The Experts below are selected from a list of 15 Experts worldwide ranked by ideXlab platform
Bo Wang - One of the best experts on this subject based on the ideXlab platform.
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supporting feature model refinement with Updatable View
Frontiers of Computer Science, 2013Co-Authors: Bo Wang, Qiang Sun, Haiyan Zhao, Yingfei Xiong, Wei Zhang, Hong MeiAbstract:In the research of software reuse, feature models have been widely adopted to capture, organize and reuse the requirements of a set of similar applications in a software domain. However, the construction, especially the refinement, of feature models is a labor-intensive process, and there lacks an effective way to aid domain engineers in refining feature models. In this paper, we propose a new approach to support interactive refinement of feature models based on the View updating technique. The basic idea of our approach is to first extract features and relationships of interest from a possibly large and complicated feature model, then organize them into a comprehensible View, and finally refine the feature model through modifications on the View. The main characteristics of this approach are twofold: a set of powerful rules (as the slicing criterion) to slice the feature model into a View automatically, and a novel use of a bidirectional transformation language to make the View Updatable. We have successfully developed a tool, and a nontrivial case study shows the feasibility of this approach.
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Supporting feature model refinement with Updatable View
frontiers of computer science, 2013Co-Authors: Bo Wang, Hu Zhenjiang, Sun Qiang, Zhao Haiyan, Xiong Yingfei, Zhang Wei, Mei HongAbstract:In the research of software reuse, feature models have been widely adopted to capture, organize and reuse the requirements of a set of similar applications in a software domain. However, the construction, especially the refinement, of feature models is a labor-intensive process, and there lacks an effective way to aid domain engineers in refining feature models. In this paper, we propose a new approach to support interactive refinement of feature models based on the View updating technique. The basic idea of our approach is to first extract features and relationships of interest from a possibly large and complicated feature model, then organize them into a comprehensible View, and finally refine the feature model through modifications on the View. The main characteristics of this approach are twofold: a set of powerful rules (as the slicing criterion) to slice the feature model into a View automatically, and a novel use of a bidirectional transformation language to make the View Updatable. We have successfully developed a tool, and a nontrivial case study shows the feasibility of this approach.Computer Science, Information SystemsComputer Science, Software EngineeringComputer Science, Theory & MethodsSCI(E)EI中国科学引文数据库(CSCD)1ARTICLE2257-271
Hong Mei - One of the best experts on this subject based on the ideXlab platform.
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supporting feature model refinement with Updatable View
Frontiers of Computer Science, 2013Co-Authors: Bo Wang, Qiang Sun, Haiyan Zhao, Yingfei Xiong, Wei Zhang, Hong MeiAbstract:In the research of software reuse, feature models have been widely adopted to capture, organize and reuse the requirements of a set of similar applications in a software domain. However, the construction, especially the refinement, of feature models is a labor-intensive process, and there lacks an effective way to aid domain engineers in refining feature models. In this paper, we propose a new approach to support interactive refinement of feature models based on the View updating technique. The basic idea of our approach is to first extract features and relationships of interest from a possibly large and complicated feature model, then organize them into a comprehensible View, and finally refine the feature model through modifications on the View. The main characteristics of this approach are twofold: a set of powerful rules (as the slicing criterion) to slice the feature model into a View automatically, and a novel use of a bidirectional transformation language to make the View Updatable. We have successfully developed a tool, and a nontrivial case study shows the feasibility of this approach.
Mei Hong - One of the best experts on this subject based on the ideXlab platform.
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Supporting feature model refinement with Updatable View
frontiers of computer science, 2013Co-Authors: Bo Wang, Hu Zhenjiang, Sun Qiang, Zhao Haiyan, Xiong Yingfei, Zhang Wei, Mei HongAbstract:In the research of software reuse, feature models have been widely adopted to capture, organize and reuse the requirements of a set of similar applications in a software domain. However, the construction, especially the refinement, of feature models is a labor-intensive process, and there lacks an effective way to aid domain engineers in refining feature models. In this paper, we propose a new approach to support interactive refinement of feature models based on the View updating technique. The basic idea of our approach is to first extract features and relationships of interest from a possibly large and complicated feature model, then organize them into a comprehensible View, and finally refine the feature model through modifications on the View. The main characteristics of this approach are twofold: a set of powerful rules (as the slicing criterion) to slice the feature model into a View automatically, and a novel use of a bidirectional transformation language to make the View Updatable. We have successfully developed a tool, and a nontrivial case study shows the feasibility of this approach.Computer Science, Information SystemsComputer Science, Software EngineeringComputer Science, Theory & MethodsSCI(E)EI中国科学引文数据库(CSCD)1ARTICLE2257-271
Hu Zhenjiang - One of the best experts on this subject based on the ideXlab platform.
