Monitoring of Probabilistic Timed Property Sequence Charts

Zhang, Pengcheng; Li, Wenrui; Wan, Dingsheng; Grunske, Lars

doi:10.1002/spe.1038

Cited by 24 publications

(5 citation statements)

References 51 publications

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“…Wang et al [19] apply a similar statistical RV technique, in conjunction with Monte Carlo simulation, to analog and mixed signal designs. Recent work on the runtime verification of probabilistic properties [11,21] uses acceptance sampling and sequential hypothesis testing to outperform these approaches. In contrast, we perform runtime verification of traditional non-probabilistic properties, but in the presence of sampling.…”

Section: Related Workmentioning

confidence: 99%

Runtime Verification with State Estimation

Stoller

Bartocci

Seyster

et al. 2012

Runtime Verification

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Abstract. We introduce the concept of Runtime Verification with StateEstimation and show how this concept can be applied to estimate the probability that a temporal property is satisfied by a run of a program when monitoring overhead is reduced by sampling. In such situations, there may be gaps in the observed program executions, thus making accurate estimation challenging. To deal with the effects of sampling on runtime verification, we view event sequences as observation sequences of a Hidden Markov Model (HMM), use an HMM model of the monitored program to "fill in" sampling-induced gaps in observation sequences, and extend the classic forward algorithm for HMM state estimation (which determines the probability of a state sequence, given an observation sequence) to compute the probability that the property is satisfied by an execution of the program. To validate our approach, we present a case study based on the mission software for a Mars rover. The results of our case study demonstrate high prediction accuracy for the probabilities computed by our algorithm. They also show that our technique is much more accurate than simply evaluating the temporal property on the given observation sequences, ignoring the gaps.

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Section: Related Workmentioning

confidence: 99%

Runtime Verification with State Estimation

Stoller

Bartocci

Seyster

et al. 2012

Runtime Verification

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“…Monitoring of probabilistic logical properties addresses a different problem, but uses relevant techniques: it relies on the information from the observed data to test whether a probability constraint holds. Approaches like ProMo [12], PTPSC [25], and BaProMon [26] perform online checks of probability constraints via as statistical hypothesis tests. Similarly, statistical approaches can estimate monitor error rates, although at design time: each rate is modeled as a Bernoulli random variable, with its parameter estimated from monitor traces.…”

Section: Detection Monitoring and Errormentioning

confidence: 99%

Compositional Probabilistic Analysis of Temporal Properties Over Stochastic Detectors

Ruchkin

Sokolsky

Weimer

et al. 2020

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Run-time monitoring is a vital part of safety-critical systems. However, early-stage assurance of monitoring quality is currently limited: it relies either on complex models that might be inaccurate in unknown ways, or on data that would only be available once the system has been built. To address this issue, we propose a compositional framework for modeling and analysis of noisy monitoring systems. Our novel 3-value detector model uses probability spaces to represent atomic (non-composite) detectors, and it composes them into a temporal logic-based monitor. The error rates of these monitors are estimated by our analysis engine, which combines symbolic probability algebra, independence inference, and estimation from labeled detection data. Our evaluation on an autonomous underwater vehicle found that our framework produces accurate estimates of error rates while using only detector traces, without any monitor traces. Furthermore, when data is scarce, our approach shows higher accuracy than noncompositional data-driven estimates from monitor traces. Thus, this work enables accurate evaluation of logical monitors in early design stages before deploying them. Disciplines Disciplines Computer Engineering | Computer Sciences Comments Comments This article was presented at the International Conference on Embedded Software 2020; original version appears as part of the ESWEEK-TCAD special issue.

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“…The specification pattern system contains eight generic patterns. PTPSC can represent seven patterns including the Transient State Probability pattern, the Probabilistic Invariance pattern, the Probabilistic Existence pattern, the Probabilistic Precedence pattern, the Probabilistic Response pattern, the Probabilistic Constrained Response pattern, and Steady State Probability [4]. Note that the PSP Steady State Probability requires long running behavioural analysis of the system and cannot be used for monitoring.…”

Section: Formal Operational Semantics Of Psc/tpsc/ptpscmentioning

confidence: 99%

“…Analyzer3 implements a monitor combining TA and SPRT process proposed in [4]. The main logic of Analyzer3 is shown in Fig.…”

Section: Ws‐psc Toolmentioning

confidence: 99%

See 1 more Smart Citation

Web services property sequence chart monitor: a tool chain for monitoring BPEL‐based web service composition with scenario‐based specifications

Zhang

Leung

et al. 2013

IET softw.

Self Cite

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Web service composition is a new paradigm to develop distributed and reactive software-intensive systems. Owing to the autonomous nature of basic services, the validation of composite service must be extended from design-time to run-time. Here, the authors describe a novel tool chain called web services property sequence chart monitor to monitor temporal, timing and probabilistic properties in composite service based on scenario-based property specifications called property sequence chart, timed property sequence chart and probabilistic timed property sequence chart, respectively. The tool chain provides a completely graphical front-end that eliminates the need to deal with any particular textual and logical formalism. Furthermore, the framework and implementation detail of the tool chain are also presented. Finally, the feasibility and usability of the tool have been validated by the case studies and performance measurement.

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Monitoring of Probabilistic Timed Property Sequence Charts

Cited by 24 publications

References 51 publications

Runtime Verification with State Estimation

Runtime Verification with State Estimation

Compositional Probabilistic Analysis of Temporal Properties Over Stochastic Detectors

Web services property sequence chart monitor: a tool chain for monitoring BPEL‐based web service composition with scenario‐based specifications

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