{"title":"Stochastic Error Propagation Analysis of Model-driven Space Robotic Software Implemented in Simulink","authors":"A. Morozov, K. Janschek, T. Krüger, A. Schiele","doi":"10.1145/3022099.3022103","DOIUrl":null,"url":null,"abstract":"Model-driven software development methods are widely used in safety-critical domains including space robotics. The MATLAB Simulink environment is the common choice of control engineers. This article introduces a new method for a fully automatic transformation of a Simulink model to a dual-graph model for stochastic error propagation analysis. The error propagation analysis provides important inputs for system reliability methods, required by industrial standards such as FTA and FMEA. The dual-graph error propagation model is a mathematical abstraction of key system design aspects that influence error propagation processes: control flow, data flow, and component-level reliability properties. This model helps to estimate the likelihood of error propagation to hazardous system parts and quantify the negative impact of a fault in a particular component on the overall system reliability. In praxis, the manual creation of an error propagation model of a complex system requires a huge effort. The transformation method, introduced in this article, is a fast and promising solution. The method is demonstrated as a part of a stochastic analysis of a real-world model-driven space robotic software.","PeriodicalId":361389,"journal":{"name":"MORSE '16","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"MORSE '16","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3022099.3022103","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
Model-driven software development methods are widely used in safety-critical domains including space robotics. The MATLAB Simulink environment is the common choice of control engineers. This article introduces a new method for a fully automatic transformation of a Simulink model to a dual-graph model for stochastic error propagation analysis. The error propagation analysis provides important inputs for system reliability methods, required by industrial standards such as FTA and FMEA. The dual-graph error propagation model is a mathematical abstraction of key system design aspects that influence error propagation processes: control flow, data flow, and component-level reliability properties. This model helps to estimate the likelihood of error propagation to hazardous system parts and quantify the negative impact of a fault in a particular component on the overall system reliability. In praxis, the manual creation of an error propagation model of a complex system requires a huge effort. The transformation method, introduced in this article, is a fast and promising solution. The method is demonstrated as a part of a stochastic analysis of a real-world model-driven space robotic software.