Tracking down root causes of defects in simulink models

Schneider, Johanna

doi:10.1145/2642937.2642943

Cited by 5 publications

(6 citation statements)

References 7 publications

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“…Our work relates to the work of Schneider [66] that proposes a technique for tracking the root causes of defects in Simulink. In that technique, engineers identify failures, typically run-time failures, at the level of code generated from Simulink models.…”

Section: Model Debuggingmentioning

confidence: 99%

“…This work focuses on runtime failures (e.g., division by zero), while in our work, we consider a wider range of fault types for Simulink models (see Section 4.2). Further, Schneider [66] does not provide any realistic evaluation of the proposed approach. Also, as they do not report the number of blocks that engineers need to eventually inspect, the expected debugging effort is not clear either.…”

Section: Model Debuggingmentioning

confidence: 99%

“…Also, as they do not report the number of blocks that engineers need to eventually inspect, the expected debugging effort is not clear either. Finally, Schneider [66] does not propose any mechanism to improve fault localization accuracy when a debugging task turns out to be inconclusive for a given test suite.…”

Section: Model Debuggingmentioning

confidence: 99%

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

et al. 2018

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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 four test objectives that aim to increase test suite diversity. We use four 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) expanding test suites used for fault localization using any of our four test objectives, even when the expansion is small, can significantly improve the accuracy of fault localization, (2) varying test objectives used to generate the initial test suites for fault localization does not have a significant impact on the fault localization results obtained based on those test suites, and (3) we identify an optimal configuration for prediction models to help stop test generation when it is unlikely to be beneficial. We further show that our optimal 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.

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Section: Model Debuggingmentioning

confidence: 99%

Section: Model Debuggingmentioning

confidence: 99%

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

et al. 2018

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“…Our work relates to the recent work of Schneider that proposes a technique for tracking the root causes of defects in Simulink. In that technique, engineers identify failures, typically run‐time failures, at the level of code generated from Simulink models.…”

Section: Related Workmentioning

confidence: 99%

Simulink fault localization: an iterative statistical debugging approach

Liu

Lucia

Nejati

et al. 2016

Software Testing Verif & Rel

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Debugging Simulink models presents a significant challenge in the embedded industry. In this work, we propose SimFL, a fault localization approach for Simulink models by combining statistical debugging and dynamic model slicing. Simulink models, being visual and hierarchical, have multiple outputs at different hierarchy levels. Given a set of outputs to observe for localizing faults, we generate test execution slices, for each test case and output, of the Simulink model. In order to further improve fault localization accuracy, we propose iSimFL, an iterative fault localization algorithm. At each iteration, iSimFL increases the set of observable outputs by including outputs at lower hierarchy levels, thus increasing the test oracle cost but offsetting it with significantly more precise fault localization. We utilize a heuristic stopping criterion to avoid unnecessary test oracle extension. We evaluate our work on three industrial Simulink models from Delphi Automotive. Our results show that, on average, SimFL ranks faulty blocks in the top 8.9% in the list of suspicious blocks. Further, we show that iSimFL significantly improves this percentage down to 4.4% by requiring engineers to observe only an average of five additional outputs at lower hierarchy levels on top of high-level model outputs.

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“…The German title is Eingrenzung möglicher Ursachen von Laufzeitfehlern in SimulinkModellen. For the ASE 2014 main conference, the paper Tracking Down Root Causes of Defects in Simulink Models [11] is accepted as well. Within this paper, the approach is described more detailed.…”

Section: Evaluation and Presentationmentioning

confidence: 99%

Determining the most probable root causes of run-time errors in simulink models

Schneider

2014

Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering

Self Cite

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Being confronted with a defect in software leads to the well known task: correcting the software such that the defect does not occur anymore. Here, the location of the defect and the corresponding root cause do not have to be identical. Thus, before any correction can be done, the reviewer has to detect the root cause for the defect. In order to reduce the reviewing effort, this approach presents a method to automatically narrow down possible root causes for defects found in Simulink models. Starting at a defect location, a backward search is applied to detect all paths leading to that defect. Each path is weighted by previously determined weights depending on the block types contained in the respective path. This weighting correlates with the probability of a path containing the root cause.

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Tracking down root causes of defects in simulink models

Cited by 5 publications

References 7 publications

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

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

Simulink fault localization: an iterative statistical debugging approach

Determining the most probable root causes of run-time errors in simulink models

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