The document discusses techniques for analyzing software evolution and defects using mining software repositories (MSR) approaches. It describes change coupling analysis, which uses part of a meta-model to make sense of large amounts of change coupling information and address the goal of understanding how changes propagate through a software system. The technique analyzes the relationships between source code changes and software defects by examining the coupling between changes in source code and bug reports.

1. On the Evolution of
Source Code and Software Defects
Marco D’Ambros
REVEAL group @ Faculty of Informatics
University of Lugano, Switzerland
Dissertation committee
Prof. Michele Lanza
Prof. Carlo Ghezzi
Prof. Cesare Pautasso
Prof. Harald C. Gall
Prof. Hausi A. Müller

2. Software & Bridges
software engineering and bridges

3. Software
Requirements

4. Software
Requirements

5. Software
Software
aging
Requirements

6. Ph.D.
We are here

7. Ph.D.
Conclusion
x
x
Thesis
Analysis
x
techniques x Tool
support
We are here

8. Ph.D.
Conclusion
swamp of
procrastination
x
peaks of tools
madness
x
Thesis
Analysis
x
techniques x Tool
support
We are here haunted teachwood
forest

9. Ph.D.
Conclusion
swamp of
procrastination
x
peaks of tools
madness
x
Thesis
Analysis
x
techniques x Tool
support
We are here haunted teachwood
forest

10. The Evolution of Software
Evolution
1975 First software configuration First bug tracking First workshop MSR becomes a
managament (SCSS) RCS CVS system (GNATS) Bugzilla Subversion on MSR Git Jazz conference
1980 1988 1990 1995 1996 1997 1999 2000 2003 2010
1982 1986 1992 2004 2006 2007 2008
Cost of software maintenance First MSR Cost of software Release History
Lehman's laws of Boehm's spiral
estimated to be 50-75% of the total approach (Ball maintenance estimated to Database
software evolution model cost of software (Sommerville, Davis) et. al.) be more than 85% (Erlik) (Fischer et. al.)
Extreme Programming
Explained: Embrace Changes

11. 1975 First software configuration
managament (SCSS) RCS CVS
1980 1988 1990
1982 1986
Lehman's laws of Boehm's spiral
software evolution model
Foundation Publication Tool

12. First bug tracking
system (GNATS) Bugzilla Subversion
1990 1995 1996 1997 1999 2000
1992
Cost of software maintenance First MSR
estimated to be 50-75% of the total approach (Ball
cost of software (Sommerville, Davis) et. al.)
Extreme Programming
Explained: Embrace Changes
Infrastructure
Implementation Publication Tool

13. First workshop MSR becomes a
on MSR Git Jazz conference
2000 2003 2010
2004 2006 2007 2008
Cost of software Release History
maintenance estimated to Database
be more than 85% (Erlik) (Fischer et. al.)
The Advent of MSR Event Publication Tool

14. The Evolution of Software
Evolution
1975 First software configuration First bug tracking First workshop MSR becomes a
managament (SCSS) RCS CVS system (GNATS) Bugzilla Subversion on MSR Git Jazz conference
1980 1988 1990 1995 1996 1997 1999 2000 2003 2010
1982 1986 1992 2004 2006 2007 2008
Cost of software maintenance First MSR Cost of software Release History
Lehman's laws of Boehm's spiral
estimated to be 50-75% of the total approach (Ball maintenance estimated to Database
software evolution model cost of software (Sommerville, Davis) et. al.) be more than 85% (Erlik) (Fischer et. al.)
Extreme Programming
Explained: Embrace Changes
Foundation Infrastructure The advent of
Implementation MSR

15. MSR Approaches
30%
23%
15%
8%
0%
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>0)
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
SCM Source Software E-mail Documentation Others
meta-data code defects archive

16. MSR Approaches
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>0)
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
SCM Source Software E-mail Documentation Others
meta-data code defects archive

