Modeling and Verification for Probabilistic Properties in Software Product Lines

Rodrigues, Genaína Nunes; Alves, Vander; Nunes, Vinicius; Lanna, André; Cordy, Maxime; Schobbens, Pierre‐Yves; Sharifloo, Amir Molzam; Legay, Axel

doi:10.1109/hase.2015.34

Cited by 34 publications

(45 citation statements)

References 27 publications

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“…This requires to extend the FTS formalism to specify quantities [31]. Another interesting information to specify in FTS is probabilities, in order to perform statistical model-checking activities [33]. Such extended FTS formalism is also of interest for testing [19,20].…”

Section: Resultsmentioning

confidence: 99%

“…We have then proposed extensions of FTS that allow us to reason on more quantitative aspect of systems. This includes real time to specify timing constraints in a timed automaton fashion [15], and probabilities that allows us to make quantitative hypotheses via a combination of FTS and Markov chains [33]. Behavioural relations such as simulation were also extended to FTS.…”

Section: Summary Of Our Resultsmentioning

confidence: 99%

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Section: Resultsmentioning

confidence: 99%

Section: Summary Of Our Resultsmentioning

confidence: 99%

On Featured Transition Systems

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et al. 2017

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“…However, performing such analysis at runtime would be too slow and would not scale properly -as observed in [40] and also witnessed by the experiments further presented in Section 4. While verification speed can be mitigated by the fact that probability formulas can be computed off-line, scalability of the verification process itself remains an issue.…”

Section: Parametric Formula Compositionmentioning

confidence: 99%

GODA: A goal-oriented requirements engineering framework for runtime dependability analysis

Mendonça

Rodrigues

Ali

et al. 2016

Information and Software Technology

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Context: Many modern software systems must deal with changes and uncertainty. Traditional dependability requirements engineering is not equipped for this since it assumes that the context in which a system operates be stable and deterministic, which often leads to failures and recurrent corrective maintenance. The Contextual Goal Model (CGM), a requirements model that proposes the idea of context-dependent goal fulfillment, mitigates the problem by relating alternative strategies for achieving goals to the space of context changes. Additionally, the Runtime Goal Model (RGM) adds behavioral constraints to the fulfillment of goals that may be checked against system execution traces.Objective: This paper proposes GODA (Goal-Oriented Dependability Analysis) and its supporting framework as concrete means for reasoning about the dependability requirements of systems that operate in dynamic contexts.Method: GODA blends the power of CGM, RGM and probabilistic model checking to provide a formal requirements specification and verification solution. At design time, it can help with design and implementation decisions; at runtime it helps the system self-adapt by analyzing the different alternatives and selecting the one with the highest probability for the system to be dependable. GODA is integrated into TAO4ME, a state-of-the-art tool for goal modeling and analysis.Results: GODA has been evaluated against feasibility and scalability on Mobee: a real-life software system that allows people to share live and updated information about public transportation via mobile devices, and on larger goal models. GODA can verify, at runtime, up to two thousand leaf-tasks in less than 35ms, and requires less than 240 kbytes of memory.Conclusion: Presented results show GODA's design-time and runtime verification capabilities, even under limited computational resources, and the scalability of the proposed solution.

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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: 98%

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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Modeling and Verification for Probabilistic Properties in Software Product Lines

Cited by 34 publications

References 27 publications

On Featured Transition Systems

On Featured Transition Systems

GODA: A goal-oriented requirements engineering framework for runtime dependability analysis

Statistical Model Checking for Product Lines

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