PROPhESY: A PRObabilistic ParamEter SYnthesis Tool

Dehnert, Christian; Junges, Sebastian; Jansen, Nils; Corzilius, Florian; Volk, Matthias; Bruintjes, Harold; Katoen, Joost-Pieter; Ábrahám, Erika

doi:10.1007/978-3-319-21690-4_13

Cited by 104 publications

(161 citation statements)

References 31 publications

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“…When the range of uncertainty is not known, one can opt parametric-Markov models, that construct a symbolic model that can later be evaluated with specific probabilities. Recent work includes methods from parametric model checking [Hahn et al 2010;Filieri et al 2011a;Dehnert et al 2015].…”

Section: Dealing With Uncertainty In Control Strategies For Adaptationmentioning

confidence: 99%

Control Strategies for Self-Adaptive Software Systems

Filieri

Maggio

Angelopoulos³

et al. 2017

ACM Trans. Auton. Adapt. Syst.

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The pervasiveness and growing complexity of software systems is challenging software engineering to design systems that can adapt their behavior to withstand unpredictable, uncertain, and continuously changing execution environments. Control theoretical adaptation mechanisms received a growing interest from the software engineering community in the last years for their mathematical grounding allowing formal guarantees on the behavior of the controlled systems. However, most of these mechanisms are tailored to specific applications and can hardly be generalized into broadly applicable software design and development processes.This paper discusses a reference control design process, from goal identification to the verification and validation of the controlled system. A taxonomy of the main control strategies is introduced, analyzing their applicability to software adaptation for both functional and non-functional goals. A brief extract on how to deal with uncertainty complements the discussion. Finally, the paper highlights a set of open challenges, both for the software engineering and the control theory research communities.

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Section: Dealing With Uncertainty In Control Strategies For Adaptationmentioning

confidence: 99%

Control Strategies for Self-Adaptive Software Systems

Filieri

Maggio

Angelopoulos³

et al. 2017

ACM Trans. Auton. Adapt. Syst.

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“…The once-and-for-all IMC semantics ( [7,19,16]) is alike to the semantics for pMC, as introduced above. The associated satisfaction relation |= o I is defined as follows:…”

Section: Existing MC Abstraction Modelsmentioning

confidence: 99%

“…The original MC nand model has already been extended as a pMC in [7], where the authors consider a single parameter p for the probability that each of the N nand gates fails during the multiplexing. We extend this model to pIMC by considering intervals for the probability that the initial inputs are stimulated and we have one parameter per nand gate to represent the probability that it fails.…”

Section: Prototype Implementationmentioning

confidence: 99%

“…Model-checking algorithms and tools have been developed in the context of pMCs [7,11,14] and IMCs with the once-and-for-all semantics [10,4]. State of the art tools [7] for pMC verification compute a rational function on the parameters that characterizes the probability of satisfying a given property, and then use external tools such as SMT solving [7] for computing the satisfying parameter values.…”

Section: Introductionmentioning

confidence: 99%

“…State of the art tools [7] for pMC verification compute a rational function on the parameters that characterizes the probability of satisfying a given property, and then use external tools such as SMT solving [7] for computing the satisfying parameter values. For these methods to be viable in practice, the number of parameters used is quite limited.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reachability in Parametric Interval Markov Chains Using Constraints

Bart

Delahaye

Lime

et al. 2017

Quantitative Evaluation of Systems

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Abstract. Parametric Interval Markov Chains (pIMCs) are a specification formalism that extend Markov Chains (MCs) and Interval Markov Chains (IMCs) by taking into account imprecision in the transition probability values: transitions in pIMCs are labeled with parametric intervals of probabilities. In this work, we study the difference between pIMCs and other Markov Chain abstractions models and investigate the two usual semantics for IMCs: once-and-for-all and at-every-step. In particular, we prove that both semantics agree on the maximal/minimal reachability probabilities of a given IMC. We then investigate solutions to several parameter synthesis problems in the context of pIMCs -consistency, qualitative reachability and quantitative reachability -that rely on constraint encodings. Finally, we propose a prototype implementation of our constraint encodings with promising results.

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Synthesis in pMDPs: A Tale of 1001 Parameters

Cubuktepe

Jansen²,

Junges

et al. 2018

Automated Technology for Verification and Analysis

Self Cite

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This paper considers parametric Markov decision processes (pMDPs) whose transitions are equipped with affine functions over a finite set of parameters. The synthesis problem is to find a parameter valuation such that the instantiated pMDP satisfies a (temporal logic) specification under all strategies. We show that this problem can be formulated as a quadratically-constrained quadratic program (QCQP) and is non-convex in general. To deal with the NP-hardness of such problems, we exploit a convex-concave procedure (CCP) to iteratively obtain local optima. An appropriate interplay between CCP solvers and probabilistic model checkers creates a procedure -realized in the tool PROPheSYthat solves the synthesis problem for models with thousands of parameters.

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PROPhESY: A PRObabilistic ParamEter SYnthesis Tool

Cited by 104 publications

References 31 publications

Control Strategies for Self-Adaptive Software Systems

Control Strategies for Self-Adaptive Software Systems

Reachability in Parametric Interval Markov Chains Using Constraints

Synthesis in pMDPs: A Tale of 1001 Parameters

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