Automated Rare Event Simulation for Fault Tree Analysis via Minimal Cut Sets

Budde, Carlos E.; Stoelinga, Mariëlle

doi:10.1007/978-3-030-43024-5_16

Cited by 4 publications

(5 citation statements)

References 23 publications

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“…For instance, the independent nature of each simulation in MCTS means that the algorithm is a good target for parallelization (Steinmetz and Gini, 2020;Chaslot et al, 2008b), so that we can improve its performance. Also, other techniques and extensions of Monte Carlo methods, such as the use of minimal cut sets (Budde and Stoelinga, 2020), rare event simulations (Rubino and Tuffin, 2009), or importance splitting (Jégourel et al, 2013) can be applied to specific problems (e.g., the finding defective configuration problem) to guide the search to effectively handle rare properties and improve the results.…”

Section: Discussionmentioning

confidence: 99%

“…The framework is open for extension, and thus, further variants of Monte Carlo and MCTS methods can be added to the framework such as parallel versions of both methods (Steinmetz and Gini, 2020;Chaslot et al, 2008b) to improve the efficiency, Heuristic MCTS (Gelly and Silver, 2011), MC-RAVE (Gelly and Silver, 2011), and further specialization that may include the use of minimal cut sets (Budde and Stoelinga, 2020), rare event simulations (Rubino and Tuffin, 2009), or importance splitting (Jégourel et al, 2013), among others.…”

Section: • Greedy Mctsmentioning

confidence: 99%

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A Monte Carlo tree search conceptual framework for feature model analyses

Horcas

Galindo

Heradio

et al. 2023

Journal of Systems and Software

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Section: Discussionmentioning

confidence: 99%

Section: • Greedy Mctsmentioning

confidence: 99%

A Monte Carlo tree search conceptual framework for feature model analyses

Horcas

Galindo

Heradio

et al. 2023

Journal of Systems and Software

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“…6 we experimented with exponential, Erlang, uniform, Rayleigh, Weibull, normal, and log-normal PDFs. Furthermore and for the first time (with the exclusion of [12,19] on which this work stands), we consider the application of [20,45] to study rare events.…”

Section: Related Workmentioning

confidence: 99%

“…In [19] it was proposed to alleviate that issue via cut set pruning and importance normalisation, but the former strategy proved inefficient in the general case. Thus here we chose to experiment with the original and normalised variants of this function, i.e.…”

Section: Related Workmentioning

confidence: 99%

Analysis of non-Markovian repairable fault trees through rare event simulation

Budde

D’Argenio

Monti

et al. 2022

Int J Softw Tools Technol Transfer

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Dynamic fault trees (DFTs) are widely adopted in industry to assess the dependability of safety-critical equipment. Since many systems are too large to be studied numerically, DFTs dependability is often analysed using Monte Carlo simulation. A bottleneck here is that many simulation samples are required in the case of rare events, e.g. in highly reliable systems where components seldom fail. Rare event simulation (RES) provides techniques to reduce the number of samples in the case of rare events. In this article, we present a RES technique based on importance splitting to study failures in highly reliable DFTs, more precisely, on a variant of repairable fault trees (RFT). Whereas RES usually requires meta-information from an expert, our method is fully automatic. For this, we propose two different methods to derive the so-called importance function. On the one hand, we propose to cleverly exploit the RFT structure to compositionally construct such function. On the other hand, we explore different importance functions derived in different ways from the minimal cut sets of the tree, i.e., the minimal units that determine its failure. We handle RFTs with Markovian and non-Markovian failure and repair distributions—for which no numerical methods exist—and implement the techniques on a toolchain that includes the RES engine FIG, for which we also present improvements. We finally show the efficiency of our approach in several case studies.

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“…If the coarse-grained analysis reveals that no variant can guarantee to satisfy an intended property in all their executions, it is interesting for engineers to identify the variant that minimizes the probability of violating this property. Such use cases typically occur, e.g., in security systems where there is a trade-off to find between fixing vulnerabilities and the cost of implementing these fixes [WKCK15], or in cyber-physical systems deployed in an uncontrollable environment [BS20]. While such systems cannot guarantee to meet their intended requirements perfectly, it is desirable to minimize their probability of failures.…”

Section: Introductionmentioning

confidence: 99%

Statistical model checking for variability-intensive systems: applications to bug detection and minimization

et al. 2021

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We propose a new Statistical Model Checking (SMC) method to identify bugs in variability-intensive systems (VIS). The state-space of such systems is exponential in the number of variants, which makes the verification problem harder than for classical systems. To reduce verification time, we propose to combine SMC with featured transition systems (FTS)—a model that represents jointly the state spaces of all variants. Our new methods allow the sampling of executions from one or more (potentially all) variants. We investigate their utility in two complementary use cases. The first case considers the problem of finding all variants that violate a given property expressed in Linear-Time Logic (LTL) within a given simulation budget. To achieve this, we perform random walks in the featured transition system seeking accepting lassos. We show that our method allows us to find bugs much faster (up to 16 times according to our experiments) than exhaustive methods. As any simulation-based approach, however, the risk of Type-1 error exists. We provide a lower bound and an upper bound for the number of simulations to perform to achieve the desired level of confidence. Our empirical study involving 59 properties over three case studies reveals that our method manages to discover all variants violating 41 of the properties. This indicates that SMC can act as a coarse-grained analysis method to quickly identify the set of buggy variants. The second case complements the first one. In case the coarse-grained analysis reveals that no variant can guarantee to satisfy an intended property in all their executions, one should identify the variant that minimizes the probability of violating this property. Thus, we propose a fine-grained SMC method that quickly identifies promising variants and accurately estimates their violation probability. We evaluate different selection strategies and reveal that a genetic algorithm combined with elitist selection yields the best results.

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Automated Rare Event Simulation for Fault Tree Analysis via Minimal Cut Sets

Cited by 4 publications

References 23 publications

A Monte Carlo tree search conceptual framework for feature model analyses

A Monte Carlo tree search conceptual framework for feature model analyses

Analysis of non-Markovian repairable fault trees through rare event simulation

Statistical model checking for variability-intensive systems: applications to bug detection and minimization

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