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Kristian Schwienbacher
Custom error and event tracking for
Ember.js apps
“Identification of Bug-Inducing
Commits Based on User Activity”
Kristian Schwienbacher
Free University of Bolzano - Bachelor in Computer Science
Bachelor Thesis
PROBLEM SOLUTION
EVALUATION
Comparison with the SZZ Algorithm
PROBLEM STATEMENT
Implementation of the System
Outline
DISCUSSION
01
02
03
04
Goal of the project and Requirements of the System
Limitations and Scope of Application
PROBLEM STATEMENT
02
● Errors inside running applications must be tracked
● Interactions of users are logged in form of
stack-traces
● Error-log are analyzed to determine bug-inducing
commit
● Comparison with traditional bug-localization
algorithm
PROBLEM STATEMENT
Phase 1
SZZ Algorithm
Bug-fixing
commits git blame
bug-introducing
commits candidates
● Sliwersky, Zimmermann, Zeller (2005)
● Find bug-fixing commits
● Trace back to bug-introducing commit
PROBLEM SOLUTION
03
PROBLEM SOLUTION
System Architecture
PROBLEM SOLUTION
Error Tracking Addon
● Catches all errors occurring during run-time
● Adds event listeners for pre-defined user events
● When error occurs, log is sent to API-endpoint
● Distributed as an open source NPM package
PROBLEM SOLUTION
Measurement Server
● initial state of application repository is analyzed
○ SZZ algorithm
○ average bug-fixing time
● logging endpoint
○ analyzes stack-traces to determine
bug-inducing-commit
○ GitHub Issues
EVALUATION
04
EVALUATION
Data Exploration
Weekday distribution of bug-introducing commits
EVALUATION
Data Exploration
Weekday distribution of bug-fixing commits
EVALUATION
Data Exploration
Bug introducing commit message analysis
CASE STUDIES
4.1
EVALUATION
Case Study I
● Bug: no plans would be available when existing
customer tried to upgrade one of their applications
● Fixing Commit: 92 line modifications, 7 files
● SZZ: pointed to commit made 17 days prior to bugfix
● Error Logger: bug was introduced 74 days before
being fixed
DISCUSSION
05
Discussion
● The majority of the bugs do not generate any error
message
● SZZ algorithm can be applied on most bugs
● Approach of this project only applicable when stack
traces are generated
● When stack trace is available this approach was
determined to be more accurate in the case studies
References
● T. Zimmermann J. Sliwerski and A. Zeller. When do changes induce
fixes? In Proceedings of International Workshop on Mining Software
Repositories, pages 24–28, Saint Louis, Missouri, USA, 2005.
● Sunghun Kim, Thomas Zimmermann, Kai Pan, and E. James Whitehead Jr.
Automatic identification of bug introducing changes. In Proceedings of the 21st
IEEE/ACM International Conference on Automated Software Engineering, pages
81–90, September 2006.
● Kim Herzig, Sascha Just, and Andreas Zeller. It’s not a bug, it’s a feature: How
misclassification impacts bug prediction. In Proceedings of the 2013
International Conference on Software Engineering, ICSE ’13, pages 392–401,
Piscataway, NJ, USA, 2013. IEEE Press
● Gema Rodríguez-Pérez, Gregorio Robles, and Jesús M González-Barahona.
Reproducibility and Credibility in Empirical Software Engineering: A Case Study
based on a Systematic Literature Review of the use of the SZZ algorithm.
Information and Software Technology
Kristian Schwienbacher
Custom error and event tracking for
Ember.js apps

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SFSCon19 - Kristian Schwienbacher - Custom error and event tracking for Ember js apps

  • 1. Kristian Schwienbacher Custom error and event tracking for Ember.js apps
  • 2. “Identification of Bug-Inducing Commits Based on User Activity” Kristian Schwienbacher Free University of Bolzano - Bachelor in Computer Science Bachelor Thesis
  • 3. PROBLEM SOLUTION EVALUATION Comparison with the SZZ Algorithm PROBLEM STATEMENT Implementation of the System Outline DISCUSSION 01 02 03 04 Goal of the project and Requirements of the System Limitations and Scope of Application
  • 5. ● Errors inside running applications must be tracked ● Interactions of users are logged in form of stack-traces ● Error-log are analyzed to determine bug-inducing commit ● Comparison with traditional bug-localization algorithm PROBLEM STATEMENT
  • 6. Phase 1 SZZ Algorithm Bug-fixing commits git blame bug-introducing commits candidates ● Sliwersky, Zimmermann, Zeller (2005) ● Find bug-fixing commits ● Trace back to bug-introducing commit
  • 9. PROBLEM SOLUTION Error Tracking Addon ● Catches all errors occurring during run-time ● Adds event listeners for pre-defined user events ● When error occurs, log is sent to API-endpoint ● Distributed as an open source NPM package
  • 10. PROBLEM SOLUTION Measurement Server ● initial state of application repository is analyzed ○ SZZ algorithm ○ average bug-fixing time ● logging endpoint ○ analyzes stack-traces to determine bug-inducing-commit ○ GitHub Issues
  • 13.
  • 17. EVALUATION Case Study I ● Bug: no plans would be available when existing customer tried to upgrade one of their applications ● Fixing Commit: 92 line modifications, 7 files ● SZZ: pointed to commit made 17 days prior to bugfix ● Error Logger: bug was introduced 74 days before being fixed
  • 19. Discussion ● The majority of the bugs do not generate any error message ● SZZ algorithm can be applied on most bugs ● Approach of this project only applicable when stack traces are generated ● When stack trace is available this approach was determined to be more accurate in the case studies
  • 20. References ● T. Zimmermann J. Sliwerski and A. Zeller. When do changes induce fixes? In Proceedings of International Workshop on Mining Software Repositories, pages 24–28, Saint Louis, Missouri, USA, 2005. ● Sunghun Kim, Thomas Zimmermann, Kai Pan, and E. James Whitehead Jr. Automatic identification of bug introducing changes. In Proceedings of the 21st IEEE/ACM International Conference on Automated Software Engineering, pages 81–90, September 2006. ● Kim Herzig, Sascha Just, and Andreas Zeller. It’s not a bug, it’s a feature: How misclassification impacts bug prediction. In Proceedings of the 2013 International Conference on Software Engineering, ICSE ’13, pages 392–401, Piscataway, NJ, USA, 2013. IEEE Press ● Gema Rodríguez-Pérez, Gregorio Robles, and Jesús M González-Barahona. Reproducibility and Credibility in Empirical Software Engineering: A Case Study based on a Systematic Literature Review of the use of the SZZ algorithm. Information and Software Technology
  • 21. Kristian Schwienbacher Custom error and event tracking for Ember.js apps