All roads lead to Rome: Commuting strategies for product-line reliability analysis

Castro, Thiago M.; Lanna, André; Alves, Vander; Teixeira, Leopoldo; Apel, Sven; Schobbens, Pierre‐Yves

doi:10.1016/j.scico.2017.10.013

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…Vending Machine In Fig. 8, we depict the FTS modelling the behaviour of a configurable vending machine from Classen (2011), an FTS benchmark which was used in ter Beek et al (2019a) and in many other publications (Classen et al , 2013Devroey et al 2014bDevroey et al , 2016bter Beek et al 2015ater Beek et al , 2015bter Beek et al , 2019bCastro et al 2018;Dimovski 2018Dimovski , 2020Dubslaff 2019). It serves a beverage (soda or tea) either for free or upon payment, in which case a compartment is opened for the customer to take the beverage after which it closes again.…”

Section: Methodsmentioning

confidence: 99%

Efficient static analysis and verification of featured transition systems

et al. 2021

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A Featured Transition System (FTS) models the behaviour of all products of a Software Product Line (SPL) in a single compact structure, by associating action-labelled transitions with features that condition their presence in product behaviour. It may however be the case that the resulting featured transitions of an FTS cannot be executed in any product (so called dead transitions) or, on the contrary, can be executed in all products (so called false optional transitions). Moreover, an FTS may contain states from which a transition can be executed only in some products (so called hidden deadlock states). It is useful to detect such ambiguities and signal them to the modeller, because dead transitions indicate an anomaly in the FTS that must be corrected, false optional transitions indicate a redundancy that may be removed, and hidden deadlocks should be made explicit in the FTS to improve the understanding of the model and to enable efficient verification—if the deadlocks in the products should not be remedied in the first place. We provide an algorithm to analyse an FTS for ambiguities and a means to transform an ambiguous FTS into an unambiguous one. The scope is twofold: an ambiguous model is typically undesired as it gives an unclear idea of the SPL and, moreover, an unambiguous FTS can efficiently be model checked. We empirically show the suitability of the algorithm by applying it to a number of benchmark SPL examples from the literature, and we show how this facilitates a kind of family-based model checking of a wide range of properties on FTSs.

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Section: Methodsmentioning

confidence: 99%

Efficient static analysis and verification of featured transition systems

et al. 2021

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“…However, none of the tools described are equipped for exploring collections of system variants. In contrast, product line reliability analysis approaches [16,17,26,41] can analyze collections of system designs encoded in feature models individually or collectively. A recent approach to continuous-time probabilistic design synthesis [12] uses a template-based solution to analyze alternative designs, but assumes an existing encoding of design options in a set of discrete variables and does not systematically enforce any structural constraints in the designs.…”

Section: Relational Modeling and Structural Verication (Rmsv)mentioning

confidence: 99%

“…On the other hand, quantitative verication or probabilistic model checking [13,23,38] can analyze quantitative guarantees of systems (e.g., on performance, reliability) under uncertainty, but use notations (e.g., PRISM [39], PEPA [27], cpGCL [34]) that do not retain the exibility in describing structures of relational methods. Recent product line reliability analysis approaches [16,17,26,41] improve on exibility by introducing systematic treatment of variability, but are not equipped to synthesize or explore complex structures, and lack languages tailored to check sophisticated properties across design spaces (i.e., temporal logics employed like PCTL [38] can capture properties only about a single model, not collections of individual variants). Other works in quantitative optimization of architectures [3,6,8,10,11,21,22,29,44,45] can in some cases synthesize complex structures [6,21], but are not compatible with formal verication and can only provide estimates of probabilistic properties that could dier from actual guarantees (e.g., worst case) available only via exhaustive state-space exploration.…”

Section: Introductionmentioning

confidence: 99%

HaiQ

Cámara

2020

Proceedings of the 8th International Conference on Formal Methods in Software Engineering

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Formal methods used to validate software designs, like Alloy, OCL, and B, are powerful tools to analyze complex structures (e.g., architectures, object-relational mappings) captured as sets of relational constraints. However, their applicability is limited when software is subject to uncertainty (derived, e.g., from lack of control over third-party components, interaction with physical elements). In contrast, quantitative verication has emerged as a powerful way of providing quantitative guarantees about the performance, cost, and reliability of systems operating under uncertainty. However, quantitative verication methods do not retain the exibility of relational modeling in describing structures, forcing engineers to trade structural exploration for analytic capabilities that concern probabilistic and other quantitative guarantees. This paper contributes a method (HaiQ) that enhances structural modeling/synthesis with quantitative guarantees in the style provided by quantitative verication. It includes a language for describing structure and (stochastic) behavior of systems, and a temporal logic that allows checking probability and reward-based properties over sets of feasible design alternatives implicitly described by the relational constraints in a HaiQ model. We report the results of applying a prototype tool in two domains, on which we show the feasibility of synthesizing structural designs that optimize probabilistic and other quantitative guarantees. CCS CONCEPTS • Software and its engineering → Formal software verication; Software design tradeos; • Theory of computation → Verication by model checking.

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