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

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

Vitellogenin Underwent Subfunctionalization to Acquire Caste and Behavioral Specific Expression in the Harvester Ant Pogonomyrmex barbatus

PMCID: PMC3744404This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication

A generative-oriented model-driven design environment for customizable video surveillance systems

To tackle the growing complexity and huge demand for tailored domestic video surveillance systems along with a high demanding time-to-market expectation, engineers at IVV Automação, LDAa are exploiting video surveillance domain as families of systems that can be developed following a pay-as-you-go fashion rather than developing an ex-nihilo new product. Several and different new functionalities are required for each new product’s hardware platforms (e.g., ranging from mobile phone, PDA to desktop PC) and operating systems (e.g., flavors of Linux, Windows and MAC OS X). Some of these functionalities have special economical constraints of development time and memory footprint. To better accommodate all the above listing requirements, a model-driven generative software development paradigm supported by mainstream tools is proposed to offer a significant leverage in hiding commonalities and configuring variabilities across families of video surveillance products while maintaining the new product quality.This work was funded through the Competitive Factors Operational Program COMPETE and through national funds though the Science and Technology Foundation - FCT, within the project: FCOMP-01-0124-FEDER-022674. This work was developed in cooperation with IVV Automation; all support and means provided by the company is acknowledged

Diagnostic value of MRI-based PSA density in predicting transperineal sector-guided prostate biopsy outcomes

PURPOSE: Prostate-specific antigen (PSA) density (PSAD) has potential to increase the diagnostic utility of PSA, yet has had poor uptake in clinical practice. We aimed to determine the diagnostic value of magnetic resonance imaging-derived PSAD (MR-PSAD) in predicting transperineal sector-guided prostate biopsy (TPSB) outcomes. MATERIALS AND METHODS: Men presenting for primary TPSB from 2007 to 2014 were considered. Histological outcomes were assessed and defined as: presence of any cancer or significant cancer defined as presence of Gleason 4 and/or maximum tumour core length (MCCL) ≥ 4 mm (G4); or Gleason 4 and/or MCCL ≥ 6 mm (G6). Sensitivity, specificity and positive and negative predictive values were calculated, and receiver operating characteristics (ROC) curves were generated to compare MR-PSAD and PSA. RESULTS: Six hundred fifty-nine men were evaluated with mean age 62.5 ± 9 years, median PSA 6.7 ng/ml (range 0.5-40.0), prostate volume 40 cc (range 7-187) and MR-PSAD 0.15 ng/ml/cc (range 0.019-1.3). ROC area under the curve (95% CI) was significantly better for MR-PSAD than PSA for all cancer definitions (p < 0.001): 0.73 (0.70-0.76) versus 0.61 (0.57-0.64) for any cancer; 0.75 (0.71-0.78) versus 0.66 (0.62-0.69) for G4; and 0.77 (0.74-0.80) versus 0.68 (0.64-0.71) for G6. Sensitivities for MR-PSAD < 0.1 ng/ml/cc were 85.0, 89.9 and 91.9% for any, G4 and G6 cancer, respectively. CONCLUSION: MR-PSAD may be better than total PSA in determining risk of positive biopsy outcome. Its use may improve risk stratification and reduce unnecessary biopsies