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Just-In-Time Bug Prediction in Mobile Applications:
The Domain Matters!
Gemma Catolino - University of Salerno
Student Research
Competition
complexity
of mobile apps
is increasing
monitoring
the quality
is necessary
The mechanism that allows users to rate releases of the apps
has meant that users appear as quality assurance managers
APP
RELEASED
The mechanism that allows users to rate releases of the apps
has meant that users appear as quality assurances
LITERATURE
LITERATURE
metrics for
usability
LITERATURE
metrics for
usability
adapt standard
metrics
OO METRICS
COSMIC
No studies address the issue of bug prediction
within the mobile context
Bugs may produce a decreasing of the reputation of
mobile apps
Just-In-Time Bug Prediction
BUG PREDICTION JIT BUG PREDICTION
SYSTEM
RELEASE COMMIT
KAMEI ET AL. METRICS
Empirically evaluate the usefulness of the metrics
proposedby Kamei et al. for JIT bug prediction in the
context of the mobile app development
Kamei,Yasutaka, et al. "A large-scale empirical study of just-in-time quality assurance." IEEETransactions on Software Engineering
KAMEI ET AL. METRICS
SIZE
Line of code added
Line of code
deleted
Line of code in file
before the change
Kamei,Yasutaka, et al. "A large-scale empirical study of just-in-time quality assurance." IEEETransactions on Software Engineering
KAMEI ET AL. METRICS
SIZE EXPERIENCE
Line of code added
Line of code
deleted
Line of code in file
before the change
Developer experience
Recent developer experience
Developer experience on a subsystem
Kamei,Yasutaka, et al. "A large-scale empirical study of just-in-time quality assurance." IEEETransactions on Software Engineering
KAMEI ET AL. METRICS
SIZE
PURPOSE
EXPERIENCE
Line of code added
Line of code
deleted
Line of code in file
before the change
Fix
Developer experience
Recent developer experience
Developer experience on a subsystem
Kamei,Yasutaka, et al. "A large-scale empirical study of just-in-time quality assurance." IEEETransactions on Software Engineering
KAMEI ET AL. METRICS
SIZE
DIFFUSION
PURPOSE
EXPERIENCE
#modified subsystems
#modified directories
#modified file
Entropy
Line of code added
Line of code
deleted
Line of code in file
before the change
Fix
Developer experience
Recent developer experience
Developer experience on a subsystem
Kamei,Yasutaka, et al. "A large-scale empirical study of just-in-time quality assurance." IEEETransactions on Software Engineering
KAMEI ET AL. METRICS
SIZE
DIFFUSION HISTORY
PURPOSE
EXPERIENCE
#modified subsystems
#modified directories
#modified file
Entropy
Line of code added
Line of code
deleted
Line of code in file
before the change
Fix
Developer experience
Recent developer experience
Developer experience on a subsystem
#developers that changed the modified files
Average time interval between the last and current change
Number of unique changes to modified files
Kamei,Yasutaka, et al. "A large-scale empirical study of just-in-time quality assurance." IEEETransactions on Software Engineering
EMPIRICAL STUDY
Evaluate the performance of prediction model built using the metrics
proposed by Kamei et al. in the context of mobile applications
GOAL
5 open source
mobile Android
applications
CONTEXT
Evaluate the performance of prediction model built using the metrics
proposed by Kamei et al. in the context of mobile applications
AnySoftKeyboard
ChatSecure
Owncloud
Worpress
Afwall
5 open source
mobile Android
applications
Metric values for apps
extracted from
COMMIT GURU PLATFORM
DATA
Evaluate the performance of prediction model built using the metrics
proposed by Kamei et al. in the context of mobile applications
COMMITx m1 m4….m2 m3
BUGGY
NON-BUGGY
SYSTEM X
BUILD MODEL PREDICTION
5 open source
mobile Android
applications
Metric values for apps
extracted from
COMMIT GURU PLATFORM
Apply a feature selection
algorithm in order
to avoid multi-collinearity
Evaluate the performance of prediction model built using the metrics
proposed by Kamei et al. in the context of mobile applications
PREDICTORS
WRAPPER TECHNIQUE
PREDICTORS
14 METRICS
WRAPPER TECHNIQUE
PREDICTORS
14 METRICS
WRAPPER TECHNIQUE
PREDICTORS
14 METRICS
WRAPPER TECHNIQUE
5 METRICS
# Modified subsystems
# Added lines# Lines of code before the commit
# Developers working on the committed files in the last month
experience of the committer
