Feature Models (FMs) are a popular formalism for modelling and reasoning about commonality and variability of a system. In essence, FMs aim to define a set of valid combinations of features, also called configurations. In this paper, we tackle the problem of synthesising an FM from a set of configurations. The main challenge is that numerous candidate FMs can be extracted from the same input configurations, yet only a few of them are meaningful and maintainable. We first characterise the different meanings of FMs and identify the key properties allowing to discriminate between them. We then develop a generic synthesis procedure capable of restituting the intended meanings of FMs based on inferred or user-specified knowledge. Using tool support, we show how the integration of knowledge into FM synthesis can be realized in different practical application scenarios that involve reverse engineering and maintaining FMs.
Abstract. In the database engineering realm, the merits of transformational approaches, that can produce in a systematic way correct, compilable and efficient database structures from abstract models, has long be recognized. Transformations that are proved to preserve the correctness of the source specifications have been proposed in virtually all the activities related to data structure engineering: schema normalization, logical design, schema integration, view derivation, schema equivalence, data conversion, reverse engineering, schema optimization, wrapper generation and others. This paper addresses both fundamental and practical aspects of database transformation techniques. The concept of transformation is developed, together with its properties of semantics-preservation (or reversibility). Major database engineering activities are redefined in terms of transformation techniques, and the impact on CASE technology is discussed. These principles are applied to database logical design and database reverse engineering. They are illustrated by the use of DB-MAIN, a programmable CASE environment that provides a large transformational toolkit.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.