17. Integrated MSR Approaches
Plain
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>0)
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
SCM Source Software E-mail Documentation Others
meta-data code defects archive

18. Models
SCM meta-data Source code
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
IDE data Documentation
Holistic
software
evolution
Chats Unit tests
Software defects E-mail archive
Bytecode

19. Models
SCM meta-data Source code
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
IDE data Documentation
Holistic
software
evolution
Chats Unit tests
Software defects E-mail archive
Bytecode

20. Models
SCM meta-data Source code
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
IDE data Documentation
Holistic
software
evolution
Chats Unit tests
Software defects E-mail archive
Bytecode

21. Models
SCM meta-data Source code
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
IDE data Documentation
Holistic
software
evolution
Chats Unit tests
Software defects E-mail archive
Bytecode A. Zeller, MSR keynote 2007

22. Models
SCM meta-data Source code
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
IDE data Documentation
Our Approach
Chats Unit tests
Software defects E-mail archive
Bytecode

23. An integrated view of software
evolution, combining historical
information regarding source
co de and software defects,
supports an extensible set of
software maintenance tasks.
D’Ambros, 2010

24. SCM meta-data Source code
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Software defects E-mail archive

25. SCM meta-data Source code
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Software defects E-mail archive
Mevo meta-model
CSMR 2008

26. SCM meta-data Source code
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Software defects E-mail archive
Mevo meta-model
CSMR 2008

27. SCM meta-data Source code
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Software defects E-mail archive
Mevo meta-model
CSMR 2008

28. 7 analysis techniques
SCM meta-data Source code
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Software defects E-mail archive
Mevo meta-model
CSMR 2008

29. Ph.D.
Conclusion
swamp of
procrastination
x
peaks of tools
madness
x
Thesis
Analysis
x
techniques x Tool
support
We are here haunted teachwood
forest

30. Ph.D.
Conclusion
swamp of
procrastination
x
peaks of tools
madness
x
Thesis
Analysis
x
techniques x Tool
support
We are here haunted teachwood
forest

31. Ph.D.
Conclusion
swamp of
procrastination
x
peaks of tools
madness
x
Thesis
Analysis
x
techniques x Tool
support
We are here haunted teachwood
forest

32. Retrospective analysis Future prediction

33. Retrospective analysis
Change coupling
1
analysis
2 3
Bug evolution
analysis
Code-bug
co-evolution analysis

34. Change Coupling Analysis
SCM meta-data Source code
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Software defects E-mail archive
Goal: Make sense of huge amount of change
coupling information

35. Technique name
Change Coupling Analysis
SCM meta-data Source code
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Software defects E-mail archive
Goal: Make sense of huge amount of change
coupling information

36. Technique name
Change Coupling Analysis
SCM meta-data Source code Used part of
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
the meta-model
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Software defects E-mail archive
Goal: Make sense of huge amount of change
coupling information

37. Technique name
Change Coupling Analysis
SCM meta-data Source code Used part of
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
the meta-model
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Software defects E-mail archive
Goal: Make sense of huge amount of change
coupling information
Goal /
Question

38. Technique name
Change Coupling Analysis
SCM meta-data Source code Used part of
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
the meta-model
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Technique
number
Software defects E-mail archive
Goal: Make sense of huge amount of change
coupling information
Goal /
Question

39. Change Coupling Analysis
SCM meta-data Source code
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Software defects E-mail archive
Goal: Make sense of huge amount of change
coupling information

40. change cou•pling
implicit dependency of files
that frequently change together
[Gall et al., ICSM 1998]

42. Module level to assess
architecture decay

43. Class level to support
change impact analysis

44. Package Class Protocol Method
browser browser browser browser
Main Evolution Secondary Evolution
Radar Visualization Radar Visualization
Current project
package list
The Evolution Radar

45. Package Class Protocol Method
browser browser browser browser
Main Evolution Secondary Evolution
Radar Visualization Radar Visualization
Current project
package list
System re-documentation and restructuring

