{"title":"Consistent management of variability in space and time","authors":"Sofia Ananieva","doi":"10.1145/3461002.3473067","DOIUrl":null,"url":null,"abstract":"Development and maintenance of software-intensive systems face major challenges. To cope with an increasing demand for customization, systems need to exist in concurrent variations at a single point in time (i.e., variability in space). Furthermore, as longevity of systems increases, it is necessary to continuously maintain sequential variations due to evolution (i.e., variability in time). Finally, systems are often built from different kinds of artifacts, such as source code or diagrams, that need to be kept consistent. Managing these challenges - the evolution of variable systems composed of heterogeneous artifacts in a consistent and integrated way - is highly demanding for engineers. To tackle the described challenges, we propose an approach for consistent, view-based management of variability in space and time. Therefore, we study, identify, and unify concepts and operations of approaches and tools dealing with variability in space and time to provide a common ground for comparing existing work and encouraging novel solutions. Furthermore, we identify consistency preservation challenges related to view-based evolution of variable systems composed of heterogeneous artifacts, such as the consistent propagation of changes between products, and across the different types of artifacts. We provide a technique for (semi-)automated detection and repair of variability-related inconsistencies. The goal of this doctoral work is to develop an integrated solution for dealing with the described challenges and, thus, advance state of the art towards uniform management of variability in space and time.","PeriodicalId":416819,"journal":{"name":"Proceedings of the 25th ACM International Systems and Software Product Line Conference - Volume B","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 25th ACM International Systems and Software Product Line Conference - Volume B","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3461002.3473067","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Development and maintenance of software-intensive systems face major challenges. To cope with an increasing demand for customization, systems need to exist in concurrent variations at a single point in time (i.e., variability in space). Furthermore, as longevity of systems increases, it is necessary to continuously maintain sequential variations due to evolution (i.e., variability in time). Finally, systems are often built from different kinds of artifacts, such as source code or diagrams, that need to be kept consistent. Managing these challenges - the evolution of variable systems composed of heterogeneous artifacts in a consistent and integrated way - is highly demanding for engineers. To tackle the described challenges, we propose an approach for consistent, view-based management of variability in space and time. Therefore, we study, identify, and unify concepts and operations of approaches and tools dealing with variability in space and time to provide a common ground for comparing existing work and encouraging novel solutions. Furthermore, we identify consistency preservation challenges related to view-based evolution of variable systems composed of heterogeneous artifacts, such as the consistent propagation of changes between products, and across the different types of artifacts. We provide a technique for (semi-)automated detection and repair of variability-related inconsistencies. The goal of this doctoral work is to develop an integrated solution for dealing with the described challenges and, thus, advance state of the art towards uniform management of variability in space and time.