Mining Features from the Object-Oriented Source Code of a Collection of Software Variants Using Formal Concept Analysis and Latent Semantic Indexing

Abstract : Companies often develop a set of software variants that share some features and differ in other ones to meet specific requirements. To exploit existing software variants and build a software product line (SPL), a feature model of this SPL must be built as a first step. To do so, it is necessary to mine optional and mandatory features from the source code of the software variants. Thus, we propose, in this paper, a new approach to mine features from the object-oriented source code of a set of software variants based on Formal Concept Analysis and Latent Semantic Indexing. To validate our approach, we applied it on ArgoUML and Mobile Media case studies. The results of this evaluation validate the relevance and the performance of our proposal as most of the features were correctly identified.
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Ra'Fat Ahmad Al-Msie'Deen, Abdelhak-Djamel Seriai, Marianne Huchard, Christelle Urtado, Sylvain Vauttier, et al.. Mining Features from the Object-Oriented Source Code of a Collection of Software Variants Using Formal Concept Analysis and Latent Semantic Indexing. SEKE: Software Engineering and Knowledge Engineering, Jun 2013, Portland, OR, United States. ⟨lirmm-00824184⟩

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