BUILD MODEL PREDICTION
5 open source
mobile Android
applications
Metric values for apps
extracted from
COMMIT GURU PLATFORM
Logistic Regression
10-folds CrossValidation
Apply a feature selection
algorithm in order
to avoid multi-collinearity
Evaluate the performance of prediction model built using the metrics
proposed by Kamei et al. in the context of mobile applications
EVALUATION METRICS
5 open source
mobile Android
applications
Metric values for apps
extracted from
COMMIT GURU PLATFORM
Accuracy, Precision, Recall, F-Measure, AUC-ROC
Apply a feature selection
algorithm in order
to avoid multi-collinearity
Logistic Regression
10-folds CrossValidation
Evaluate the performance of prediction model built using the metrics
proposed by Kamei et al. in the context of mobile applications
RESULTS
0
25
50
75
100
AnySoftKeyborad Afwall ChatSecure Owncloud WordPress
F-MEASURE
0
25
50
75
100
AnySoftKeyborad Afwall ChatSecure Owncloud WordPress
0
25
50
75
100
AnySoftKeyborad Afwall ChatSecure Owncloud WordPress
F-MEASURE
0
25
50
75
100
AnySoftKeyborad Afwall ChatSecure Owncloud WordPress
0
25
50
75
100
AnySoftKeyborad Afwall ChatSecure Owncloud WordPress
F-MEASURE
34%
AVERAGE
PRECISION
RECALL
76%
28%
ACCURACY
58%
Model is able to discover only a limited number of real bugs
occurring in the subject apps, thus providing a limited
usefulness to developers
POSSIBLE CAUSES
Developers’ experience
Mobile apps can be developed by both
expert and novice developers, thus
possibly creating confounding effects
with respect to the computed metrics
POSSIBLE CAUSES
Development Process
The release process of mobile apps is
shorter than the one of traditional
applications: this implies that the source
code change continuously
POSSIBLE CAUSES
# Lines of code before the commit
May not be significant in the mobile context
POSSIBLE CAUSES
SIZE
APP APP
POSSIBLE CAUSES
#modified subsystems
May not make sense for mobile apps
POSSIBLE CAUSES
#modified subsystems
May not make sense for mobile apps
POSSIBLE CAUSES
FUTURE DIRECTION
Extend study
with more systems
Make a Survey
with Developers
Definition of
new metrics
Just-In-Time Bug Prediction in Mobile Applications:
The Domain Matters!
Gemma Catolino - University of Salerno
Student Research
Competition

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Just-in-Time Bug Prediction in Mobile Applications: The Domain Matters!

  • 1. Just-In-Time Bug Prediction in Mobile Applications: The Domain Matters! Gemma Catolino - University of Salerno Student Research Competition
  • 4. The mechanism that allows users to rate releases of the apps has meant that users appear as quality assurance managers
  • 5. APP RELEASED The mechanism that allows users to rate releases of the apps has meant that users appear as quality assurances
  • 9. No studies address the issue of bug prediction within the mobile context
  • 10. Bugs may produce a decreasing of the reputation of mobile apps
  • 12. BUG PREDICTION JIT BUG PREDICTION SYSTEM RELEASE COMMIT
  • 13. KAMEI ET AL. METRICS Empirically evaluate the usefulness of the metrics proposedby Kamei et al. for JIT bug prediction in the context of the mobile app development Kamei,Yasutaka, et al. "A large-scale empirical study of just-in-time quality assurance." IEEETransactions on Software Engineering
  • 14. KAMEI ET AL. METRICS SIZE Line of code added Line of code deleted Line of code in file before the change Kamei,Yasutaka, et al. "A large-scale empirical study of just-in-time quality assurance." IEEETransactions on Software Engineering
  • 15. KAMEI ET AL. METRICS SIZE EXPERIENCE Line of code added Line of code deleted Line of code in file before the change Developer experience Recent developer experience Developer experience on a subsystem Kamei,Yasutaka, et al. "A large-scale empirical study of just-in-time quality assurance." IEEETransactions on Software Engineering
  • 16. KAMEI ET AL. METRICS SIZE PURPOSE EXPERIENCE Line of code added Line of code deleted Line of code in file before the change Fix Developer experience Recent developer experience Developer experience on a subsystem Kamei,Yasutaka, et al. "A large-scale empirical study of just-in-time quality assurance." IEEETransactions on Software Engineering