46. Assessing Change impact
ay
architecture dec analysis
Coupled files
Tasks Supported
System evolution analys
is
Protocol Method
Package Class browser
browser browser
browser
Main Evolution Secondary Evolution
Radar Visualization Radar Visualization
Time
Current project
package list
System re-documentation and restructuring

47. Change Coupling Analysis
The Evolution Radar shows
integrated change coupling
information, supporting various
maintenance tasks.
MSR 2006, WCRE 2006, TSE 2009

48. Bug Evolution Analysis
SCM meta-data Source code
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Software defects E-mail archive
Goal: Study the history of software defects

49. 35%
Bu g lifetime
di stribution in Mozilla
> 50%
28%
21%
14%
7%
0%
12 Hours 1 Day 1 Week 1 Month 6 Months 1 Year 2 Years More

50. System Radiography view
Which components experienced
many defects?

51. System Radiography view
Which components experienced
many defects?
Which defects are hard to fix?
Bug watch view

52. Bug Evolution Analysis
The visual analysis of bug
histories permits the detection
of critical software components
and exceptional bugs.
VISSOFT 2007

53. Bug-Code Co-Evolution Analysis
SCM meta-data Source code
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Software defects E-mail archive
Goal: Detect patterns in the co-evolution
of source code and defects

54. Code committed in SCM repository
Foo.java
SCM meta-data Time

55. Code committed in SCM repository
Foo.java
SCM meta-data Time
Foo.java
Defects Time
Defect reported

56. Code committed in SCM repository
Foo.java
Bar.java
Boo.java
SCM meta-data Time
Foo.java
Bar.java
Baz.java
Defects Time
Defect reported

57. Code committed in SCM repository
Foo.java
Bar.java
Boo.java
SCM meta-data Time
Foo.java
Bar.java
Baz.java
Defects Time
Defect reported

58. Code committed in SCM repository
Foo.java
Bar.java
Boo.java
SCM meta-data Time
Foo.java
Bar.java
Baz.java
Defects Time
Defect reported

59. Code committed in SCM repository
Foo.java
Bar.java
Boo.java
Co-evolution
pattern
SCM meta-data Time
Foo.java Δt
Bar.java
Baz.java
Defects Time
Defect reported

60. Catalog of 10 formally
defined patterns

61. Bug-Code Co-Evolution Analysis
Detecting and visualizing
co-evolutionary patterns allows
the characterization of software
components based on their
co-evolution.
CSMR 2006, JSME 2009

62. Retrospective analysis Future prediction

63. Future prediction
Bug Bug prediction
4 5
prediction with e-mails
6 7
Bug prediction with
change coupling
Software quality
analysis

64. Bug Prediction
SCM meta-data Source code
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Software defects E-mail archive
Question: Which bug prediction approach
is the best over different systems?

65. Bug prediction is a
very active research field
100+
PAPERS

66. Bug prediction is a
Which one is better? very active research field
100+
PAPERS
Other factors
Previous defects
Process metrics
Code metrics

67. Moser et al. SCM meta-data
Basili et al. Source code metrics
Kim et al. Previous defects
Hassan Entropy of changes

68. . SCM meta-data
. Source code metrics
. Previous defects
n Entropy of changes

69. Churn of
. SCM meta-data
code metrics
Entropy of
. Source code metrics
code metrics
. Previous defects
n Entropy of changes

70. for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>0)
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Time
Release X

71. Prediction goal
Post release defects
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>0)
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Time
Past “Future”
Release X

72. Prediction goal
Post release defects
1 Previous defects
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>0)
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Time
Past “Future”
Release X

73. 2 SCM meta-data Prediction goal
3 Entropy of changes
Post release defects
1 Previous defects
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>0)
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Time
Past “Future”
Release X

