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ICST 2020
Sat 24 - Wed 28 October 2020 Porto, Portugal
Sun 25 Oct 2020 09:15 - 09:45 at Infante - RT1 - Fault Localization and Debugging Infante Chair(s): Andreas Zeller
Sun 25 Oct 2020 20:15 - 20:45 at Infante - RT1 - Fault Localization and Debugging Infante Chair(s): Jose Campos

Fault localization and repair are time-consuming and tedious. There is a significant and growing need for automated techniques to support such tasks. Despite significant progress in this area, existing fault localization techniques are not widely applied in practice yet and their effectiveness varies greatly from case to case. Existing work suggests new algorithms and ideas as well as adjustments to the test suites to improve the effectiveness of automated fault localization. However, important questions remain open: Why is the effectiveness of these techniques so unpredictable? What are the factors that influence the effectiveness of fault localization? Can we accurately predict fault localization effectiveness? In this paper, we try to answer these questions by collecting 70 static, dynamic, test suite, and fault-related metrics that we hypothesize are related to effectiveness. Our analysis shows that a combination of only a few static, dynamic, and test metrics enables the construction of a prediction model with excellent discrimination power between levels of effectiveness (eight metrics yielding an AUC of .86; fifteen metrics yielding an AUC of.88). The model hence yields a practically useful confidence factor that can be used to assess the potential effectiveness of fault localization. Given that the metrics are the most influential metrics explaining the effectiveness of fault localization, they can also be used as a guide for corrective actions on code and test suites leading to more effective fault localization.

Sun 25 Oct

Displayed time zone: Lisbon change

09:15 - 10:45
RT1 - Fault Localization and Debugging InfanteResearch Papers at Infante +11h
Chair(s): Andreas Zeller CISPA, Germany
09:15
30m
Talk
Can We Predict the Quality of Spectrum-based Fault Localization?
Research Papers
Mojdeh Golagha Technical University of Munich, Alexander Pretschner Technical University of Munich, Lionel Briand University of Luxembourg, University of Ottawa
Link to publication DOI
09:45
30m
Talk
Substate Profiling for Enhanced Fault Detection and Localization: An Empirical Study
Research Papers
Rawad Abou Assi American University of Beirut, Wes Masri American University of Beirut, Chadi Trad American University of Beirut
Link to publication DOI
10:15
30m
Talk
More Accurate Dynamic Slicing for Better Supporting Software Debugging
Research Papers
Xiangyu Li Georgia Institute of Technology, Alessandro Orso Georgia Tech
Link to publication DOI
20:15 - 21:45
RT1 - Fault Localization and Debugging InfanteResearch Papers at Infante
Chair(s): Jose Campos University of Lisbon, Portugal
20:15
30m
Talk
Can We Predict the Quality of Spectrum-based Fault Localization?
Research Papers
Mojdeh Golagha Technical University of Munich, Alexander Pretschner Technical University of Munich, Lionel Briand University of Luxembourg, University of Ottawa
Link to publication DOI
20:45
30m
Talk
Substate Profiling for Enhanced Fault Detection and Localization: An Empirical Study
Research Papers
Rawad Abou Assi American University of Beirut, Wes Masri American University of Beirut, Chadi Trad American University of Beirut
Link to publication DOI
21:15
30m
Talk
More Accurate Dynamic Slicing for Better Supporting Software Debugging
Research Papers
Xiangyu Li Georgia Institute of Technology, Alessandro Orso Georgia Tech
Link to publication DOI