Synthesis of Attributed Feature Models From Product Descriptions: Foundations

Feature modeling is a widely used formalism to characterize a set of products (also called configurations). As a manual elaboration is a long and arduous task, numerous techniques have been proposed to reverse engineer feature models from various kinds of artefacts. But none of them synthesize feature attributes (or constraints over attributes) despite the practical relevance of attributes for documenting the different values across a range of products. In this report, we develop an algorithm for synthesizing attributed feature models given a set of product descriptions. We present sound, complete, and parametrizable techniques for computing all possible hierarchies, feature groups, placements of feature attributes, domain values, and constraints. We perform a complexity analysis w.r.t. number of features, attributes, configurations, and domain size. We also evaluate the scalability of our synthesis procedure using randomized configuration matrices. This report is a first step that aims to describe the foundations for synthesizing attributed feature models

A case study for NoC based homogeneous MPSoC architectures

The many-core design paradigm requires flexible and modular hardware and software components to provide the required scalability to next-generation on-chip multiprocessor architectures. A multidisciplinary approach is necessary to consider all the interactions between the different components of the design. In this paper, a complete design methodology that tackles at once the aspects of system level modeling, hardware architecture, and programming model has been successfully used for the implementation of a multiprocessor network-on-chip (NoC)-based system, the NoCRay graphic accelerator. The design, based on 16 processors, after prototyping with field-programmable gate array (FPGA), has been laid out in 90-nm technology. Post-layout results show very low power, area, as well as 500 MHz of clock frequency. Results show that an array of small and simple processors outperform a single high-end general purpose processo

Ontology based contextualization and context constraints management in web service processes

The flexibility and dynamism of service-based applications impose shifting the validation process to runtime; therefore, runtime monitoring of dynamic features attached to service-based systems is becoming an important direction of research that motivated the definition of our work. We propose an ontology based contextualization and a framework and techniques for managing context constraints in a Web service process for dynamic requirements validation monitoring at process runtime. Firstly, we propose an approach to define and model dynamic service context attached to composition and execution of services in a service process at run-time. Secondly, managing context constraints are defined in a framework, which has three main processes for context manipulation and reasoning, context constraints generation, and dynamic instrumentation and validation monitoring of context constraints. The dynamic requirements attached to service composition and execution are generated as context constraints. The dynamic service context modeling is investigated based on empirical analysis of application scenarios in the classical business domain and analysing previous models in the literature. The orientation of context aspects in a general context taxonomy is considered important. The Ontology Web Language (OWL) has many merits on formalising dynamic service context such as shared conceptualization, logical language support for composition and reasoning, XML based interoperability, etc. XML-based constraint representation is compatible with Web service technologies. The analysis of complementary case study scenarios and expert opinions through a survey illustrate the validity and completeness of our context model. The proposed techniques for context manipulation, context constraints generation, instrumentation and validation monitoring are investigated through a set of experiments from an empirical evaluation. The analytical evaluation is also used to evaluate algorithms. Our contributions and evaluation results provide a further step towards developing a highly automated dynamic requirements management system for service processes at process run-time

Réagir et s’adapter à son environnement: Concevoir des méthodes autonomes pour l’optimisation combinatoire à plusieurs objectifs

