Abstract
Model-based software development using MATLAB Simulink is widely used in safety-critical domains. The reliability properties of the developed software have to be numerically evaluated for the precise system-level dependability analysis. Data errors occurred in RAM or CPU registers can propagate to critical outputs and cause a failure. The reliability properties can be evaluated at the assembly level, i.e. on the compiled instructions, by performing a probabilistic modeling of data errors. It is more accurate to conduct reliability assessment at the low level, however, the method scalability is questionable due to the complicated procedure, complexity of the assembly code, and considerable computation effort. Thus assembly-level evaluation is unsuitable for huge and complex Simulink models. In addition, it is more convenient for design engineers to estimate dependability properties of Simulink models and even to design reliable control systems at the model level.
In this paper, we propose a method for the reliability evaluation of Simulink models at the model level, extended with the assembly-level evaluation. More specifically, we transform the Simulink model into a stochastic dual-graph error propagation model and specify the reliability properties of individual Simulink blocks by loading the data from a database that have been obtained via the assembly-level evaluation. We verified the efficiency of the proposed method by the comparison of the reliability properties, evaluated at the assembly level and at the model level. The experimental results indicate that the reliability metrics, evaluated at the model level, are almost equivalent to the ones, evaluated at the assembly level. Most prominently, the application of the proposed model-level assessment can reduce the computation and engineering effort, and increase the method scalability.
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References
Open errorpro on the github. https://mbsa-tud.github.io/OpenErrorPro/
Ayatolahi, F., Sangchoolie, B., Johansson, R., Karlsson, J.: A study of the impact of single bit-flip and double bit-flip errors on program execution. In: Bitsch, F., Guiochet, J., Kaâniche, M. (eds.) SAFECOMP 2013. LNCS, vol. 8153, pp. 265–276. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40793-2_24
Ding, K., Morozov, A., Janschek, K.: Classification of hierarchical fault-tolerant design patterns. In: 2017 IEEE 15th International Conference Dependable, Autonomic and Secure Computing (DASC). IEEE (2017)
Ding, K., Morozov, A., Janschek, K.: MORE: MOdel-based REdundancy for Simulink. In: Gallina, B., Skavhaug, A., Bitsch, F. (eds.) SAFECOMP 2018. LNCS, vol. 11093, pp. 250–264. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99130-6_17
Ding, K., Morozov, A., Janschek, K.: Reliability evaluation of functionally equivalent simulink implementations of a PID controller under silent data corruption. In: 2018 IEEE 29th International Symposium on Software Reliability Engineering (ISSRE). IEEE (2018)
Ding, K., Mutzke, T., Morozov, A., Janschek, K.: Automatic transformation of uml system models for model-based error propagation analysis of mechatronic systems
Eriksson, H.: D 5.1 - simulating hardware-related faults at model level. Technical report
Folkesson, P., Ayatolahi, F., Sangchoolie, B., Vinter, J., Islam, M., Karlsson, J.: Back-to-back fault injection testing in model-based development. In: Koornneef, F., van Gulijk, C. (eds.) SAFECOMP 2015. LNCS, vol. 9337, pp. 135–148. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24255-2_11
Juez, G., Amparan, E., Lattarulo, R., Ruíz, A., Pérez, J., Espinoza, H.: Early safety assessment of automotive systems using sabotage simulation-based fault injection framework. In: Tonetta, S., Schoitsch, E., Bitsch, F. (eds.) SAFECOMP 2017. LNCS, vol. 10488, pp. 255–269. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66266-4_17
Koopman, P.: A case study of toyota unintended acceleration and software safety. Presentation (2014)
Kwiatkowska, M., Norman, G., Parker, D.: PRISM 4.0: verification of probabilistic real-time systems. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 585–591. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22110-1_47
MATLAB: version 9.6 (R2019a). The MathWorks Inc., Natick, Massachusetts
Morozov, A., Janschek, K.: Probabilistic error propagation model for mechatronic systems. Mechatronics 24(8), 1189–1202 (2014)
Morozov, A., Janschek, K., Krüger, T., Schiele, A.: Stochastic error propagation analysis of model-driven space robotic software implemented in simulink. In: Proceedings of the 3rd Workshop on Model-Driven Robot Software Engineering. ACM (2016)
Saraoğlu, M., Morozov, A., Söylemez, M.T., Janschek, K.: ErrorSim: a tool for error propagation analysis of simulink models. In: Tonetta, S., Schoitsch, E., Bitsch, F. (eds.) SAFECOMP 2017. LNCS, vol. 10488, pp. 245–254. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66266-4_16
Schroeder, B., Pinheiro, E., Weber, W.D.: Dram errors in the wild: a large-scale field. In: ACM SIGMETRICS Performance Evaluation Review, vol. 37. ACM (2009)
Skarin, D., Barbosa, R., Karlsson, J.: Goofi-2: a tool for experimental dependability assessment. In: 2010 IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), pp. 557–562. IEEE (2010)
Svenningsson, R.: Model-implemented fault injection for robustness assessment
Svenningsson, R., Eriksson, H., Vinter, J., Törngren, M.: Model-implemented fault injection for hardware fault simulation. In: 2010 Workshop on Model-Driven Engineering, Verification, and Validation (MoDeVVa), pp. 31–36. IEEE (2010)
Svenningsson, R., Vinter, J., Eriksson, H., Törngren, M.: MODIFI: A MODel-Implemented Fault Injection Tool. In: Schoitsch, E. (ed.) SAFECOMP 2010. LNCS, vol. 6351, pp. 210–222. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15651-9_16
Swift, G.M., Guertin, S.M.: In-flight observations of multiple-bit upset in drams. IEEE Trans. Nucl. Sci. 47(6), 2386–2391 (2000)
Verzola, I., Lagny, A.E., Biswas, J.: A predictive approach to failure estimation and identification for space systems operations. In: SpaceOps 2014 Conference (2014)
Vinter, J., Johansson, A., Folkesson, P., Karlsson, J.: On the design of robust integrators for fail-bounded control systems. In: Proceedings of 2003 International Conference on Dependable Systems and Networks, pp. 415–424, June 2003
Acknowledgements
This work is supported by the German Research Foundation (DFG) under project No. JA 1559/5-1.
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Ding, K., Morozov, A., Janschek, K. (2019). Efficient Model-Level Reliability Analysis of Simulink Models. In: Romanovsky, A., Troubitsyna, E., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2019. Lecture Notes in Computer Science(), vol 11698. Springer, Cham. https://doi.org/10.1007/978-3-030-26601-1_10
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