74. 2 SCM meta-data Prediction goal
3 Entropy of changes
Post release defects
1 Previous defects
CBO DIT NOC
99
23.4
12.1
20.3
99
23.4
12.1
20.3
99
23.4
12.1
20.3
4 Source code
62.1
43.8
62.1
43.8
62.1
43.8 metrics
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>0)
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Time
Past “Future”
Release X

75. 2 SCM meta-data Prediction goal
3 Entropy of changes
Post release defects
1 Previous defects
5 6 Entropy and churn of code metrics
CBO DIT NOC CBO DIT NOC CBO DIT NOC
99
23.4
12.1
20.3
99
23.4
12.1
20.3
99
23.4
12.1
20.3 deltas
99
23.4
12.1
20.3
99
23.4
12.1
20.3
99
23.4
12.1
20.3
deltas
99
23.4
12.1
20.3
99
23.4
12.1
20.3
99
23.4
12.1
20.3
4 Source code
62.1
43.8
62.1
43.8
62.1
43.8
62.1
43.8
62.1
43.8
62.1
43.8
62.1
43.8
62.1
43.8
62.1
43.8 metrics
for(int j=m; j>i; j--){ for(int j=m; j>i; j--){ for(int j=m; j>i; j--){
uCJM1= dataUC[j-1]; uCJM1= dataUC[j-1]; uCJM1= dataUC[j-1];
uCJ= dataUC[j]; uCJ= dataUC[j]; uCJ= dataUC[j];
if(uCJM1.compare(z)>0) if(uCJM1.compare(z)>0) if(uCJM1.compare(z)>0)
{ /* exchange */ { /* exchange */ { /* exchange */
tempStr= data[j-1]; tempStr= data[j-1]; tempStr= data[j-1];
/* sort the data */ /* sort the data */ /* sort the data */
data[j-1]= data[j]; data[j-1]= data[j]; data[j-1]= data[j];
data[j]= tempStr; data[j]= tempStr; data[j]= tempStr;
dataUC[j-1]= uCJ; dataUC[j-1]= uCJ; dataUC[j-1]= uCJ;
dataUC[j]= uCJM1; dataUC[j]= uCJM1; dataUC[j]= uCJM1;
} } }
} } }
Time
Past “Future”
Release X

76. Comparing Prediction Models
SCM meta-data
Churn of code metrics
Entropy of code metrics
Code metrics
Previous defects
Entropy of changes Prediction
performance
0.2 0.4 0.6 0.8

77. Comparing Prediction Models
Previous defects
Prediction
performance
0.2 0.4 0.6 0.8

78. Comparing Prediction Models

79. Comparing Prediction Models
100%
90%
score ← score + 3
score ← score + 1
75%
50%
score ← score - 1

80. Comparing Prediction Models
100%
90%
score ← score + 3
score ← score + 1
75%
50%
score ← score - 1

81. Comparing Prediction Models
JDT Core PDE Mylyn Equinox Lucene
100% 100% 100% 100% 100%
90%
score ← score + 3 score ← score + 3 score ← score + 3 score ← score + 3 score ← score + 3
90% 90% 90% 90%
score ← score + 1 score ← score + 1 score ← score + 1 score ← score + 1 score ← score + 1
75% 75% 75% 75% 75%
50% 50% 50% 50% 50%
score ← score - 1 score ← score - 1 score ← score - 1 score ← score - 1 score ← score - 1
score = Σ
s ∈ systems
score(s)

82. Prediction Performance Across Five Systems
SCM meta-data
Entropy of changes
Code metrics
Previous defects
Churn of code metrics
Entropy of code metrics
0 4 8 11 15 Score

83. Prediction Performance Across Five Systems
SCM meta-data
Performance not stable
across systems
Entropy of changes
Code metrics
Good performance and
fast to compute
Previous defects
Churn of code metrics Most stable performance
but computationally
expensive
Entropy of code metrics
0 4 8 11 15 Score

84. Bug Prediction
The entropy and the churn of
code metrics are the most stable
predictors across different
systems.
MSR 2010, EMSE under review

85. Bug Prediction with Change Coupling
SCM meta-data Source code
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Software defects E-mail archive
Question: Does change coupling correlate
with software defects?