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Toward Co-existing Database Schemas based on Bidirectional Transformation
2019Co-Authors: Tanaka Jumpei, Tran Van-dang, Kato Hiroyuki, Hu ZhenjiangAbstract:According to strong demands for rapid and reliable software delivery, co-existing database schema versions with multiple application versions are reality to contribute them. Current database management systems do not support co-existing schema versions in one database. Although a design of co-existing schema based on Updatable View tables was previously proposed, its flexibility is limited due to pre-defined several restrictions to achieve data synchronization among schemas and handling independent unsynchronized data in each schema. In this preliminary report, we present a new approach for co-existing schemas based on bidirectional transformation. We explain the required properties to realize co-existing schemas, bidirectionality and totality. We show that the co-existing schemas can be implemented systematically by applying putback-based bidirectional transformation to satisfy both the bidirectionality and the totality. While the bidirectionality can be satisfied by applying bidirectional transformation, to satisfy the totality, extra functions need to be introduced. How to derive these extra functions is presented.Comment: Proceedings of the Third Workshop on Software Foundations for Data Interoperability (SFDI2019+), October 28, 2019, Fukuoka, Japa
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Supporting feature model refinement with Updatable View
frontiers of computer science, 2013Co-Authors: Bo Wang, Hu Zhenjiang, Sun Qiang, Zhao Haiyan, Xiong Yingfei, Zhang Wei, Mei HongAbstract:In the research of software reuse, feature models have been widely adopted to capture, organize and reuse the requirements of a set of similar applications in a software domain. However, the construction, especially the refinement, of feature models is a labor-intensive process, and there lacks an effective way to aid domain engineers in refining feature models. In this paper, we propose a new approach to support interactive refinement of feature models based on the View updating technique. The basic idea of our approach is to first extract features and relationships of interest from a possibly large and complicated feature model, then organize them into a comprehensible View, and finally refine the feature model through modifications on the View. The main characteristics of this approach are twofold: a set of powerful rules (as the slicing criterion) to slice the feature model into a View automatically, and a novel use of a bidirectional transformation language to make the View Updatable. We have successfully developed a tool, and a nontrivial case study shows the feasibility of this approach.Computer Science, Information SystemsComputer Science, Software EngineeringComputer Science, Theory & MethodsSCI(E)EI中国科学引文数据库(CSCD)1ARTICLE2257-271
Cheney James - One of the best experts on this subject based on the ideXlab platform.
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Incremental Relational Lenses
ACM, 2018Co-Authors: Horn Rudi, Perera Roly, Cheney JamesAbstract:Lenses are a popular approach to bidirectional transformations, a generalisation of the View update problem in databases, in which we wish to make changes to source tables to effect a desired change on a View. However, perhaps surprisingly, lenses have seldom actually been used to implement Updatable Views in databases. Bohannon, Pierce and Vaughan proposed an approach to Updatable Views called relational lenses, but to the best of our knowledge this proposal has not been implemented or evaluated to date. We propose incremental relational lenses, that equip relational lenses with change-propagating semantics that map small changes to the View to (potentially) small changes to the source tables. We also present a language-integrated implementation of relational lenses and a detailed experimental evaluation, showing orders of magnitude improvement over the non-incremental approach. Our work shows that relational lenses can be used to support expressive and efficient View updates at the language level, without relying on Updatable View support from the underlying database
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Incremental Relational Lenses
'Association for Computing Machinery (ACM)', 2018Co-Authors: Horn Rudi, Perera Roly, Cheney JamesAbstract:Lenses are a popular approach to bidirectional transformations, a generalisation of the View update problem in databases, in which we wish to make changes to source tables to effect a desired change on a View. However, perhaps surprisingly, lenses have seldom actually been used to implement Updatable Views in databases. Bohannon, Pierce and Vaughan proposed an approach to Updatable Views called relational lenses, but to the best of our knowledge this proposal has not been implemented or evaluated to date. We propose incremental relational lenses, that equip relational lenses with change-propagating semantics that map small changes to the View to (potentially) small changes to the source tables. We also present a language-integrated implementation of relational lenses and a detailed experimental evaluation, showing orders of magnitude improvement over the non-incremental approach. Our work shows that relational lenses can be used to support expressive and efficient View updates at the language level, without relying on Updatable View support from the underlying database.Comment: To appear, ICFP 201
