Reverse engineering techniques involve metrics, queries, and visualizations to analyze software systems. Metrics compress a system into measurable properties like lines of code and complexity. Queries detect patterns and flaws through metric-based rules. Visualizations compress a system into graphical representations like UML diagrams and polymetric views to show relationships and changes over time.

1. Reverse engineering
techniques
Tudor Gîrba
www.tudorgirba.com

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4. McCabe = 21
NOM 0
= 102
3 ,00
75
=
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Metrics Queries Visualizations ...
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5. McCabe = 21
NOM 0
= 102
3 ,00
75
=
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LO
Metrics Queries Visualizations ...
{ {
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6. McCabe = 21
NOM 0
= 102
3 ,00
75
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Metrics Queries Visualizations ...
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7. What are software metrics?

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

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

10. 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

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

12. Metrics compress the system into numbers

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

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

15. Queries reduce the analysis space

17. Detection Strategies are metric-based queries to
detect design flaws
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

18. Flaw: 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

19. Flaw: a Data Class provides data to other classes,
but little or no functionality of its own

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

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

22. Visualization compresses the system into pictures

23. 1854, London cholera epidemic

24. a picture is worth
a thousand words

26. UML took it literally :)

27. We
are
visual
beings

28. How many groups do you see?

29. How many groups do you see?

30. How many groups do you see?

31. How many groups do you see?

32. Gestalt principles
proximity similarity
enclosure connectivity

33. More Gestalt principles
closure continuity

34. We
are
visual
beings

35. a picture is worth
a thousand words

36. example: what is ?

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

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

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

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

46. Class Blueprint shows class internals

47. Distribution Map shows properties
Ducasse etal, 2006

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50. Spectographs show change activity
Wu etal, 2004
commit
time

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

52. Evolution Matrix shows changes in classes

53. 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

54. 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

55. History contains too much data
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 ...

56. 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 ...

57. Hierarchy Evolution View reveals patterns

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

59. But, what actually happens
behind the scene?

60. Example: CVS shows activity

61. Who did this?

62. Alphabetical order is no order
Kuhn

63. Ownership Map orders histories
Girba etal, 2006

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

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

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