86. Change coupling is harmful
What is the impact on software defects?

87. Measuring Change Coupling
Distribution
Number of coupled classes
Force
Number of co-changes
Time decay
Changes far in the past count less

88. Correlation with Software Defects
Distribution
Change coupling str
ongly
Force correlates with softw
are defects
0.8+
Time decay
Spearman’s correlation

89. Correlation with Software Defects
Distribution
Change coupling str
ongly
Force correlates with softw
are defects
0.8+
Time decay
Spearman’s correlation

90. Defect Prediction
SCM Change
data coupling
Code
metrics
Explanative power

91. Defect Prediction
0.9
0.8
SCM Change
0.6
data coupling
0.5
0.3 Code
metrics
0.2
0
Explanative power

92. Defect Prediction
0.9
Change
0.8 coupling
SCM
Change data
0.6
SCM
data coupling
Code
metrics
0.5
0.3 Code
metrics
0.2
0
Explanative power Predictive power

93. Defect Prediction
0.9
Change
0.8
14% coupling
SCM
Change data
0.6
SCM
data coupling
Code
metrics
0.5
0.3 Code
metrics
0.2
0
Explanative power Predictive power

94. Bug Prediction with Change Coupling
Change coupling correlates
with software defects and can
be used to improve defect
prediction models.
WCRE 2009

95. Software Quality Analysis
SCM meta-data Source code
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Software defects E-mail archive
Question: Do design flaws correlate with
software defects?

96. Design Flaws
Class (Size ∝responsibility)
Design guideline: A class should have
one responsibility

97. Design Flaws
Class (Size ∝responsibility)
Design guideline: A class should have
one responsibility
Vio l ate d

98. Design Flaws
Does the pre sence of design
flaws corre late with software
defects?
An d their addition?
Class (Size ∝responsibility)
Design guideline: A class should have
one responsibility
Vio l ate d

99. Analyzing Design Flaws
Design flaws ~
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
Source code
if(uCJM1.compare(z)>0)
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}

100. Analyzing Design Flaws
Design flaws ~
Detection strategies
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
Source code
if(uCJM1.compare(z)>0)
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}

101. Analyzing Design Flaw Deltas
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>0)
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}

102. Analyzing Design Flaw Deltas
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>0)
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Time

103. Analyzing Design Flaw Deltas
for(int j=m; j>i; j--){ for(int j=m; j>i; j--){ for(int j=m; j>i; j--){ for(int j=m; j>
uCJM1= dataUC[j-1]; uCJM1= dataUC[j-1]; uCJM1= dataUC[j-1]; uCJM1= dataUC
2 weeks 2 weeks 2 weeks
uCJ= dataUC[j]; uCJ= dataUC[j]; uCJ= dataUC[j]; uCJ= dataUC[j
if(uCJM1.compare(z)>0) if(uCJM1.compare(z)>0) if(uCJM1.compare(z)>0) if(uCJM1.comp
{ /* exchange */ { /* exchange */ { /* exchange */ { /* exchange
tempStr= data[j-1]; tempStr= data[j-1]; tempStr= data[j-1]; tempStr= da
/* sort the data */ /* sort the data */ /* sort the data */ /* sort the
data[j-1]= data[j]; data[j-1]= data[j]; data[j-1]= data[j]; data[j-1]=
data[j]= tempStr; data[j]= tempStr; data[j]= tempStr; data[j]= te
dataUC[j-1]= uCJ; dataUC[j-1]= uCJ; dataUC[j-1]= uCJ; dataUC[j-1]
dataUC[j]= uCJM1; dataUC[j]= uCJM1; dataUC[j]= uCJM1; dataUC[j]=
} } } }
} } } }
Time