  • 17. KAMEI ET AL. METRICS SIZE DIFFUSION PURPOSE EXPERIENCE #modified subsystems #modified directories #modified file Entropy Line of code added Line of code deleted Line of code in file before the change Fix Developer experience Recent developer experience Developer experience on a subsystem Kamei,Yasutaka, et al. "A large-scale empirical study of just-in-time quality assurance." IEEETransactions on Software Engineering
  • 18. KAMEI ET AL. METRICS SIZE DIFFUSION HISTORY PURPOSE EXPERIENCE #modified subsystems #modified directories #modified file Entropy Line of code added Line of code deleted Line of code in file before the change Fix Developer experience Recent developer experience Developer experience on a subsystem #developers that changed the modified files Average time interval between the last and current change Number of unique changes to modified files Kamei,Yasutaka, et al. "A large-scale empirical study of just-in-time quality assurance." IEEETransactions on Software Engineering
  • 20. Evaluate the performance of prediction model built using the metrics proposed by Kamei et al. in the context of mobile applications GOAL
  • 21. 5 open source mobile Android applications CONTEXT Evaluate the performance of prediction model built using the metrics proposed by Kamei et al. in the context of mobile applications
  • 23. 5 open source mobile Android applications Metric values for apps extracted from COMMIT GURU PLATFORM DATA Evaluate the performance of prediction model built using the metrics proposed by Kamei et al. in the context of mobile applications COMMITx m1 m4….m2 m3 BUGGY NON-BUGGY SYSTEM X
  • 24. BUILD MODEL PREDICTION 5 open source mobile Android applications Metric values for apps extracted from COMMIT GURU PLATFORM Apply a feature selection algorithm in order to avoid multi-collinearity Evaluate the performance of prediction model built using the metrics proposed by Kamei et al. in the context of mobile applications
  • 28. PREDICTORS 14 METRICS WRAPPER TECHNIQUE 5 METRICS # Modified subsystems # Added lines# Lines of code before the commit # Developers working on the committed files in the last month experience of the committer
  • 29. BUILD MODEL PREDICTION 5 open source mobile Android applications Metric values for apps extracted from COMMIT GURU PLATFORM Logistic Regression 10-folds CrossValidation Apply a feature selection algorithm in order to avoid multi-collinearity Evaluate the performance of prediction model built using the metrics proposed by Kamei et al. in the context of mobile applications
  • 30. EVALUATION METRICS 5 open source mobile Android applications Metric values for apps extracted from COMMIT GURU PLATFORM Accuracy, Precision, Recall, F-Measure, AUC-ROC Apply a feature selection algorithm in order to avoid multi-collinearity Logistic Regression 10-folds CrossValidation Evaluate the performance of prediction model built using the metrics proposed by Kamei et al. in the context of mobile applications
  • 32. 0 25 50 75 100 AnySoftKeyborad Afwall ChatSecure Owncloud WordPress F-MEASURE
  • 33. 0 25 50 75 100 AnySoftKeyborad Afwall ChatSecure Owncloud WordPress 0 25 50 75 100 AnySoftKeyborad Afwall ChatSecure Owncloud WordPress F-MEASURE
  • 34. 0 25 50 75 100 AnySoftKeyborad Afwall ChatSecure Owncloud WordPress 0 25 50 75 100 AnySoftKeyborad Afwall ChatSecure Owncloud WordPress F-MEASURE 34% AVERAGE
  • 36. Model is able to discover only a limited number of real bugs occurring in the subject apps, thus providing a limited usefulness to developers
  • 38. Developers’ experience Mobile apps can be developed by both expert and novice developers, thus possibly creating confounding effects with respect to the computed metrics POSSIBLE CAUSES
  • 39. Development Process The release process of mobile apps is shorter than the one of traditional applications: this implies that the source code change continuously POSSIBLE CAUSES
  • 40. # Lines of code before the commit May not be significant in the mobile context POSSIBLE CAUSES
  • 42. #modified subsystems May not make sense for mobile apps POSSIBLE CAUSES
  • 43. #modified subsystems May not make sense for mobile apps POSSIBLE CAUSES
  • 44. FUTURE DIRECTION Extend study with more systems Make a Survey with Developers Definition of new metrics
  • 45. Just-In-Time Bug Prediction in Mobile Applications: The Domain Matters! Gemma Catolino - University of Salerno Student Research Competition