Construction and Verification of Performance and Reliability Models

Over the last two decades formal methods have been extended towards performance and reliability evaluation. This paper tries to provide a rather intuitive explanation of the basic concepts and features in this area. Instead of striving for mathematical rigour, the intention is to give an illustrative introduction to the basics of stochastic models, to stochastic modelling using process algebra, and to model checking as a technique to analyse stochastic models

A foundation for machine learning in design

This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, "What types of knowledge can be learnt?", "How does learning occur?", and "When does learning occur?". Five main elements of MLinD are presented as the input knowledge, knowledge transformers, output knowledge, goals/reasons for learning, and learning triggers. Using this foundation, published systems in MLinD were reviewed. The systematic review presents a basis for validating the presented foundation. The paper concludes that there is considerable work to be carried out in order to fully formalize the foundation of MLinD

Automated analysis of feature models: Quo vadis?

Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.Ministerio de Economía y Competitividad TIN2015-70560-RJunta de Andalucía TIC-186

Compositional Verification of Evolving SPL

This paper presents a novel approach to the design verification of Software Product Lines(SPL). The proposed approach assumes that the requirements and designs are modeled as finite state machines with variability information. The variability information at the requirement and design levels are expressed differently and at different levels of abstraction. Also the proposed approach supports verification of SPL in which new features and variability may be added incrementally. Given the design and requirements of an SPL, the proposed design verification method ensures that every product at the design level behaviorally conforms to a product at the requirement level. The conformance procedure is compositional in the sense that the verification of an entire SPL consisting of multiple features is reduced to the verification of the individual features. The method has been implemented and demonstrated in a prototype tool SPLEnD (SPL Engine for Design Verification) on a couple of fairly large case studies.Ce papier présente une approche nouvelle de vérification pour les lignes de produits logiciels (LPL). L'approche proposée considère que la spécification et la conception de LPL peuvent être abstraites comme des automates à états finis comprenant des informations sur la variabilité. Ces informations sont exprimées différemment aux niveaux spécification et conceptions. Sous ces hypothèses, l'approche proposée supporte la vérification de LPLs dans lesquelles des fonctionnalités peuvent être ajoutées incrémentalement. A partir de la spécification et de la conception d'une LPL, la méthode de vérification proposée assure que chaque produit au niveau conception se conforme, comportementalement parlant, à un produit au niveau spécification. La procédure de conformité est compositionnelle car la vérification de la LPL en entier se réduit à la vérification des fonctionnalités qui la compose individuellement. La méthode a été implantée dans un outil appelé ''SPLEnD'' et essayée sur deux cas d'étude relativement larges