Gemma Catolino, Fabio Palomba, Andrea De Lucia, Filomena Ferrucci, Andy Zaidman Paper accepted at 25th International Conference on Program Comprehension 2017 - Buenos Aires, Argentina FULL PAPER: https://tinyurl.com/ktl9qvn

1. Developer-Related Factors in Change
Prediction:An Empirical Assessment
May 22 and 23
Buenos Aires - Argentina
Gemma Catolino*, Fabio Palomba°, Andrea De Lucia*,
Filomena Ferrucci*, Andy Zaidman°
*University of Salerno, °Delft University of Technology

2. CHANGE is unavoidable

3. Knowing in advancethe code elements potentially exhibiting
a higher change-proneness is crucialfor developers

4. CHANGE
PREDICTION
Effective technique to identify the classes more
prone to be modified in the future

5. PREDICTION
CHANGE
LITERATURE

6. PREDICTION
CHANGE
Factors influencing
the phenomenon
LITERATURE

7. PREDICTION
CHANGE
Factors influencing
the phenomenon
Product and
evolution
metrics influencing
the phenomenon
LITERATURE

8. PREDICTION
CHANGE
Factors influencing
the phenomenon
Devising
Prediction Models
Product and
evolution
metrics influencing
the phenomenon
LITERATURE

9. Developer-related factors

10. non-focused developers could introduce maintainability
pitfalls that lead to increase the change-proneness of the
modified classes

11. Developer-factor can be a useful source of information
to predict classes more likely to be changed in the future

12. BASIC CODE
CHANGE MODEL
Entropy of changes
applied by
developers in a given
time period
Developer-related factors

13. DEVELOPER
CHANGE BASED
MODEL
Developers structural
and
semantic scattering
Developer-related factors
BASIC CODE
CHANGE MODEL
Entropy of changes
applied by
developers in a given
time period

14. DEVELOPER
MODEL
Number of developers
who modified a code
component in a give
time period
Developer-related factors
DEVELOPER
CHANGE BASED
MODEL
Developers structural
and
semantic scattering
BASIC CODE
CHANGE MODEL
Entropy of changes
applied by
developers in a given
time period

15. Developer-related factors
DEVELOPER
MODEL
Number of developers
who modified a code
component in a give
time period
DEVELOPER
CHANGE BASED
MODEL
Developers structural
and
semantic scattering
BASIC CODE
CHANGE MODEL
Entropy of changes
applied by
developers in a given
time period

16. STUDY
EMPIRICAL

17. Performanceof developer-based prediction models
GOAL

18. Performance comparedwith the state of the art
and complementarity
GOAL
Performanceof developer-based prediction models

19. GOAL
Performance comparedwith the state of the art
and complementarity
Elish model Zhou model
EM CM

20. 10 SYSTEMS

21. ORACLE

22. DISTRIBUTION OF CHANGES OF ALL CLASSES OF A SYSTEM
0 N
ORACLE

23. DISTRIBUTION OF CHANGES OF ALL CLASSES OF A SYSTEM
0 N
C1
ORACLE

24. DISTRIBUTION OF CHANGES OF ALL CLASSES OF A SYSTEM
0 N
C1
t
ORACLE

25. DISTRIBUTION OF CHANGES OF ALL CLASSES OF A SYSTEM
0 N
NUMBER OF CHANGES
MEDIAN
>=
CHANGE PRONE
ORACLE

26. DISTRIBUTION OF CHANGES OF ALL CLASSES OF A SYSTEM
0 N
NUMBER OF CHANGES
MEDIAN
<
NON-CHANGE PRONE
ORACLE

27. EVALUATION
METRICS

28. EVALUATION
METRICS
Precision
Recall
F-Mesure
AUC-ROC
Overlap

29. EVALUATION
METRICS
OVERLAP
Mi Mj

30. EVALUATION
METRICS
OVERLAP
Mi Mj
% of true positives correctly identified by both models

31. EVALUATION
METRICS
OVERLAP
Mi Mj
% of true positives correctly identified by both models
% change-prone classes correctly classified by Mi only and missed by Mj

32. CLASSIFIER
VALIDATION TECHNIQUES

33. TIME WINDOWS
CLASSIFIER
VALIDATION TECHNIQUES
LOGISTIC REGRESSION

34. TIME WINDOWS
CLASSIFIER
VALIDATION TECHNIQUES

35. TIME WINDOWS
CLASSIFIER
VALIDATION TECHNIQUES
TW1
HISTORY OF THE SYSTEM DIVIDED IN TIME PERIOD OF THREE MONTHS
TW2 TW3

