1. Metrics and Problem Detection
Jorge Ressia

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

3. 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

4. }
}
{
{
}
}
{
{
g
rin
ee
gin
en
d
ar
rw
fo

5. fo
rw
ar
d
en
gin
ee
rin
g
{ {
{ { { {
} { {
}
} actual development } }
} { } } }

6. What is the current state?
fo
rw
ar
What should we do?
d
en
Where to start?
gin
ee
How to proceed?
rin
g
{ {
{ { { {
} { {
}
} actual development } }
} { } } }

7. fo
rw
g
rin
ar
ee
d
gin
en
gin
en
ee
e
rs
rin
ve
g
re
{ {
{ { { {
} { {
}
} actual development } }
} { } } }

8. Reverse engineering is analyzing a subject system to:
identify components and their relationships, and
create more abstract representations.
Chikofky & Cross, 90

9. quality?
udg e its
to j
How {
{
{
{
}
}
}
} { }
A large system contains lots of details.

10. http://moose.unibe.ch
http://loose.upt.ro/incode

11. 1
Metrics
2Design
Problems
3
Code Duplication

12. Metrics
1

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

14. Metrics are functions that assign numbers to
products, processes and resources.

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

16. Metrics compress system traits into numbers.

17. Let’s see some examples...

18. 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
Lorenz, Kidd, 1994
Chidamber, Kemerer, 1994

19. McCabe cyclomatic complexity (CYCLO) counts
the number of independent paths through the code of a
function.
McCabe, 1977
 it reveals the minimum number of tests to write
 interpretation can’t directly lead to improvement action

20. Weighted Method Count (WMC) sums up the
complexity of class’ methods (measured by the metric
of your choice; usually CYCLO).
Chidamber, Kemerer, 1994
 it is configurable, thus adaptable to our precise needs
 interpretation can’t directly lead to improvement action

21. Depth of Inheritance Tree (DIT) is the (maximum)
depth level of a class in a class hierarchy.
Chidamber, Kemerer, 1994
 inheritance is measured
 only the potential and not the real impact is quantified

22. Coupling between objects (CBO) shows the number
of classes from which methods or attributes are used.
Chidamber, Kemerer, 1994
 it takes into account real dependencies not just declared ones
 no differentiation of types and/or intensity of coupling

23. Tight Class Cohesion (TCC) counts the relative
number of method-pairs that access attributes of the
class in common.
Bieman, Kang, 1995
TCC = 2 / 10 = 0.2
 interpretation can lead to improvement action
 ratio values allow comparison between systems

24. Access To Foreign Data (ATFD) counts how many
attributes from other classes are accessed directly from
a measured class.
Marinescu 2006

25. ...

26. Design Problems
2

27. McCall, 1977

28. Metrics Assess and Improve Quality!
ea lly?
R

29. McCall, 1977

30. Problem 1: metrics granularity
?
capture symptoms, not causes of problems
in isolation,
they don’t lead to improvement solutions
Problem 2: implicit mapping
we don’t reason in terms of metrics,
but in terms of design principles

31. 2 big obstacles in using metrics:
Thresholds make metrics hard to interpret
Granularity make metrics hard to use in isolation

32. Can metrics help me
in what I really care for? :)

33. et rics!
ith m
o do w
thi ng t
an t no
Iw
fo
rw
ar
How do I understand code?
d
en
How do I improve code?
gin
ee
rin
g
{ {
{ { { {
} { {
}
} actual development } }
} { } } }

34. How to get an initial understanding of a system?

35. Metric Value
LOC 35175
NOM 3618
NOC 384
CYCLO 5579
NOP 19
CALLS 15128
FANOUT 8590
AHH 0.12
ANDC 0.31

36. Metric Value
LOC 35175
NOM 3618
NOC 384
CYCLO 5579
NOP 19
CALLS 15128
FANOUT 8590
t?
wha
AHH
nd now 0.12
A
ANDC 0.31

37. We need means to compare.

38. hierarchies?
coupling?

39. The Overview Pyramid provides a metrics
overview. Lanza, Marinescu 2006
Inheritance
ANDC 0.31
AHH 0.12
20.21 NOP 19
9.42 NOC 384
9.72 NOM 3618 NOM 418
0.15 LOC 35175 15128 CALLS 0.56
CYCLO 5579 8590 FANOUT
Size Communication

40. The Overview Pyramid provides a metrics
overview. Lanza, Marinescu 2006
ANDC 0.31
AHH 0.12
20.21 NOP 19
9.42 NOC 384
9.72 NOM 3618 NOM 418
0.15 LOC 35175 15128 CALLS 0.56
CYCLO 5579 8590 FANOUT
Size

41. The Overview Pyramid provides a metrics
overview. Lanza, Marinescu 2006
ANDC 0.31
AHH 0.12
20.21 NOP 19
9.42 NOC 384
9.72 NOM 3618 NOM 418
0.15 LOC 35175 15128 CALLS 0.56
CYCLO 5579 8590 FANOUT
Communication

42. The Overview Pyramid provides a metrics
overview. Lanza, Marinescu 2006
Inheritance
ANDC 0.31
AHH 0.12
20.21 NOP 19
9.42 NOC 384
9.72 NOM 3618 NOM 418
0.15 LOC 35175 15128 CALLS 0.56
CYCLO 5579 8590 FANOUT

43. The Overview Pyramid provides a metrics
overview. Lanza, Marinescu 2006
ANDC 0.31
AHH 0.12
20.21 NOP 19
9.42 NOC 384
9.72 NOM 3618 NOM 418
0.15 LOC 35175 15128 CALLS 0.56
CYCLO 5579 8590 FANOUT

44. Java C++
LOW AVG HIGH LOW AVG HIGH
CYCLO/LOC 0.16 0.20 0.24 0.20 0.25 0.30
LOC/NOM 7 10 13 5 10 16
NOM/NOC 4 7 10 4 9 15
...

