to several classes
Shotgun Surgery
change several classes simultaneously, but do not add methods

80. {A, B, C, D, E}
1 2 3 4 5 6 Ø
A {A, D, B} {A, E, C, D}
{2} {6}
B
{D, B} {A, D} {A, E, C}
C FCA
{2, 4} {2, 6} {5, 6}
D
{D} {A} {C}
E {2, 4, 6} {2, 5, 6} {3, 5, 6}
Ø
{1, 2, 3, 4, 5, 6}

81. {A, B, C, D, E}
1 2 3 4 5 6 Ø
A {A, D, B} {A, E, C, D}
{2} {6}
tity
B
s en A.
st clas {D, B}
FC
firFCA g to{2, 4} {A, D} {A, E, C}
y as appin
C {2, 6} {5, 6}
stor s m
Hi ble
D
ena {D} {A} {C}
E {2, 4, 6} {2, 5, 6} {3, 5, 6}
Ø
{1, 2, 3, 4, 5, 6}

82. How developers drive
software evolution
6

83. CVS shows activity.

84. Who is responsible for this?

85. Who is responsible for this?

86. Alphabetical order is no order.

87. The Hausdorf metric can be used to compute the
similarity between commits.
A
d(A, B) = ∑ min2{ | a - b | b in B }
a in A
B

88. Alphabetical order is no order.

89. Ownership Map reveals development patterns.
Girba etal, 2006

90. Ownership Map reveals development patterns.
Girba etal, 2006
tity anges.
s en c ch
clas listi
rst ho
as fi out
tory ing ab
His son
les rea
enab

91. Measuring Yesterday’s Time-based
history Weather Detection Strategies
1 2 3
4 5 6
Visualizing the evolution Detecting How developers
of hierarchies co-change patterns drive evolution

92. System System
History Version
Inheritance Inheritance
History Version
Class Class
History Version

93. History Version
History Version
History Version

94. History Version
en tity.
st c lass 200
5
as fir Gir ba,
his tory History Version
mo dels
Hi smo
History Version

95. Modeling History
to Understand Software Evolution
Inauguraldissertation der
Philosophisch-naturwissenschaftlichen
Fakultät der Universität Bern
vorgelegt von
Tudor Gîrba
von Rumänien
Leiter der Arbeit:
Prof. Dr. Stéphane Ducasse
Prof. Dr. Oscar Nierstrasz
Institut für Informatik und
angewandte Mathematik
www.tudorgirba.com

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