{"title":"UML状态机中状态和转换的一组度量","authors":"Gefei Zhang, Matthias M. Hölzl","doi":"10.1145/2630768.2630770","DOIUrl":null,"url":null,"abstract":"UML state machines are widely used to model software system behaviors. Seemingly intuitive and simple, state machines may actually be rather complex. We present a set of metrics to reflect which model elements actually account for the complexity. Our metrics give a better understanding of the complexity of UML state machines, and may alert the modeler to pay more attention to more complex states and transitions.","PeriodicalId":223556,"journal":{"name":"International Workshop on Behaviour Modelling - Foundations and Applications","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"A Set of Metrics for States and Transitions in UML State Machines\",\"authors\":\"Gefei Zhang, Matthias M. Hölzl\",\"doi\":\"10.1145/2630768.2630770\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"UML state machines are widely used to model software system behaviors. Seemingly intuitive and simple, state machines may actually be rather complex. We present a set of metrics to reflect which model elements actually account for the complexity. Our metrics give a better understanding of the complexity of UML state machines, and may alert the modeler to pay more attention to more complex states and transitions.\",\"PeriodicalId\":223556,\"journal\":{\"name\":\"International Workshop on Behaviour Modelling - Foundations and Applications\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Workshop on Behaviour Modelling - Foundations and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2630768.2630770\",\"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 Workshop on Behaviour Modelling - Foundations and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2630768.2630770","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Set of Metrics for States and Transitions in UML State Machines
UML state machines are widely used to model software system behaviors. Seemingly intuitive and simple, state machines may actually be rather complex. We present a set of metrics to reflect which model elements actually account for the complexity. Our metrics give a better understanding of the complexity of UML state machines, and may alert the modeler to pay more attention to more complex states and transitions.
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