6,615 research outputs found
Defects in Product Line Models and How to Identify Them
This chapter is about generic (language-independent) verification criteria of product line models, its identification, formalisation, categorization, implementation with constraint programming techniques and its evaluation on several industrial and academic product line models represented with several languages
Execution/Simulation of Context/Constraint-aware Composite Services using GIPSY
For fulfilling a complex requirement comprising of several sub-tasks, a composition of simple web services, each of which is dedicated to performing a specific sub-task involved, proves to be a more competent solution in comparison to an equivalent atomic web service. Owing to advantages such as re-usability of components, broader options for composition requesters and liberty to specialize for component providers, for over two decades now, composite services have been extensively researched to the point of being perfected in many aspects. Yet, most of the studies undertaken in this field fail to acknowledge that every web service has a limited context in which it can successfully perform its tasks, the boundaries of which are defined by the internal constraints placed on the service by its providers. When used as part of a composition, the restricted context-spaces of all such component services together define the contextual boundaries of the composite service as a unit, which makes internal constraints an influential factor for composite service functionality. However, due to the limited exposure received by them, no systems have yet been proposed to cater to the specific verification of internal constraints imposed on components of a composite service. In an attempt to address this gap in service composition research, in this thesis, we propose a multi-faceted solution capable of not only automatically constructing context-aware composite web services with their internal constraints positioned for optimum resource-utilization but also of validating the generated compositions using the General Intensional Programming SYstem (GIPSY) as a time- and cost-efficient simulation/execution environment
Using Constraint Programming to Verify DOPLER Variability Models
Software product lines are typically developed using model-based approaches. Models are used to guide and automate key activities such as the derivation of products. The verification of product line models is thus essential to ensure the consistency of the derived products. While many authors have proposed approaches for verifying feature models there is so far no such approach for decision models. We discuss challenges of analyzing and verifying decision-oriented DOPLER variability models. The manual verification of these models is an error-prone, tedious, and sometimes infeasible task. We present a preliminary approach that converts DOPLER variability models into constraint programs to support their verification. We assess the feasibility of our approach by identifying defects in two existing variability models
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Automated generation of computationally hard feature models using evolutionary algorithms
This is the post-print version of the final paper published in Expert Systems with Applications. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2014 Elsevier B.V.A feature model is a compact representation of the products of a software product line. The automated extraction of information from feature models is a thriving topic involving numerous analysis operations, techniques and tools. Performance evaluations in this domain mainly rely on the use of random feature models. However, these only provide a rough idea of the behaviour of the tools with average problems and are not sufficient to reveal their real strengths and weaknesses. In this article, we propose to model the problem of finding computationally hard feature models as an optimization problem and we solve it using a novel evolutionary algorithm for optimized feature models (ETHOM). Given a tool and an analysis operation, ETHOM generates input models of a predefined size maximizing aspects such as the execution time or the memory consumption of the tool when performing the operation over the model. This allows users and developers to know the performance of tools in pessimistic cases providing a better idea of their real power and revealing performance bugs. Experiments using ETHOM on a number of analyses and tools have successfully identified models producing much longer executions times and higher memory consumption than those obtained with random models of identical or even larger size.European Commission (FEDER), the Spanish Government and
the Andalusian Government
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