36. TIME WINDOWS
CLASSIFIER
VALIDATION TECHNIQUES
HISTORY OF THE SYSTEM DIVIDED IN TIME PERIOD OF THREE MONTHS
WE TRAIN THE MODEL ON
TW1 TW2 TW3

37. TIME WINDOWS
CLASSIFIER
VALIDATION TECHNIQUES
HISTORY OF THE SYSTEM DIVIDED IN TIME PERIOD OF THREE MONTHS
TO TEST THE PERFORMANCE OF
TW1 TW2 TW3

38. TIME WINDOWS
CLASSIFIER
VALIDATION TECHNIQUES
HISTORY OF THE SYSTEM DIVIDED IN TIME PERIOD OF THREE MONTHS
TW1 TW2 TW3

39. TIME WINDOWS
CLASSIFIER
VALIDATION TECHNIQUES
HISTORY OF THE SYSTEM DIVIDED IN TIME PERIOD OF THREE MONTHS
TW1 TW2 TW3

40. RESULTS

41. 0
25
50
75
100
BCCM DCBM DM
Performanceof developer-based prediction models
F-MEASUREOVERALL

42. class org.gjt.sp.BufferHistory
JEDIT
AUGUST OCTOBER
19 CHANGES BY ONE DEVELOPER!!!!
2009

43. DEVELOPER MODEL
NON-CHANGE PRONE
JEDIT
AUGUST OCTOBER
19 CHANGES BY ONE DEVELOPER!!!!
class org.gjt.sp.BufferHistory

44. 36 MODIFICATIONS
OVER DIFFERENT PACKAGE!!!
High level of both semantic and structural scattering!
AUGUST OCTOBER
JEDIT
class org.gjt.sp.BufferHistory

45. 36 MODIFICATIONS
OVER DIFFERENT PACKAGE!!!
AUGUST OCTOBER
JEDIT
class org.gjt.sp.BufferHistory
DEVELOPER CHANGE BASED MODEL
CHANGE PRONE

46. 0
25
50
75
100
BCCM DCBM DM CM EM
Performanceof developer-based prediction models
F-MEASUREOVERALL

47. 0
25
50
75
100
BCCM DCBM DM CM EM
0
25
50
75
100
BCCM DCBM DM CM EM
Performance comparedwith the state of the art
and complementarity
F-MEASUREOVERALL

48. OVERLAP
DCBM VS EM
DCBM∩EM
EM-DCBM
DCBM-EM

49. svg.io.SVGFigureFactory
RESPONSIBLE FOR PERFORMING THE
MAIN FUNCTIONALITY OF ENTIRE THE PROJECT!
JHOTDRAW
FREQUENTLY MODIFIED!

50. svg.io.SVGFigureFactory
RESPONSIBLE FOR PERFORMING THE
MAIN FUNCTIONALITY OF ENTIRE THE PROJECT!
JHOTDRAW
EVOLUTION MODEL
CHANGE PRONE

51. APACHE-ANT
DECEMBER FEBRAURY
ENTIRE RESTRUCTURING OF THE SYSTEM!
2006

52. APACHE-ANT
DECEMBER FEBRAURY
ENTIRE DESTRUCTING OF THE SYSTEM!
ELIMINATION OF
OLD CLASSES
REDISTRIBUSTION
OF RESPONSIBILITIES
2006

53. APACHE-ANT
DECEMBER FEBRAURY
ENTIRE DESTRUCTING OF THE SYSTEM!
EVOLUTION MODEL
2006

54. DBCM +8%F-MEASURE

55. Developer-based models work better
DBCM +8%F-MEASURE
than existing models

56. DBCM +8%F-MEASURE
Developer-based models work better
than existing models
Highly complementarybetween models

57. Combination
of these factors
Relation between “changy”
and buggy classes

58. Developer-Related Factors in Change
Prediction:An Empirical Assessment
May 22 and 23
Buenos Aires - Argentina
Gemma Catolino*, Fabio Palomba°, Andrea De Lucia*,
Filomena Ferrucci*, Andy Zaidman°
*University of Salerno, °Delft University of Technology