Rijul Saini, G. Mussbacher, Jin L. C. Guo, J. Kienzle
{"title":"A Neural Network Based Approach to Domain Modelling Relationships and Patterns Recognition","authors":"Rijul Saini, G. Mussbacher, Jin L. C. Guo, J. Kienzle","doi":"10.1109/MoDRE51215.2020.00016","DOIUrl":"https://doi.org/10.1109/MoDRE51215.2020.00016","url":null,"abstract":"Model-Driven Software Engineering advocates the use of models and their transformations across different stages of software engineering to better understand and analyze systems under development. Domain modelling is used during requirements analysis or the early stages of design to transform informal requirements written in natural language to domain models which are analyzable and more concise. Since domain modelling is time-consuming and requires modelling skills and experience, many approaches have been proposed to extract domain concepts and relationships automatically using extraction rules. However, relationships and patterns are often hidden in the sentences of a problem description. Automatic recognition of relationships or patterns in those cases requires context information and external knowledge of participating domain concepts, which goes beyond what is possible with extraction rules. In this paper, we draw on recent work on domain model extraction and envision a novel technique where sentence boundaries are customized and clusters of sentences are created for domain concepts. The technique further exploits a BiLSTM neural network model to identify relationships and patterns among domain concepts. We also present a classification strategy for relationships and patterns and use it to instantiate our technique. Preliminary results indicate that this novel idea is promising and warrants further research.","PeriodicalId":174751,"journal":{"name":"2020 IEEE Tenth International Model-Driven Requirements Engineering (MoDRE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115402061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Toward Achieving the Core Goals of Digital Business Transformation: A Preliminary Study","authors":"Malak Baslyman, Azzah Alghamdi, Sarah AlMuhaysh","doi":"10.1109/MoDRE51215.2020.00014","DOIUrl":"https://doi.org/10.1109/MoDRE51215.2020.00014","url":null,"abstract":"Many businesses are going through digital business transformation (DBT) in order to keep ahead of market, optimize customer experience, and reach business targets. The process of digital business transformation is complex as it involves multiple key elements such as stakeholders, business objectives, technology, and strategies. In addition, all specific and temporary goals, and business models have to be aligned with the key goals of the DBT. One of the risks organizations face during the transformation process is focusing on short-term solutions and failing to consider their impacts on the transformation core goals. In this study, we propose a goal-oriented model that provides a shared and holistic understanding of the DBT context, and captures its essential high-level goals and key elements. Moreover, the DBT goal model supports business owners in the selection of transformation strategies based on an organization's digital readiness, and the achievements of both short-term goals as well as the core goals of DBT. The DBT goal model was build based on literature analysis and experts validation. We exploit the Goal-oriented Requirement Language (GRL) to model and analyze the context using jUCMNave tool. The validity of the DBT goal model was assessed by domain experts and business owners, and its effectiveness is demonstrated through an illustrative example.","PeriodicalId":174751,"journal":{"name":"2020 IEEE Tenth International Model-Driven Requirements Engineering (MoDRE)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127449316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}