104. Analyzing Design Flaw Deltas
Number of flaws
Time

105. Analyzing Design Flaw Deltas
Number of flaws Addition of flaws
Time

106. Analyzing Design Flaw Deltas
Number of flaws Addition of flaws
Time
correlation
Defects
Time
Δt

107. Results on Six Large Systems
0.4 +
Flaw presence correlation
0.6+
Flaw addition correlation

108. Results on Six Large Systems
0.4 +
Flaw presence correlation
aw correlate s more than
No fl
sistently acr oss systems
other s con
0.6+
Flaw addition correlation

109. Software Quality Analysis
The presence and addition of
design flaws correlates with
software defects.
QSIC 2010

110. Bug Prediction with E-mails
SCM meta-data Source code
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Software defects E-mail archive
Question: Can bug prediction techniques
be improved with e-mail data?

111. Software entities that are frequently
mentioned in development mailing
lists are defect prone
Popularity Metrics

112. Spearman’s correlation
Do popularit
y metrics
correlate wit
h defects?
Popularity metrics

113. Spearman’s correlation
Do popularit
y metrics
0.8 correlate wit
h defects?
Popularity metrics
0.6
0.4
0.2
0
Equinox Jackrabbit Lucene Maven

114. Spearman’s correlation
Do popularit
y metrics
0.8 correlate wit
h defects?
Popularity metrics
Lines of code
0.6
0.4
0.2
0
Equinox Jackrabbit Lucene Maven

115. Prediction performance Do popularity metrics improve
existing bug prediction techniques?
POP POP
metrics metrics

116. Prediction performance Do popularity metrics improve
existing bug prediction techniques?
0.5
SCM data
0.4
Code
metrics
0.3
POP POP
metrics metrics
0.2
0.1
0

117. Prediction performance Do popularity metrics improve
existing bug prediction techniques?
0.5
4% SCM
0.4
SCM data + 12% Code
+
POP POP
Code
metrics
0.3
POP POP
metrics metrics
0.2
0.1
0

118. Bug Prediction with E-mails
Popularity metrics extracted
from development mailing lists
correlate with defects and
improve existing bug prediction
techniques.
FASE 2010

119. Retrospective Analysis
Change coupling Bug evolution Code-bug
1 2 3
analysis analysis co-evolution analysis
4 5 6 7
Bug prediction Bug prediction
with change
Software quality
analysis
Bug prediction
with e-mails
coupling
Future Prediction

120. Change coupling
1 2
analysis
Bug prediction
with e-mails
7 for(int j=m; j>i; j--){
}
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
}
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
Bug evolution
analysis
Software quality
analysis
6 3 Mevo meta-model
Code-bug
co-evolution
analysis
5 4
Bug prediction with
change coupling
Bug prediction

121. Ph.D.
Conclusion
swamp of
procrastination
x
peaks of tools
madness
x
Thesis
Analysis
x
techniques x Tool
support
We are here haunted teachwood
forest

122. Ph.D.
Conclusion
swamp of
procrastination
x
peaks of tools
madness
x
Thesis
Analysis
x
techniques x Tool
support
We are here haunted teachwood
forest

123. Ph.D.
Conclusion
swamp of
procrastination
x
peaks of tools
madness
x
Thesis
Analysis
x
techniques x Tool
support
We are here haunted teachwood
forest

124. tools were
fundamental for my
daddy's research

125. for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Mevo meta-model

126. Churrasco framework
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Mevo meta-model
SCP 2010

127. Churrasco framework
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
Importers
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Mevo meta-model
Web interface
SCP 2010

128. Churrasco framework
for(int j=m; j>i; j--){ Evolution Radar
Data interface
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
Importers
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
Bug’s Life
BugCrawler
Mevo meta-model Pendolino
Web interface
SCP 2010

