Domain Specific Languages for Managing Feature Models: Advances and Challenges

International audienceManaging multiple and complex feature models is a tedious and error-prone activity in software product line engineering. Despite many advances in formal methods and analysis techniques, the supporting tools and APIs are not easily usable together, nor unified. In this paper, we report on the development and evolution of the Familiar Domain-Specific Language (DSL). Its toolset is dedicated to the large scale management of feature models through a good support for separating concerns, composing feature models and scripting manipulations. We overview various applications of Familiar and discuss both advantages and identified drawbacks. We then devise salient challenges to improve such DSL support in the near future

Feature Model Differences

International audienceFeature models are a widespread means to represent commonality and variability in software product lines. As is the case for other kinds of models, computing and managing feature model differences is useful in various real-world situations. In this paper, we propose a set of novel differencing techniques that combine syntactic and semantic mechanisms, and automatically produce meaningful differences. Practitioners can exploit our results in various ways: to understand, manipulate, visualize and reason about differences. They can also combine them with existing feature model composition and decomposition operators. The proposed automations rely on satisfiability algorithms. They come with a dedicated language and a comprehensive environment. We illustrate and evaluate the practical usage of our techniques through a case study dealing with a configurable component framework

Empirical assessment of generating adversarial configurations for software product lines

Software product line (SPL) engineering allows the derivation of products tailored to stakeholders’ needs through the setting of a large number of configuration options. Unfortunately, options and their interactions create a huge configuration space which is either intractable or too costly to explore exhaustively. Instead of covering all products, machine learning (ML) approximates the set of acceptable products (e.g., successful builds, passing tests) out of a training set (a sample of configurations). However, ML techniques can make prediction errors yielding non-acceptable products wasting time, energy and other resources. We apply adversarial machine learning techniques to the world of SPLs and craft new configurations faking to be acceptable configurations but that are not and vice-versa. It allows to diagnose prediction errors and take appropriate actions. We develop two adversarial configuration generators on top of state-of-the-art attack algorithms and capable of synthesizing configurations that are both adversarial and conform to logical constraints. We empirically assess our generators within two case studies: an industrial video synthesizer (MOTIV) and an industry-strength, open-source Web-app configurator (JHipster). For the two cases, our attacks yield (up to) a 100% misclassification rate without sacrificing the logical validity of adversarial configurations. This work lays the foundations of a quality assurance framework for ML-based SPLs

Sensor Data Visualisation: A Composition-Based Approach to Support Domain Variability

International audienceIn the context of the Internet of Things, sensors are surrounding our environment. These small pieces of electronics are inserted in everyday life's elements (e.g., cars, doors, radiators, smartphones) and continuously collect information about their environment. One of the biggest challenges is to support the development of accurate monitoring dashboard to visualise such data. The one-size-fits-all paradigm does not apply in this context, as user's roles are variable and impact the way data should be visualised: a building manager does not need to work on the same data as classical users. This paper presents an approach based on model composition techniques to support the development of such monitoring dashboards, taking into account the domain variability. This variability is supported at both implementation and modelling levels. The results are validated on a case study named SmartCampus, involving sensors deployed in a real academic campus

Multifocal Aggressive Squamous Cell Carcinomas Induced by Prolonged Voriconazole Therapy: A Case Report

Voriconazole is a treatment for severe fungal infections. Prolonged voriconazole therapy may induce skin reactions, with 1% of severe photosensitivity accidents. Recently the imputability of voriconazole in skin carcinogenesis has been suggested. This report concerns a 55-year-old man suffering from pulmonary aspergillosis who presented a phototoxic reaction a few months after introduction of voriconazole, followed by multiple squamous cell carcinomas of sun-exposed skin areas. After voriconazole discontinuation, no new carcinoma was observed. The detection of EBV and HPV in skin lesions was negative. Exploration of gene mutations involved in skin carcinogenesis showed two variants of the MICR gene. The occurrence of multiple, recurrent, aggressive squamous cell carcinomas is rare with voriconazole, but its imputability is strongly suggested. A plausible hypothesis is that several factors including voriconazole uptake, immunosuppression, and genetic background could explain the phenotype of fast-developing skin carcinomas. Voriconazole therapy should be accompanied by stringent photoprotection and skin monitoring

Nematic liquid crystal alignment on chemical patterns

Patterned Self-Assembled Monolayers (SAMs) promoting both homeotropic and planar degenerate alignment of 6CB and 9CB in their nematic phase, were created using microcontact printing of functionalised organothiols on gold films. The effects of a range of different pattern geometries and sizes were investigated, including stripes, circles and checkerboards. EvanescentWave Ellipsometry was used to study the orientation of the liquid crystal (LC) on these patterned surfaces during the isotropic-nematic phase transition. Pretransitional growth of a homeotropic layer was observed on 1 ¹m homeotropic aligning stripes, followed by a homeotropic mono-domain state prior to the bulk phase transition. Accompanying Monte-Carlo simulations of LCs aligned on nano-patterned surfaces were also performed. These simulations also showed the presence of the homeotropic mono-domain state prior to the transition.</p

Multi-objective genetic algorithm applied to spectroscopic ellipsometry of organic-inorganic hybrid planar waveguides

The applicably of multi-objective optimization to ellipsometric data analysis is presented and a method to handle complex ellipsometric problems such as multi sample or multi angle analysis using multi-objective optimization is described. The performance of a multi-objective genetic algorithm (MOGA) is tested against a single objective common genetic algorithm (CGA). The procedure is applied to the characterization (refractive index and thickness) of planar waveguides intended for the production of optical components prepared sol-gel derived organic-inorganic hybrids, so-called di-ureasils, modified with zirconium tetrapropoxide, Zr(OPr(n))(4) deposited on silica on silicon substrates. The results show that for the same initial conditions, MOGA performs better than the CGA, showing a higher success rate in the task of finding the best final solution. (C) 2010 Optical Society of AmericaFCTFEDERPTDC/CTM/72093/2006SFRH/BD/41943/2007MP070

Evaluating the usability of a visual feature modeling notation

International audienceFeature modeling is a popular Software Product Line Engineering (SPLE) technique used to describe variability in a product family. A usable feature modeling tool environment should enable SPLE practitioners to produce good quality models, in particular, models that effectively communicate modeled information. FAMILIAR is a text-based environment for manipulating and composing Feature Models (FMs). In this paper we present extensions we made to FAMILIAR to enhance its usability. The extensions include a visualization of FMs, or more precisely , a feature diagram rendering mechanism that supports the use of a combination of text and graphics to describe FMs, their configurations, and the results of FM analyses. We also present the results of a preliminary evaluation of the environment's usability. The evaluation involves comparing the use of the extended environment with the previous text-based console-driven version. The preliminary experiment provides some evidence that use of the new environment results in increased cognitive effectiveness of novice users and improved quality of new FMs