45. The Overview Pyramid provides a metrics
overview. Lanza, Marinescu 2006
ANDC 0.31
AHH 0.12
20.21 NOP 19
9.42 NOC 384
9.72 NOM 3618 NOM 418
0.15 LOC 35175 15128 CALLS 0.56
CYCLO 5579 8590 FANOUT
close to high close to average close to low

46. The Overview Pyramid provides a metrics
overview. Lanza, Marinescu 2006
close to high close to average close to low

47. rics!
met
do with
o
ng t
n othi
Iw ant
fo
rw
ar
How do I understand code?
d
en
How do I improve code?
gin
ee
rin
g
{ {
{ { { {
} { {
}
} actual development } }
} { } } }

48. How do I improve code?

49. Quality is more than 0 bugs.
Breaking design principles, rules and best practices
deteriorates the code;
it leads to design problems.

50. Imagine changing just a small design fragment
and33%
of all classes
would require changes

51. expensive
Design problems
are frequent
unavoidable
?
e them
inat
elim
and
detect
How to

52. God Classes tend to centralize the intelligence of the
system, to do everything and to use data from small
data-classes.
Riel, 1996

53. God Classes tend
to centralize the intelligence of the system,
to do everything and
to use data from small data-classes.

54. God Classes
centralize the intelligence of the system,
do everything and
use data from small data-classes.

55. God Classes
are complex,
are not cohesive,
access external data.

56. God Classes
are complex,
WMC is high
are not cohesive,
TCC is low
access external data.
ATFD more than few
su sing
u erie
to q s
cs in rator
etri pe
se m ical o
C o mpo log

57. 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

58. 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

59. An Envious Method is more interested in data
from a handful of classes. Lanza, Marinescu 2006
Method uses directly more than
a few attributes of other classes
ATFD > FEW
Method uses far more attributes
of other classes than its own
AND Feature Envy
LAA < ONE THIRD
The used quot;foreignquot; attributes
belong to very few other classes
FDP ! FEW

60. Data Classes are dumb data holders.
Lanza, Marinescu 2006
Interface of class reveals data
rather than offering services
WOC < ONE THIRD
AND Data Class
Class reveals many attributes and is
not complex

61. Data Classes are dumb data holders.
Lanza, Marinescu 2006
More than a few public
data
NOAP + NOAM > FEW
AND
Complexity of class is not
high
WMC < HIGH
Class reveals many
OR attributes and is not
Class has many public complex
data
NOAP + NOAM > MANY
AND
Complexity of class is not
very high
WMC < VERY HIGH

62. Shotgun Surgery depicts that a change in an
operation triggers many (small) in a lot of different
operation and classes.
Lanza, Marinescu 2006

63. Code Duplication
3

64. What is Code Duplication?
so f it?
pro blem
he
ta re t
a
Wh

65. Code Duplication Detection
Lexical Equivalence
Syntactical Equivalence
Semantic Equivalence

66. Visualization of Copied Code Sequences
File A File B
File A
File B

67. Transformation Comparison
Source Code Transformed Code Duplication Data
Author Level Transformed Code Comparison Technique
Johnson 94 Lexical Substrings String-Matching
Ducasse 99 Lexical Normalized Strings String-Matching
Baker 95 Syntactical Parameterized Strings String-Matching
Mayrand 96 Syntactical Metrics Tuples Discrete comparison
Kontogiannis 97 Syntactical Metrics Tuples Euclidean distance
Baxter 98 Syntactical AST Tree-Matching

68. Noise Elimination
…
//assign same fastid as container fastid=NULL;
fastid = NULL; constchar*fidptr=get_fastid();
const char* fidptr = get_fastid(); if(fidptr!=NULL)
if(fidptr != NULL) { intl=strlen(fidptr)
int l = strlen(fidptr); fastid = newchar[l+]
fastid = newchar[ l + 1 ];

69. Enhanced Simple Detection Approach

70. lines from source
a b c d a b c d
lines
from
source

71. lines from source
a b c d a x y d
lines
from
source

72. lines from source
a b c a b x y c
lines
from
source

73. lines from source
a x b x c x d x
lines
from
source

74. lines from source 2
lines
from
source 1

75. lines from source 2
lines
from
source 1

76. lines from source 2
lines
from
source 1
exact
chunk

77. lines from source 2
lines
from
source 1
exact line
chunk bias

78. lines from source 2
lines
from
source 1
exact line exact
chunk bias chunk

79. Significant Duplication:
- It is the largest possible duplication chain uniting
all exact clones that are close enough to each
other.
- The duplication is large enough.
Lanza, Marinescu 2006

80. 1
Metrics
2Design
Problems
3
Code Duplication

81. Shotgun
Surgery has
uses is
has (partial) Feature Data
Envy uses Class
is partially
God has
Intensive Class
Coupling Brain has
has
Method
Extensive Brain has Significant
Coupling Class Duplication
has
is
is
has
Refused
is Tradition
Parent
Breaker
Bequest
has (subclass)
Futile
Hierarchy Lanza, Marinescu 2006
Identity Collaboration Classification
Disharmonies Disharmonies Disharmonies

82. Follow a clear and repeatable process
mb ers!
u
ms of n
in ter
qu ality
ut
n abo
easo
Don’t r

83. QA is part of the the Development Process
http://loose.upt.ro/incode

84. Tudor Gîrba
www.tudorgirba.com
Jorge Ressia
creativecommons.org/licenses/by/3.0/