Localizing Multiple Faults in Simulink Models

Abstract

Abstract-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 multifault 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 clusteringbased 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.

Effective fault localization of automotive Simulink models: achieving the trade-off between test oracle effort and fault localization accuracy

“…We showed that the accuracy of our approach, when applied to Simulink models from the automotive industry, is comparable to the accuracy of statistical debugging applied to source code [42]. We further extended our approach to handle fault localization for Simulink models with multiple faults [41].…”

CPSDebug: Automatic failure explanation in CPS models

“…SBFL has been recently employed to localize faults in Simulink/Stateflow CPS models [5,9,[19][20][21], showing similar accuracy as in the application to software systems [21]. The explanatory power of this approach is, however, limited, because it generates neither information that can help the engineers understand if a selected code location is really faulty nor information about how a fault is propagated across components.…”

“…The complexity of CPS models is manifold: these models typically combine discrete and continuous dynamics with an interplay between many variables, signals, state machines, look-up tables and components. Detecting problems in the early stages of CPS design [2,5,9,19,20] is of uttermost importance, before they propagate to the actual CPS with potentially catastrophic consequences.…”

Software fault localisation: a systematic mapping study

“…In terms of countries with high publications, we have identified that 37% of our primary studies were published from China, while 28% of the studies were from the United States (USA). However, countries like Singapore (24), Australia (15), and Portugal (12) are producing good papers out of 26 countries. Therefore, more effort is needed from other institutions in the rest of the countries to move the SFL research area forward in the near future.…”

“…In the last decades, various fault localisation techniques had been proposed with competing ways of effective fault identification [4,[6][7][8][9][10][11][12]. In a general sense, a fault localisation techniques assign suspicious scores to program statements that signify a statements degree of association with failure.…”