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Mathieu Acher

Bio: Mathieu Acher is an academic researcher from University of Rennes. The author has contributed to research in topics: Software product line & Feature model. The author has an hindex of 25, co-authored 117 publications receiving 2140 citations. Previous affiliations of Mathieu Acher include Université de Namur & University of Nice Sophia Antipolis.


Papers
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Journal ArticleDOI
TL;DR: FAMILIAR is presented as a Domain-Specific Language (DSL) that is dedicated to the large scale management of feature models and that complements existing tool support and demonstrates their applicability to different domains and use for different purposes.

196 citations

Proceedings ArticleDOI
25 Jan 2012
TL;DR: This paper aims at easing the transition from product descriptions expressed in a tabular format to FMs accurately representing them, and guarantees that the resulting FM represents the set of legal feature combinations supported by the considered products and has a readable tree hierarchy together with variability information.
Abstract: In product line engineering, domain analysis is the process of analyzing related products to identify their common and variable features. This process is generally carried out by experts on the basis of existing product descriptions, which are expressed in a more or less structured way. Modeling and reasoning about product descriptions are error-prone and time consuming tasks. Feature models (FMs) constitute popular means to specify product commonalities and variabilities in a compact way, and to provide automated support to the domain analysis process. This paper aims at easing the transition from product descriptions expressed in a tabular format to FMs accurately representing them. This process is parameterized through a dedicated language and high-level directives (e.g., products/features scoping). We guarantee that the resulting FM represents the set of legal feature combinations supported by the considered products and has a readable tree hierarchy together with variability information. We report on our experiments based on public data and characterize the properties of the derived FMs.

157 citations

Proceedings ArticleDOI
18 Aug 2013
TL;DR: This paper presents a novel, automated approach for constructing FMs from publicly available product descriptions found in online product repositories and marketing websites such as SoftPedia and CNET.
Abstract: Feature Models (FMs) are used extensively in software product line engineering to help generate and validate individual product configurations and to provide support for domain analysis. As FM construction can be tedious and time-consuming, researchers have previously developed techniques for extracting FMs from sets of formally specified individual configurations, or from software requirements specifications for families of existing products. However, such artifacts are often not available. In this paper we present a novel, automated approach for constructing FMs from publicly available product descriptions found in online product repositories and marketing websites such as SoftPedia and CNET. While each individual product description provides only a partial view of features in the domain, a large set of descriptions can provide fairly comprehensive coverage. Our approach utilizes hundreds of partial product descriptions to construct an FM and is described and evaluated against antivirus product descriptions mined from SoftPedia.

143 citations

Book ChapterDOI
05 Oct 2009
TL;DR: This paper proposes a set of composition operators dedicated to feature models that enable the development of large feature models by composing smaller feature models which address well-defined concerns.
Abstract: Feature modeling is a widely used technique in Software Product Line development. Feature models allow stakeholders to describe domain concepts in terms of commonalities and differences within a family of software systems. Developing a complex monolithic feature model can require significant effort and restrict the reusability of a set of features already modeled. We advocate using modeling techniques that support separating and composing concerns to better manage the complexity of developing large feature models. In this paper, we propose a set of composition operators dedicated to feature models. These composition operators enable the development of large feature models by composing smaller feature models which address well-defined concerns. The operators are notably distinguished by their documented capabilities to preserve some significant properties.

96 citations

Book ChapterDOI
13 Sep 2011
TL;DR: This paper develops automated techniques to extract and combine different variability descriptions of an architecture, and applies alignment and reasoning techniques to integrate the architect knowledge and reinforce the extracted FM.
Abstract: Reverse engineering the variability of an existing system is a challenging activity. The architect knowledge is essential to identify variation points and explicit constraints between features, for instance in feature models (FMs), but the manual creation of FMs is both time-consuming and error-prone. On a large scale, it is very difficult for an architect to guarantee that the resulting FM is consistent with the architecture it is associated with. In this paper, we present a comprehensive, tool supported process for reverse engineering architectural FMs. We develop automated techniques to extract and combine different variability descriptions of an architecture. Then, alignment and reasoning techniques are applied to integrate the architect knowledge and reinforce the extracted FM. We illustrate the process when applied to a representative software system and we report on our experience in this context.

75 citations


Cited by
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01 Jan 2004
TL;DR: Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance and describes numerous important application areas such as image based rendering and digital libraries.
Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

3,627 citations

Journal Article
TL;DR: A framework for model driven engineering is set out, which proposes an organisation of the modelling 'space' and how to locate models in that space, and identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions.
Abstract: The Object Management Group's (OMG) Model Driven Architecture (MDA) strategy envisages a world where models play a more direct role in software production, being amenable to manipulation and transformation by machine. Model Driven Engineering (MDE) is wider in scope than MDA. MDE combines process and analysis with architecture. This article sets out a framework for model driven engineering, which can be used as a point of reference for activity in this area. It proposes an organisation of the modelling 'space' and how to locate models in that space. It discusses different kinds of mappings between models. It explains why process and architecture are tightly connected. It discusses the importance and nature of tools. It identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions. It concludes with a call to align metamodelling with formal language engineering techniques.

1,476 citations

Book ChapterDOI
20 Dec 2013

780 citations

Proceedings Article
01 Jan 2002
TL;DR: In this paper, an algorithm for generating attack graphs using model checking as a subroutine is presented, which allows analysts to decide which minimal set of security measures would guarantee the safety of the system.
Abstract: An attack graph is a succinct representation of all paths through a system that end in a state where an intruder has successfully achieved his goal. Today Red Teams determine the vulnerability of networked systems by drawing gigantic attack graphs by hand. Constructing attack graphs by hand is tedious, error-prone, and impractical for large systems. By viewing an attack as a violation of a safety property, we can use off-the-shelf model checking technology to produce attack graphs automatically: a successful path from the intruder's viewpoint is a counterexample produced by the model checker In this paper we present an algorithm for generating attack graphs using model checking as a subroutine. Security analysts use attack graphs for detection, defense and forensics. In this paper we present a minimization analysis technique that allows analysts to decide which minimal set of security measures would guarantee the safety of the system. We provide a formal characterization of this problem: we prove that it is polynomially equivalent to the minimum hitting set problem and we present a greedy algorithm with provable bounds. We also present a reliability analysis technique that allows analysts to perform a simple cost-benefit trade-off depending on the likelihoods of attacks. By interpreting attack graphs as Markov Decision Processes we can use the value iteration algorithm to compute the probabilities of intruder success for each attack the graph.

467 citations

Posted Content
TL;DR: The main advances regarding the use of the Choquet and Sugeno integrals in multi-criteria decision aid over the last decade are reviewed in this paper, mainly a bipolar extension of both Choquet integral and the Sugeno integral.
Abstract: The main advances regarding the use of the Choquet and Sugeno integrals in multi-criteria decision aid over the last decade are reviewed. They concern mainly a bipolar extension of both the Choquet integral and the Sugeno integral, interesting particular submodels, new learning techniques, a better interpretation of the models and a better use of the Choquet integral in multi-criteria decision aid. Parallel to these theoretical works, the Choquet integral has been applied to many new fields, and several softwares and libraries dedicated to this model have been developed.

449 citations