{"title":"建模概率时序分析:正在进行的工作","authors":"Bojan Nokovic, E. Sekerinski","doi":"10.1145/3125503.3125566","DOIUrl":null,"url":null,"abstract":"We describe the process of calculating the execution time profile (ETP) in order to determine the probabilistic worst case execution time (WCET) using a model-based approach. By hierarchical state machines with probabilistic transitions and costs/reward specifications, we model the instructions with probabilistic execution time. From the model, our tool, pState, generates input code for a probabilistic model checker on which properties can be analysed.","PeriodicalId":143573,"journal":{"name":"International Conference on Embedded Software","volume":"161 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modelling probabilistic timing analysis: work-in-progress\",\"authors\":\"Bojan Nokovic, E. Sekerinski\",\"doi\":\"10.1145/3125503.3125566\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We describe the process of calculating the execution time profile (ETP) in order to determine the probabilistic worst case execution time (WCET) using a model-based approach. By hierarchical state machines with probabilistic transitions and costs/reward specifications, we model the instructions with probabilistic execution time. From the model, our tool, pState, generates input code for a probabilistic model checker on which properties can be analysed.\",\"PeriodicalId\":143573,\"journal\":{\"name\":\"International Conference on Embedded Software\",\"volume\":\"161 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Embedded Software\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3125503.3125566\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Embedded Software","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3125503.3125566","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We describe the process of calculating the execution time profile (ETP) in order to determine the probabilistic worst case execution time (WCET) using a model-based approach. By hierarchical state machines with probabilistic transitions and costs/reward specifications, we model the instructions with probabilistic execution time. From the model, our tool, pState, generates input code for a probabilistic model checker on which properties can be analysed.