{"title":"确保变更可追溯性和多模型一致性的模型驱动方法","authors":"Claudia Szabo, Yufei Chen","doi":"10.1109/ASWEC.2013.24","DOIUrl":null,"url":null,"abstract":"In model driven engineering, high-level models of an application are constructed to enable reasoning about functional and non-functional requirements independently of implementation issues and concerns. This allows for reduced maintenance, shortens development time, and permits automated model updates, system model executions, and impact assessment. Part of model driven engineering, multi-modeling integrates models that abstract various aspects of the system, such as I/O, behavioral, and functional among others, at different levels of granularity and using various domain specific modeling languages. An important challenge is to understand the relationship between these models towards preserving multi-model consistency as changes in one model affect other models in the multi-model. This paper presents a multi-modeling architecture that captures model relationships at syntactic and semantic levels. We define a taxonomy of change effects that relies on a relationship correspondence meta-model to highlight and trace the impact of changes across various modeling environments. Following the correspondence meta-model and associated change effects, our prototype implementation ensures that multi-model consistency is met and notifies stakeholders of significant changes. Our case study of a submarine tracking system checks multi model consistency and highlights the impact of changes across system modeling tools that capture its functional and behavioral aspects among others. Our experiments show the feasibility of our approach while highlighting important challenges.","PeriodicalId":394020,"journal":{"name":"2013 22nd Australian Software Engineering Conference","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"A Model-Driven Approach for Ensuring Change Traceability and Multi-model Consistency\",\"authors\":\"Claudia Szabo, Yufei Chen\",\"doi\":\"10.1109/ASWEC.2013.24\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In model driven engineering, high-level models of an application are constructed to enable reasoning about functional and non-functional requirements independently of implementation issues and concerns. This allows for reduced maintenance, shortens development time, and permits automated model updates, system model executions, and impact assessment. Part of model driven engineering, multi-modeling integrates models that abstract various aspects of the system, such as I/O, behavioral, and functional among others, at different levels of granularity and using various domain specific modeling languages. An important challenge is to understand the relationship between these models towards preserving multi-model consistency as changes in one model affect other models in the multi-model. This paper presents a multi-modeling architecture that captures model relationships at syntactic and semantic levels. We define a taxonomy of change effects that relies on a relationship correspondence meta-model to highlight and trace the impact of changes across various modeling environments. Following the correspondence meta-model and associated change effects, our prototype implementation ensures that multi-model consistency is met and notifies stakeholders of significant changes. Our case study of a submarine tracking system checks multi model consistency and highlights the impact of changes across system modeling tools that capture its functional and behavioral aspects among others. Our experiments show the feasibility of our approach while highlighting important challenges.\",\"PeriodicalId\":394020,\"journal\":{\"name\":\"2013 22nd Australian Software Engineering Conference\",\"volume\":\"59 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 22nd Australian Software Engineering Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ASWEC.2013.24\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 22nd Australian Software Engineering Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASWEC.2013.24","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Model-Driven Approach for Ensuring Change Traceability and Multi-model Consistency
In model driven engineering, high-level models of an application are constructed to enable reasoning about functional and non-functional requirements independently of implementation issues and concerns. This allows for reduced maintenance, shortens development time, and permits automated model updates, system model executions, and impact assessment. Part of model driven engineering, multi-modeling integrates models that abstract various aspects of the system, such as I/O, behavioral, and functional among others, at different levels of granularity and using various domain specific modeling languages. An important challenge is to understand the relationship between these models towards preserving multi-model consistency as changes in one model affect other models in the multi-model. This paper presents a multi-modeling architecture that captures model relationships at syntactic and semantic levels. We define a taxonomy of change effects that relies on a relationship correspondence meta-model to highlight and trace the impact of changes across various modeling environments. Following the correspondence meta-model and associated change effects, our prototype implementation ensures that multi-model consistency is met and notifies stakeholders of significant changes. Our case study of a submarine tracking system checks multi model consistency and highlights the impact of changes across system modeling tools that capture its functional and behavioral aspects among others. Our experiments show the feasibility of our approach while highlighting important challenges.