Relational Data Exploration by Relational Concept Analysis

Relational Concept Analysis [4] is an extension to FCA con- sidering several contexts with relations between them. Often used to extend the knowledge that can be learned with FCA, RCA also meets the issue of combinatorial explosion. The initial specification of RCA implies a monotonic growth of the number of concepts and an exhaustiveness of all the concepts that can be obtained when a fixed point is reached. In this position paper we propose a different specification of RCA that permits an interactive exploration of the data by letting the choice of the user for each step. This change will permit to handle richer relational data in a more flexible way by restraining the relations explored at each step hence reducing the number of created concepts

Learning Model Transformations from Examples using FCA: One for All or All for One?

International audienceIn Model-Driven Engineering (MDE), model transformations are basic and primordial entities. An efficient way to assist the definition of these transformations consists in completely or partially learning them. MTBE (Model Transformation By-Example) is an approach that aims at learning a model transformation from a set of examples, i.e. pairs of transformation source and target models. To implement this approach, we use Formal Concept Analysis as a learning mechanism in order to extract executable rules. In this paper, we investigate two learning strategies. In the first strategy, transformation rules are learned independently from each example. Then we gather these rules into a single set of rules. In the second strategy, we learn the set of rules from all the examples. The comparison of the two strategies on the well-known transformation problem of class diagrams to relational schema showed that the rules obtained from the two strategies are interesting. Besides the first one produces rules which are more proper to their examples and apply well compared to the second one which builds more detailed rules but larger and more difficult to analyze and to apply

Class Model Normalization Outperforming Formal Concept Analysis approaches with AOC-posets

International audienceDesigning or reengineering class models in the domain of programming or modeling involves capturing technical and domain concepts , finding the right abstractions and avoiding duplications. Making this last task in a systematic way corresponds to a kind of model nor-malization. Several approaches have been proposed, that all converge towards the use of Formal Concept Analysis (FCA). An extension of FCA to linked data, Relational Concept Analysis (RCA) helped to mine better reusable abstractions. But RCA relies on iteratively building concept lattices, which may cause a combinatorial explosion in the number of the built artifacts. In this paper, we investigate the use of an alternative RCA process, relying on a specific sub-order of the concept lattice (AOC-poset) which preserves the most relevant part of the normal form. We measure, on case studies from Java models extracted from Java code and from UML models, the practical reduction that AOC-posets bring to the normal form of the class model

Un Framework de traçabilité pour des transformations à caractère impératif

National audienceCet article s’inscrit dans le cadre de l’ingénierie dirigée par les mo- dèles et apporte une contribution au problème de la traçabilité des artefacts de modélisation durant une chaîne de transformations écrites dans un langage impé- ratif. L’approche que nous proposons nécessite peu d’interventions de l’utilisa- teur. Nous introduisons un métamodèle générique des traces qui permet entre autres d’apporter une dimension multi-échelles aux traces grâce à l’applica- tion du patron de conception composite. Le principe de notre approche est de surveiller certaines catégories d’opérations intéressantes pour la génération de traces pertinentes. Ces catégories sont définies à l’aide du type des objets mani- pulés par les opérations. Une fois les catégories définies, la trace est générée par du code dédié qui est injecté automatiquement dans la transformation, autour des opérations caractérisées par les catégories définies. Un prototype a été réa- lisé pour les transformations de modèles écrites en Java, sur le framework EMF. L’injection du code dédié à la traçabilité est réalisée à l’aide de la programmation par aspects

Utilisation de l'analyse formelle de concepts pour extraire le plus grand modèle commun

International audienceThe development of information systems follows a long and complex process in which various actors are involved. We report an experiment in which we observe the evolution of the analysis model of an information system through 15 successive versions. We use indicators on the underlying concept lattices built by applying Relational Concept Analysis (RCA) to each version. RCA is an extension of FCA which groups entities based on characteristics they share, including links to other entities. It here helps in analyzing their evolution. From this experience, we establish recommendations to monitor and verify the proper evolution of the analysis process

Automated Requirements-based Generation of Test Cases for Product Families

Software product families (PF) are becoming one of the key challenges of software engineering. Despite recent interest in this area, the extent to which the close relationship between PF and requirements engineering is exploited to guide the V&V tasks is still limited. In particular, PF processes generally lack support for generating test cases from requirements. In this paper, we propose a requirements-based approach to functional testing of product lines, based on a formal test generation tool. Here, we outline how product-specific test cases can be automatically generated from PF functional requirements expressed in UML. We study the efficiency of the generated test cases on a case study

