W. Mehmood, A. Waheed Khan, W. Aslam, Shafiq Ahmad, Ahmed M. El-Sherbeeny, M. Shafiq
{"title":"Requirement Design for Software Configuration and System Modeling","authors":"W. Mehmood, A. Waheed Khan, W. Aslam, Shafiq Ahmad, Ahmed M. El-Sherbeeny, M. Shafiq","doi":"10.32604/iasc.2022.016116","DOIUrl":null,"url":null,"abstract":"Software Configuration Management (SCM) aims to control the development of complex software systems. Traditional SCM systems treat text files as central artifacts, so they are mainly developed for source code. Such a system is not suitable for model-based software development with model-centric artifacts. When applying traditional systems to model-based software development, new challenges such as model mapping, differentiation, and merging arise. Many existing methods mainly use UML or domain-specific languages to determine model differences. However, as far as we know, there is no such technology for System Modeling Language (SysML) models. SysML covers the entire development life cycle of various complex systems, covering information, processes, hardware and software. SysML contains nine types of diagrams for system modeling. One of them is the SysML requirement diagram, which is used to capture the functional requirements of the system. We propose a differentiation method for the SysML demand model. We recommend to create a SysML requirement model in the CASE tool first, and then export the SysML model in the form of XMI. Then, we parse the XMI representation through difference calculations. Finally, we summarize the results in annotated form. We implemented our method in a satellite system case study and demonstrated the experimental use of the method.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"33 1","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Intelligent Automation and Soft Computing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.32604/iasc.2022.016116","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0
Abstract
Software Configuration Management (SCM) aims to control the development of complex software systems. Traditional SCM systems treat text files as central artifacts, so they are mainly developed for source code. Such a system is not suitable for model-based software development with model-centric artifacts. When applying traditional systems to model-based software development, new challenges such as model mapping, differentiation, and merging arise. Many existing methods mainly use UML or domain-specific languages to determine model differences. However, as far as we know, there is no such technology for System Modeling Language (SysML) models. SysML covers the entire development life cycle of various complex systems, covering information, processes, hardware and software. SysML contains nine types of diagrams for system modeling. One of them is the SysML requirement diagram, which is used to capture the functional requirements of the system. We propose a differentiation method for the SysML demand model. We recommend to create a SysML requirement model in the CASE tool first, and then export the SysML model in the form of XMI. Then, we parse the XMI representation through difference calculations. Finally, we summarize the results in annotated form. We implemented our method in a satellite system case study and demonstrated the experimental use of the method.
期刊介绍:
An International Journal seeks to provide a common forum for the dissemination of accurate results about the world of intelligent automation, artificial intelligence, computer science, control, intelligent data science, modeling and systems engineering. It is intended that the articles published in the journal will encompass both the short and the long term effects of soft computing and other related fields such as robotics, control, computer, vision, speech recognition, pattern recognition, data mining, big data, data analytics, machine intelligence, cyber security and deep learning. It further hopes it will address the existing and emerging relationships between automation, systems engineering, system of systems engineering and soft computing. The journal will publish original and survey papers on artificial intelligence, intelligent automation and computer engineering with an emphasis on current and potential applications of soft computing. It will have a broad interest in all engineering disciplines, computer science, and related technological fields such as medicine, biology operations research, technology management, agriculture and information technology.