Reference : Improving Fault Localization for Simulink Models using Search-Based Testing and Predi...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/29076
Improving Fault Localization for Simulink Models using Search-Based Testing and Prediction Models
English
Liu, Bing mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Lucia, Lucia mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)]
Nejati, Shiva mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Briand, Lionel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
2017
24th IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2017)
Yes
International
24th IEEE International Conference on Software Analysis, Evolution, and Reengineering
21-02-2017 to 24-02-2017
Klagenfurt
Austria
[en] Fault localization ; Simulink models ; search-based testing
[en] One promising way to improve the accuracy of fault localization based on statistical debugging is to increase diversity among test cases in the underlying test suite. In many practical situations, adding test cases is not a cost-free option because test oracles are developed manually or running test cases is expensive. Hence, we require to have test suites that are both diverse and small to improve debugging. In this paper, we focus on improving fault localization of Simulink models by generating test cases. We identify three test objectives that aim to increase test suite diversity. We use these objectives in a search-based algorithm to generate diversified but small test suites. To further minimize test suite sizes, we develop a prediction model to stop test generation when adding test cases is unlikely to improve fault localization. We evaluate our approach using three industrial subjects. Our results show (1) the three selected test objectives are able to significantly improve the accuracy of fault localization for small test suite sizes, and (2) our prediction model is able to maintain almost the same fault localization accuracy while reducing the average number of newly generated test cases by more than half.
http://hdl.handle.net/10993/29076
H2020 ; 694277 - TUNE - Testing the Untestable: Model Testing of Complex Software-Intensive Systems
FnR ; FNR8003491 > Bing Liu > AUTODEBUGSLM > Automated Debugging and Fault Localization of MATLAB/Simulink Models > 01/03/2014 > 14/07/2017 > 2014

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