A Framework for Concurrent Design of Metamodels and Diagrams: Towards an Agile Method for the Synthesis of Domain Specific Graphical Modeling Languages

International audienceDSML (Domain Specific Modeling Languages) are an alternative to general purpose modeling languages (e.g. UML or SysML) for describing models with concepts and relations specific to a domain. DSML design is often based on Ecore metamodels, which follow the class-relation paradigm and also require defining a concrete syntax which can be either graphical or textual. In this paper, we focus on graphical concrete syntax, and we introduce an approach and a tool (Diagraph) to assist the design of a graphical DSML. The main principles are: non-intrusive annotations of the metamodel to identify nodes, edges, nesting structures and other graphical information; immediate validation of metamodels by immediate generation of an EMF-GMF instance editor supporting multi-diagramming. We report a comparison experience between Diagraph and Obeo Designer (a commercial proprietary tool), which was conducted as part of a Model Driven Engineering Course

Modelling equivalence classes of feature models with concept lattices to assist their extraction from product descriptions

International audienceSoftware product line engineering gathers a set of methods to help create, manage and maintain a collection of similar software systems. Variability modelling is a focal point of this paradigm, where feature models (FMs) are the prevalent notation. Migration from single system development to software product lines is a spreading topic in software engineering. To ease the migration, research has been done to automatically extract FMs from software descriptions, but most of these approaches are defined in a functional manner based on an ad-hoc variability analysis. In this paper, we propose a theoretical view on FM extraction from software descriptions based on Formal Concept Analysis (FCA). It is a structural framework for variability representation which allows to lay down theoretical foundation to variability extraction. We propose an original mapping between relationships expressed in FMs and the ones emphasised in FCA conceptual structures. We show that conceptual structures represent equivalence classes of FMs that steer the user choices during their synthesis, and propose a reverse engineering method based on them. We discuss its applicability and show that the combinatorial explosion of concept lattices can be avoided by the use of two sub-orders embodying the necessary information concerning variability

Génération automatique de tests à partir des exigences et application aux lignes de produits logicielles

La contribution de cette thèse est une approche de génération automatique de tests fonctionnels à partir des exigences, prenant en compte la maîtrise du coût de test, l'adaptabilité au contexte des lignes de produits, la compatibilité avec les pratiques industrielles et la complexité des logiciels réels. Notre approche se base sur un modèle de cas d'utilisation étendus, relié à un analyseur de langage naturel contrôlé en amont et un générateur de tests en aval. Le langage contrôlé rapproche la méthode des pratiques industrielles, et formalise assez les exigences pour les transformer en un modèle de cas d'utilisation simulables (via l'ajout de contrats interprétables). Des critères de test permettent alors de générer des objectifs de test de haut niveau, qui sont ensuite raffinés vers des cas de test en utilisant des scénarios. La variabilité dans les exigences est prise en compte à chaque niveau de la génération de tests, cette approche est donc adaptée aux lignes de produits.RENNES1-BU Sciences Philo (352382102) / SudocSudocFranceF

Towards Complex Product Line Variability Modelling: Mining Relationships from Non-Boolean Descriptions

International audienceSoftware product line engineering relies on systematic reuse and mass customisation to reduce the development time and cost of a software system family. The extractive adoption of a product line requires to extract variability information from the description of a collection of existing software systems to model their variability. With the increasing complexity of software systems, software product line engineering faces new challenges including variability extraction and modelling. Extensions of existing boolean variability models, such as multi-valued attributes or UML-like cardinalities, were proposed to enhance their expressiveness and support variability modelling in complex product lines. In this paper, we propose an approach to extract complex variability information, i.e., involving features as well as multi-valued attributes and cardinalities, in the form of logical relationships. This approach is based on Formal Concept Analysis and Pattern Structures, two mathematical frameworks for knowledge discovery that bring theoretical foundations to complex variability extraction algorithms. We present an application on product comparison matrices representing complex descriptions of software system families. We show that our method does not suffer from scalability issues and extracts all pertinent relationships, but that it also extracts numerous accidental relationships that need to be filtered