Jessie Carbonnel

Feature Model Composition Assisted by Formal Concept Analysis.

In the domain of software product lines, Feature Models (FM) play a central role in variability modeling, completed by configuration collections (from concrete software product lines), logical representations, constraint programming or conceptual structures, coming from the field of Formal Concept Analysis (FCA). The development of feature models may take several forms, including their synthesis from configuration collections or their design in several steps (by several teams or with different concerns). FM composition (merge) operators are part of that design activity as they assist their iterative building. In this paper, we describe an approach, based on two main merging semantics (intersection and union), which assists designers in merging several FMs. This approach benefits from the help of FCA to represent all the FMs with the same configuration set through a canonical form. We describe the implementation of our approach and present a set of concrete examples.

Analyzing Variability in Product Families through Canonical Feature Diagrams

Product line engineering aims to reduce the cost and effort to develop new related softwares, while increasing the software quality and the software scope. Variability analysis and modeling is a key issue in this approach. Several representations were proposed, including feature models (FMs) and product comparison matrices (PCMs). While PCMs are useful for presenting products in a tabular form, for their understanding and manipulation, it helps to switch to a graphical view. FMs are graphical views, but they are not canonical (i.e., several equivalent FMs can represent a same PCM) and user intervention is necessary to ensure the extraction of a meaningful FM from PCMs. In this paper, we investigate the benefits of a new structure, which captures variability in a canonical graphical representation. We outline its construction and we give insights about its shape and use when it is used as an alternative representation of wikipedia PCMs in the domain of software.

On-Demand Generation of AOC-Posets: Reducing the Complexity of Conceptual Navigation

Exploratory search allows to progressively discover a dataspace by browsing through a structured collection of documents. Concept lattices are graph structures which support exploratory search by conceptual navigation, i.e., navigating from concept to concept by selecting and deselecting descriptors. These methods are known to be limited by the size of concept lattices which can be too large to be efficiently computed or too complex to be browsed intelligibly. In this paper, we address the problem of providing techniques that reduce the complexity of FCA-based exploratory search. We show the suitability of AOC-posets, a condensed alternative structure to achieve conceptual navigation. Also, we outline algorithms to enable an on-demand generation of AOC-posets. The necessity to devise more flexible methods to perform product selection in software product line engineering is what motivates our work.

Poster: On Extracting Relevant and Complex Variability Information from Software Descriptions with Pattern Structures

The migration from existing software variants to a software product line is an arduous task that necessitates to synthesise a variability model based on already developed softwares. Nowadays, the increasing complexity of software product lines compels practitioners to design more complex variability models that represent other information than binary features, e.g., multi-valued attributes. Assisting the extraction of complex variability models from variant descriptions is a key task to help the migration towards complex software product lines. In this paper, we address the problem of extracting complex variability information from software descriptions, as a part of the process of complex variability model synthesis. We propose an approach based on Pattern Structures to extract variability information, in the form of logical relationships involving both binary features and multi-valued attributes.

Assisting Configurations-Based Feature Model Composition

Feature Models (FMs) have been introduced in the domain of Software Product Lines (SPL) to model and represent product variability. They have become a de facto standard, based on a logical tree structure accompanied by textual cross-tree constraints. Other representations are: (product) configuration sets from concrete software product lines, logical representations, constraint programming, or conceptual structures, coming from the Formal Concept Analysis (FCA) framework. Modeling variability through FMs may consist in extracting them from configuration sets (namely, doing FM synthesis), or designing them in several steps potentially involving several teams with different concerns. FM composition is useful in this design activity as it may assist FM iterative building. In this paper, we describe an approach, based on a configuration set and focusing on two main composition semantics (union, intersection), to assist designers in FM composition. We also introduce an approximate intersection notion. FCA is used to represent, for a product family, all the FMs that have the same configuration set through a canonical form. The approach is able to take into account cross-tree constraints and FMs with different feature sets and tree structure, thus it lets the expert free of choosing a different ontological interpretation. We describe the implementation of our approach and we present a set of concrete examples.