Model-based verification of quantitative non-functional properties for software product lines

Ghezzi, Carlo; Sharifloo, Amir Molzam

doi:10.1016/j.infsof.2012.07.017

Cited by 62 publications

(65 citation statements)

References 26 publications

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“…PMC modelling and analysis, like that described above, is conventionally used for offline analysis of system properties [15], [16]. We instead apply these techniques at runtime updating our PMC parameters in light of observed interactions.…”

Section: Online Learning Of Sharing Behaviourmentioning

confidence: 99%

Learning to share: Engineering adaptive decision-support for online social networks

Rafiq

Dickens

Russo

et al. 2017

2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)

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Abstract-Some online social networks (OSNs) allow users to define friendship-groups as reusable shortcuts for sharing information with multiple contacts. Posting exclusively to a friendship-group gives some privacy control, while supporting communication with (and within) this group. However, recipients of such posts may want to reuse content for their own social advantage, and can bypass existing controls by copy-pasting into a new post; this cross-posting poses privacy risks.This paper presents a learning to share approach that enables the incorporation of more nuanced privacy controls into OSNs. Specifically, we propose a reusable, adaptive software architecture that uses rigorous runtime analysis to help OSN users to make informed decisions about suitable audiences for their posts. This is achieved by supporting dynamic formation of recipient-groups that benefit social interactions while reducing privacy risks. We exemplify the use of our approach in the context of Facebook.

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Section: Online Learning Of Sharing Behaviourmentioning

confidence: 99%

Learning to share: Engineering adaptive decision-support for online social networks

Rafiq

Dickens

Russo

et al. 2017

2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)

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“…5(b)). The method relies on analyzing the derived product architectural models instead of relying on a set of predicted values, based on a previous measurement process of a sample of products as in [65], [37], [70]. Those approaches could have scalability concerns, due to the exponential growth of the number of configurations as a function of the number of features and they also fail on managing those products that refine or extend the NFRs of the product line with delta requirements specific for the product under development.…”

Section: Fig 2 Excerpt Of a Product Line Architecturementioning

confidence: 99%

“…Various studies concerned with the derivation (e.g., [14], [61]) and/or evaluation of software architectures from several points of view (e.g., [65], [37], [70], [66]) have been proposed in literature. After reviewing these studies, we have observed that: (a) There is a lack of systematic methods that model the impact between architectural design decisions and quality attributes to support the integrated derivation, evaluation and quality enhancement of software architectures.…”

Section: Introductionmentioning

confidence: 99%

Validating a model-driven software architecture evaluation and improvement method: A family of experiments

González-Huerta

Insfrán

Abrahão

et al. 2015

Information and Software Technology

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ElsevierGonzález Huerta, J.; Insfrán Pelozo, CE.; Abrahao Gonzales, SM.; Scanniello, G. (2015 Context: Software architectures should be evaluated during the early stages of software development in order to verify whether the Non-Functional Requirements (NFRs) of the product can be fulfilled. This activity is even more crucial in Software Product Line (SPL) development, since it is also necessary to identify whether the NFRs of a particular product can be achieved by exercising the variation mechanisms provided by the product line architecture or whether additional transformations are required. These issues have motivated us to propose QuaDAI, a method for the derivation, evaluation and improvement of software architectures in model-driven SPL development.Objective: We present in this paper the results of a family of four experiments carried out to empirically validate the evaluation and improvement strategy of QuaDAI. Method:The family of experiments was carried out by 92 participants: Computer Science Master's and undergraduate students from Spain and Italy. The goal was to compare the effectiveness, efficiency, perceived ease of use, perceived usefulness and intention to use with regard to participants using the evaluation and improvement strategy of QuaDAI as opposed to the Architecture Tradeoff Analysis Method (ATAM). Results:The main result was that the participants produced their best results when applying QuaDAI, signifying that the participants obtained architectures with better values for the NFRs faster, and that they found the method easier to use, more useful and more likely to be used. The results of the meta-analysis carried out to aggregate the results obtained in the individual experiments also confirmed these results. Conclusions:The results support the hypothesis that QuaDAI would achieve better results than ATAM in the experiments and that QuaDAI can be considered as a promising approach with which to perform architectural evaluations that occur after the product architecture derivation in model-driven SPL development processes when carried out by novice software evaluators.

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“…While we know of several, quite different, approaches that apply probabilistic model checking to product lines [16,21,22,24,29,38], to the best of our knowledge, we were the first to apply SMC to product lines in [8,9]. In this paper, however, we give more details of QFLan and of the case study and report more analyses.…”

Section: Introductionmentioning

confidence: 99%

Statistical Model Checking for Product Lines

Beek

Legay

Lafuente³

et al. 2016

Leveraging Applications of Formal Methods, Verification and Validation: Foundational Techniques

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Abstract. We report on the suitability of statistical model checking for the analysis of quantitative properties of product line models by an extended treatment of earlier work by the authors. The type of analysis that can be performed includes the likelihood of specific product behaviour, the expected average cost of products (in terms of the attributes of the products' features) and the probability of features to be (un)installed at runtime. The product lines must be modelled in QFLan, which extends the probabilistic feature-oriented language PFLan with novel quantitative constraints among features and on behaviour and with advanced feature installation options. QFLan is a rich process-algebraic specification language whose operational behaviour interacts with a store of constraints, neatly separating product configuration from product behaviour. The resulting probabilistic configurations and probabilistic behaviour converge in a discrete-time Markov chain semantics, enabling the analysis of quantitative properties. Technically, a Maude implementation of QFLan, integrated with Microsoft's SMT constraint solver Z3, is combined with the distributed statistical model checker MultiVeStA, developed by one of the authors. We illustrate the feasibility of our framework by applying it to a case study of a product line of bikes.

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Model-based verification of quantitative non-functional properties for software product lines

Cited by 62 publications

References 26 publications

Learning to share: Engineering adaptive decision-support for online social networks

Learning to share: Engineering adaptive decision-support for online social networks

Validating a model-driven software architecture evaluation and improvement method: A family of experiments

Statistical Model Checking for Product Lines

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