Large-scale optimisation problems are usually hard to solve optimally. Approximation algorithms such as metaheuristics, able to quickly find sub-optimal solutions, are often preferred. This thesis focuses on multi-objective local search (MOLS) algorithms, metaheuristics able to deal with the simultaneous optimisation of multiple criteria. As many algorithms, metaheuristics expose many parameters that significantly impact their performance. These parameters can be either predicted and set before the execution of the algorithm, or dynamically modified during the execution itself.While in the last decade many advances have been made on the automatic design of algorithms, the great majority of them only deal with single-objective algorithms and the optimisation of a single performance indicator such as the algorithm running time or the final solution quality. In this thesis, we investigate the relations between automatic algorithm design and multi-objective optimisation, with an application on MOLS algorithms.We first review possible MOLS strategies ans parameters and present a general, highly configurable, MOLS framework. We also propose MO-ParamILS, an automatic configurator specifically designed to deal with multiple performance indicators. Then, we conduct several studies on the automatic offline design of MOLS algorithms on multiple combinatorial bi-objective problems. Finally, we discuss two online extensions of classical algorithm configuration: first the integration of parameter control mechanisms, to benefit from having multiple configuration predictions; then the use of configuration schedules, to sequentially use multiple configurations.Les problèmes d’optimisation à grande échelle sont généralement difficiles à résoudre de façon optimale. Des algorithmes d’approximation tels que les métaheuristiques, capables de trouver rapidement des solutions sous-optimales, sont souvent préférés. Cette thèse porte sur les algorithmes de recherche locale multi-objectif (MOLS), des métaheuristiques capables de traiter l’optimisation simultanée de plusieurs critères. Comme de nombreux algorithmes, les MOLS exposent de nombreux paramètres qui ont un impact important sur leurs performances. Ces paramètres peuvent être soit prédits et définis avant l’exécution de l’algorithme, soit ensuite modifiés dynamiquement.Alors que de nombreux progrès ont récemment été réalisés pour la conception automatique d’algorithmes, la grande majorité d’entre eux ne traitent que d’algorithmes mono-objectif et l’optimisation d’un unique indicateur de performance. Dans cette thèse, nous étudions les relations entre la conception automatique d’algorithmes et l’optimisation multi-objective.Nous passons d’abord en revue les stratégies MOLS possibles et présentons un framework MOLS général et hautement configurable. Nous proposons également MO-ParamILS, un configurateur automatique spécialement conçu pour gérer plusieurs indicateurs de performance. Nous menons ensuite plusieurs études sur la conception automatique de MOLS sur de multiples problèmes combinatoires bi-objectifs. Enfin, nous discutons deux extensions de la configuration d’algorithme classique : d’abord l’intégration des mécanismes de contrôle de paramètres, pour bénéficier de multiples prédictions de configuration; puis l’utilisation séquentielle de plusieurs configurations

FaaS: Federation-as-a-Service

This document is the main high-level architecture specification of the SUNFISH cloud federation solution. Its main objective is to introduce the concept of Federation-as-a-Service (FaaS) and the SUNFISH platform. FaaS is the new and innovative cloud federation service proposed by the SUNFISH project. The document defines the functionalities of FaaS, its governance and precise objectives. With respect to these objectives, the document proposes the high-level architecture of the SUNFISH platform: the software architecture that permits realising a FaaS federation. More specifically, the document describes all the components forming the platform, the offered functionalities and their high-level interactions underlying the main FaaS functionalities. The document concludes by outlining the main implementation strategies towards the actual implementation of the proposed cloud federation solution.Comment: Technical Report Edited by Francesco Paolo Schiavo, Vladimiro Sassone, Luca Nicoletti and Andrea Margher

Supporting Change in Product Lines Within the Context of Use Case-driven Development and Testing

Product Line Engineering (PLE) is a crucial practice in many software development environments where systems are complex and developed for multiple customers with varying needs. At the same time, many business contexts are use case-driven where use cases are the main artifacts driving requirements elicitation and many other development activities. In these contexts, variability information is often not explicitly represented, which leads to ad-hoc change management for use cases, domain models and test cases in product families. In this thesis, we address the problems of modeling variability in requirements with additional traceability to feature models and the manual and error prone requirements configuration and regression testing in product families. We provide the following contributions: - A modeling method for capturing variability information in product line use case and domain models by relying exclusively on commonly used artifacts in use-case driven development, thus avoiding unnecessary modeling overhead. - An approach for automated configuration of product specific use case and domain models that guides customers in making configuration decisions and automatically generates use case diagrams, use case specifications, and domain models for configured products. - A change impact analysis approach for evolving configuration decisions in product line use case models that automatically identifies the impact of decision changes on other decisions, and incrementally reconfigures product specific use case diagrams and specifications for evolving decisions. - An approach for automated classification and prioritization of system test cases in a family of products that automatically classifies and prioritizes, for each new product, system test cases of previous product(s) in a product line, and provides guidance in modifying existing system test cases to cover new use case scenarios that have not been tested in the product line before. All our approaches have been developed and evaluated in close collaboration with our industry partner IEE