129. Tool Gallery
Evolution Radar

130. Tool Gallery
Bug’s Life

131. Tool Gallery
BugCrawler

132. Tool Gallery
Pendolino

133. Tool Gallery
Churrasco

135. Churrasco Evolution Radar Bug’s Life BugCrawler Pendolino
MSR 2006
1 Change coupling
analysis
WCRE 2006
TSE 2009
2 Bug evolution
analysis
VISSOFT
2007
3 Code-bug
co-evolution analysis
CSMR 2006
4 Bug prediction MSR 2010
5 Bug prediction with
change coupling
WCRE 2009
6 Software quality
analysis
QSIC 2010
7 Bug prediction with
e-mails
FASE 2010
WASDeTT 2008 MSR Challenge CSMR 2007

136. Ph.D.
Conclusion
swamp of
procrastination
x
peaks of tools
madness
x
Thesis
Analysis
x
techniques x Tool
support
We are here haunted teachwood
forest

137. Ph.D.
Conclusion
swamp of
procrastination
x
peaks of tools
madness
x
Thesis
Analysis
x
techniques x Tool
support
We are here haunted teachwood
forest

138. Ph.D.
Intermezzo
x Conclusion
swamp of
procrastination
x
peaks of tools
madness
x
Thesis
Analysis
x
techniques x Tool
support
We are here haunted teachwood
forest

139. Replicating
Experiments
Bug prediction benchmar k
Models available through
Churrasco web interface

140. Recent annotations
added
User
SVG
Interactive
Visualization
People participating
to the collaboration
Report generator
Selected figure
Context menu
Selected figure
information
Metrics mapping
configurator
Package selector
Regular expression
matcher
Collaboration in Churrasco STTT 2010

141. Ph.D.
Intermezzo
x Conclusion
swamp of
procrastination
x
peaks of tools
madness
x
Thesis
Analysis
x
techniques x Tool
support
We are here haunted teachwood
forest

142. Ph.D.
Intermezzo
x Conclusion
swamp of
procrastination
x
peaks of tools
madness
x
Thesis
Analysis
x
techniques x Tool
support
We are here haunted teachwood
forest

143. Ph.D.
Intermezzo
x Conclusion
swamp of
procrastination
x
peaks of tools
madness
x
Thesis
Analysis
x
techniques x Tool
support
We are here haunted teachwood
forest

144. Ph.D.
Intermezzo
x Conclusion
swamp of
procrastination
x
peaks of tools
madness
x
Thesis
Analysis
x
techniques x Tool
support
We are here haunted teachwood
forest

153. Deve
lop
er
neo
Limitations

154. User studies Other languages
Deve
lop
er
neo
Limitations
More case studies

155. User studies Other languages
Deve
lop
er
neo
Limitations
More case studies
Future Work

156. User studies Other languages
Deve
lop
er
neo
Limitations
Exploit author data More case studies
Future Work

157. User studies Other languages
Deve
lop
er
neo
Limitations
Exploit author data More case studies
for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
Extend the Combine
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
meta-model techniques Future Work
Mevo

158. for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
HOLISTIC
SOFTWARE
EVOLUTION
MORE
REPOSITORIES AHEAD

159. for(int j=m; j>i; j--){
uCJM1= dataUC[j-1];
uCJ= dataUC[j];
if(uCJM1.compare(z)>
{ /* exchange */
tempStr= data[j-1];
/* sort the data */
data[j-1]= data[j];
data[j]= tempStr;
dataUC[j-1]= uCJ;
dataUC[j]= uCJM1;
}
}
HOLISTIC
SOFTWARE Holistic
EVOLUTION
software
MORE
REPOSITORIES AHEAD
evolution

160. Ph.D.
Intermezzo
x Conclusion
swamp of
procrastination
x
peaks of tools
madness
x
Thesis
Analysis
x
techniques x Tool
support
We are here haunted teachwood
forest

