{"title":"Understanding modifications in state-based models","authors":"B. Korel, L. Tahat","doi":"10.1109/WPC.2004.1311068","DOIUrl":null,"url":null,"abstract":"System modeling is a widely used technique to model state-based systems. System models are frequently large and complex and are hard to understand. In addition, they are frequently modified because of specification changes. Understanding the effect of these changes on the model and the system may be very difficult for large models. In this paper, we present an approach that may support understanding the effect of model modifications. The goal is to identify these parts of the model that may exhibit different behavior because of the modification. In this approach, the difference between the original model and the modified model is identified and then affected parts of the model are computed based on model dependence analysis. Our initial experience shows that the approach may be helpful in understanding the effect of modifications on the system.","PeriodicalId":164866,"journal":{"name":"Proceedings. 12th IEEE International Workshop on Program Comprehension, 2004.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. 12th IEEE International Workshop on Program Comprehension, 2004.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WPC.2004.1311068","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
System modeling is a widely used technique to model state-based systems. System models are frequently large and complex and are hard to understand. In addition, they are frequently modified because of specification changes. Understanding the effect of these changes on the model and the system may be very difficult for large models. In this paper, we present an approach that may support understanding the effect of model modifications. The goal is to identify these parts of the model that may exhibit different behavior because of the modification. In this approach, the difference between the original model and the modified model is identified and then affected parts of the model are computed based on model dependence analysis. Our initial experience shows that the approach may be helpful in understanding the effect of modifications on the system.