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Model-Based Fault Classification for Automotive Software

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Programming Languages and Systems (APLAS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13658))

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Abstract

Intensive testing using model-based approaches is the standard way of demonstrating the correctness of automotive software. Unfortunately, state-of-the-art techniques leave a crucial and labor intensive task to the test engineer: identifying bugs in failing tests. Our contribution is a model-based classification algorithm for failing tests that assists the engineer when identifying bugs. It consists of three steps. (i) Fault localization replays the test on the model to identify the moment when the two diverge. (ii) Fault explanation then computes the reason for the divergence. The reason is a subset of messages from the test that is sufficient for divergence. (iii) Fault classification groups together tests that fail for similar reasons. Our approach relies on machinery from formal methods: (i) symbolic execution, (ii) Hoare logic and a new relationship between the intermediary assertions constructed for a test, and (iii) a new relationship among Hoare proofs. A crucial aspect in automotive software are timing requirements, for which we develop appropriate Hoare logic theory. We also briefly report on our prototype implementation for the CAN bus Unified Diagnostic Services in an industrial project.

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Notes

  1. 1.

    One can show that the inclusion \( P _i\sqsubseteq _{} R _i\) is always satisfied in our setting where \(\varDelta \)-cycles are idempotent and the widenings \(\nabla \) and \(\overline{\nabla }\) simply enumerate all necessary decompositions of time progressions. Refer to [4] for a more general property.

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Acknowledgements

The results were obtained in the projects “Virtual Test Analyzer I – III”, conducted in collaboration with IAV GmbH. The last author is supported by a Junior Fellowship from the Simons Foundation (855328, SW).

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Authors and Affiliations

  1. TU Braunschweig, Braunschweig, Germany

    Mike Becker, Roland Meyer, Tobias Runge, Ina Schaefer & Sören van der Wall

  2. KIT Karlsruhe, Karlsruhe, Germany

    Tobias Runge & Ina Schaefer

  3. New York University, New York, USA

    Sebastian Wolff

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Correspondence to Sebastian Wolff .

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  1. National University of Singapore, Singapore, Singapore

    Ilya Sergey

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Becker, M., Meyer, R., Runge, T., Schaefer, I., van der Wall, S., Wolff, S. (2022). Model-Based Fault Classification for Automotive Software. In: Sergey, I. (eds) Programming Languages and Systems. APLAS 2022. Lecture Notes in Computer Science, vol 13658. Springer, Cham. https://doi.org/10.1007/978-3-031-21037-2_6

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