161. Ph.D.
Intermezzo
x Conclusion
swamp of
procrastination
x
peaks of tools
madness
x
Thesis
Analysis
x
techniques x Tool
support
We are here haunted teachwood
forest

162. Journal papers Conference papers
1. On Porting Software Visualization Tools to the Web 1. On the Impact of Design Flaws on Software Defects
Marco D'Ambros, Michele Lanza, Mircea Lungu, Romain Robbes
Marco D'Ambros, Alberto Bacchelli, Michele Lanza
In Software Tools for Technology Transfer (STTT), Springer, 2010.
In Proceedings of QSIC 2010, pp. 23-31.
2. Distributed and Collaborative Software Evolution Analysis with
Churrasco 2. An Extensive Comparison of Bug Prediction Approaches
Marco D'Ambros, Michele Lanza Marco D'Ambros, Michele Lanza, Romain Robbes
In Journal of Science of Computer Programming (SCP),Vol. 75. No. 4, pp. 276-287. In Proceedings of MSR 2010, pp. 31-41.
Elsevier, 2010.
3. Are Popular Classes More Defect Prone?
3. Visualizing Co-Change Information with the Evolution Radar Alberto Bacchelli, Marco D'Ambros, Michele Lanza
Marco D'Ambros, Michele Lanza, Mircea Lungu In Proceedings of FASE 2010, pp. 59-73.
In IEEE Transactions on Software Engineering (TSE),Vol. 35. No. 5, pp. 720-735. IEEE CS
4. On the Relationship Between Change Coupling and Software Defects
Press, 2009.
Marco D'Ambros and Michele Lanza and Romain Robbes
4. Visual Software Evolution Reconstruction In Proceedings of WCRE 2009, pp. 135-144.
Marco D'Ambros, Michele Lanza
5. Promises and Perils of Porting Software Visualization Tools to the Web
In Journal on Software Maintenance and Evolution: Research and Practice (JSME),Vol.21,
Marco D'Ambros, Mircea Lungu, Michele Lanza, Romain Robbes
No.3, pp. 217-232, May 2009. John Wiley & Sons, 2009.
In Proceedings of WSE 2009, pp. 109-118.
6. A Flexible Framework to Support Collaborative Software Evolution Analysis
Marco D'Ambros, Michele Lanza
In Proceedings of CSMR 2008, pp. 3-12.
7. Reverse Engineering with Logical Coupling
Other publications Marco D'Ambros, Michele Lanza
In Proceedings of WCRE 2006, pp. 189-198.
8. Software Bugs and Evolution: A Visual Approach to Uncover Their Relationships
1. Supporting Software Evolution Analysis with Historical Dependencies and
Marco D'Ambros, Michele Lanza
Defect Information
In Proceedings of CSMR 2006, pp. 227-236.
Marco DʼAmbros
In Proceedings of ICSM 2008, pp. 412-415.
2. The Metabase: Generating Object Persistency Using Meta Descriptions
Marco D'Ambros, Michele Lanza, Martin Pinzger
In Proceedings of FAMOOSr 2007.
3. BugCrawler: Visualizing Evolving Software Systems
Workshop papers
Marco D'Ambros, Michele Lanza
1. Churrasco: Supporting Collaborative Software Evolution Analysis
In Proceedings of CSMR 2007, pp. 333-334.
Marco D'Ambros, Michele Lanza
4. Applying the Evolution Radar to PostgreSQL In Proceedings of WASDeTT 2008, 2008.
Marco D'Ambros, Michele Lanza
2. "A Bug's Life" - Visualizing a Bug Database
In Proceedings of MSR 2006, pp. 177-178, 2006.
Marco D'Ambros, Michele Lanza, Martin Pinzger
In Proceedings of VISSOFT 2007, pp. 113-120.
3. The Evolution Radar: Visualizing Integrated Logical Coupling Information
Marco D'Ambros, Michele Lanza, Mircea Lungu
In Proceedings of MSR 2006, pp. 26-32.