Reverse Engineering Feature Models

 
9783659614521: Reverse Engineering Feature Models

Companies often develop in a non-disciplined manner a set of software variants that share some features and differ in others to meet variant-specific requirements. To exploit existing software variants and manage them coherently as a software product line, a feature model must be built as a first step. To do so, it is necessary to extract mandatory and optional features from the code of the variants in addition to associate each feature implementation with its name. In this book, we propose an automatic approach to organize the mined documented features into a feature model. The feature model is a tree which highlights mandatory features, optional features and feature groups (and, or, xor groups). The feature model is completed with requirement and mutual exclusion constraints. We rely on Formal Concept Analysis and software configurations to mine a unique and consistent feature model. To validate our approach, we apply it on several case studies. The results of this evaluation validate the relevance and performance of our proposal as most of the features and their associated constraints are correctly identified.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

About the Author :

Ra’Fat Al-Msie’Deen received Ph.D. in 2014 from University of Montpellier 2 for science and technology, France. His research interest includes Software Engineering, Software Maintenance, Reverse Engineering, Reuse, Variability Modeling, Re-engineering, Software Product Line.

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Description du livre LAP LAMBERT Academic Publishing. Paperback. État : New. Paperback. 228 pages. Dimensions: 8.7in. x 5.9in. x 0.5in.Companies often develop in a non-disciplined manner a set of software variants that share some features and dier in others to meet variant-specific requirements. To exploit existing software variants and manage them coherently as a software product line, a feature model must be built as a first step. To do so, it is necessary to extract mandatory and optional features from the code of the variants in addition to associate each feature implementation with its name. In this book, we propose an automatic approach to organize the mined documented features into a feature model. The feature model is a tree which highlights mandatory features, optional features and feature groups (and, or, xor groups). The feature model is completed with requirement and mutual exclusion constraints. We rely on Formal Concept Analysis and software configurations to mine a unique and consistent feature model. To validate our approach, we apply it on several case studies. The results of this evaluation validate the relevance and performance of our proposal as most of the features and their associated constraints are correctly identied. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. N° de réf. du libraire 9783659614521

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Al-Msie deen Ra fat, Huchard Marianne, Urtado Christelle
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Description du livre LAP Lambert Academic Publishing, United States, 2014. Paperback. État : New. Language: English . Brand New Book ***** Print on Demand *****.Companies often develop in a non-disciplined manner a set of software variants that share some features and di er in others to meet variant-specific requirements. To exploit existing software variants and manage them coherently as a software product line, a feature model must be built as a first step. To do so, it is necessary to extract mandatory and optional features from the code of the variants in addition to associate each feature implementation with its name. In this book, we propose an automatic approach to organize the mined documented features into a feature model. The feature model is a tree which highlights mandatory features, optional features and feature groups (and, or, xor groups). The feature model is completed with requirement and mutual exclusion constraints. We rely on Formal Concept Analysis and software configurations to mine a unique and consistent feature model. To validate our approach, we apply it on several case studies. The results of this evaluation validate the relevance and performance of our proposal as most of the features and their associated constraints are correctly identi ed. N° de réf. du libraire AAV9783659614521

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