Localizing Multiple Faults in Simulink Models
| Autor: | Lionel C. Briand, Shiva Nejati, Lucia, Thomas Bruckmann, Bing Liu |
|---|---|
| Přispěvatelé: | Fonds National de la Recherche - FnR [sponsor], Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Software Verification and Validation Lab (SVV Lab) [research center] |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Computer science
media_common.quotation_subject Decision tree 02 engineering and technology Fault (power engineering) Machine learning computer.software_genre 0202 electrical engineering electronic engineering information engineering Cluster analysis Selection (genetic algorithm) media_common Computer science [C05] [Engineering computing & technology] decision trees business.industry 020207 software engineering Sciences informatiques [C05] [Ingénierie informatique & technologie] statistical debugging 020202 computer hardware & architecture Fault localization Simulink models machine learning Debugging Artificial intelligence Focus (optics) business computer |
| Zdroj: | 23rd IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2016). (2016). SANER |
| Popis: | As Simulink is a widely used language in the embedded industry, there is a growing need to support debugging activities for Simulink models. In this work, we propose an approach to localize multiple faults in Simulink models. Our approach builds on statistical debugging and is iterative. At each iteration, we identify and resolve one fault and re-test models to focus on localizing faults that might have been masked before. We use decision trees to cluster together failures that satisfy similar (logical) conditions on model blocks or inputs. We then present two alternative selection criteria to choose a cluster that is more likely to yield the best fault localization results among the clusters produced by our decision trees. Engineers are expected to inspect the ranked list obtained from the selected cluster to identify faults. We evaluate our approach on 240 multi-fault models obtained from three different industrial subjects. We compare our approach with two baselines: (1) Statistical debugging without clustering, and (2) State-of-the-art clustering-based statistical debugging. Our results show that our approach significantly reduces the number of blocks that engineers need to inspect in order to localize all faults, when compared with the two baselines. Furthermore, with our approach, there is less performance degradation than in the baselines when increasing the number of faults in the underlying models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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