Home > Runtime Verification > Runtime Verification With Particle Filtering# Runtime Verification With Particle Filtering

## Get Access Abstract We introduce Runtime Verification with Particle Filtering (RVPF), a powerful and versatile method for controlling the tradeoff between uncertainty and overhead in runtime verification.

We had posited that two activity **patterns indicated two** separable sets of users, each of which might benefit from a differently tailored app version, but our subsequent analysis detailed users' interleaving The meaning of B is a joint distribution over its variables. The 28 revised full papers presented together with 2 tool papers, and 8short papers were carefully reviewed and selected from 70 submissions. Our approach is based on extending the classic forwardalgorithm for HMM state estimation to take into account the paired execution of an HMM system model and monitor automaton for the temporal have a peek here

When His in state siand Mis in state m,pi(m, n) is theprobability that the next observation (i.e., the observation in state sj) causesMto transition to state n. Diehl, S.: Software Visualization: Visualizing the Structure, Behavior, and Evolu-tion of Software. These algorithms allow (but do not require) the user to provideinformation about the structure of the HMM; speciﬁcally, that certain entriesin the transition probability matrix and the observation probability matrix arezero. Full-text · Dec 2016 · Procedia EngineeringRead now My AccountSearchMapsYouTubePlayNewsGmailDriveCalendarGoogle+TranslatePhotosMoreShoppingWalletFinanceDocsBooksBloggerContactsHangoutsEven more from GoogleSign inHidden fieldsBooksbooks.google.com - This book constitutes the refereed proceedings of the 5th International Conference on Runtime Verification, RV

Let Pr(c1|c2)denote the probability that c1holds, given that c2holds. Supposenow that τis an incomplete trace of an execution with implicit gaps due tosampling. If the system is deter-ministic, ˇOcan be produced by re-running the system on the same input asfor Owhile using sampling. Springer (2005) CitationsCitations31ReferencesReferences26Feedback Control for Statistical Model Checking of Cyber-Physical Systems"Two sequential Monte-Carlo techniques, importance sampling (ISam) [7] and importance splitting (ISpl) [9], originally developed for statistical physics, promise to overcome

The next **subsectionpresents our algorithm for RVSE. **The system returned: (22) Invalid argument The remote host or network may be down. Commands are issued from groundto the rover and are characterized by three parameters: instrument id (AorB), command name, and a time stamp indicating at what time the event occurs.The other three The model is written in the Scala programming language,4allowingfor fast prototyping.

Section 2 provides background. We use a special symbolto mark gaps, i.e., points in the observation sequence where unobserved events might have occurred. Our results confim RVPF’s versatility, showing how it can be used to control the tradeoff between execution time and memory usage while, at the same time, being the most accurate of Our algorithm assumes that the temporalproperty φto be monitored is expressed as a parametrized deterministic ﬁnitestate machine (DFSM).

Stoller1, Ezio Bartocci2, Justin Seyster1, Radu Grosu1,Klaus Havelund3, Scott A. Produce another set TEof traces by monitoring the system without sampling,and use them for evaluation as follows.3. Note that gap(L0) represents a deﬁnite gap of length 1. Upon receipt of a command, therover reports this event to the logger (by sending the command to the logger),and then sends the command to the relevant instrument.

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- The main changesare introduction of a conditional expression in equation (6), reﬂecting that theinitial state of Mis always minit, and introduction of a sum over predecessorsmof nwith respect to Ot+1 in
- In the other half of the traces, the requirementis violated by approximately 30% of the commands; among those commands,approximately half have an explicit Fail event, and the other half do not
- SmolkaNo preview available - 2014Common terms and phrasesabstraction affine functions algorithm application approach asynchronous automatic Bayesian networks behavior benchmark Bonakdarpour Boolean bound BSRV Btrfs bugs choreography component Computer constraints data structures
- PF can be used to estimate the state probability distribution P ( X t ), given an observation sequence o 1: t .
- Since the length of a gap (i.e., the number of unobservedevents) might be unknown, we allow the gap length to be characterized bya probability distribution.–We evaluate our RVSE methodology using a

Speciﬁed events trig-ger creation of a new instance of the parameterized property, and parameters of the trigger event are used as parameters of the property. http://what-when-how.com/Tutorial/topic-4138ap/Runtime-Verification-172.html In: Proc. 2006 ACM SIGPLAN Conference on ProgrammingLanguage Design and Implementation (PLDI 2006). ISpl and ISam have individually demonstrated their utility on a number of models. "[Show abstract] [Hide abstract] ABSTRACT: We introduce feedback-control statistical system checking (FC-SSC), a new approach to statistical model The probability of reaching sjis calculated by summing overthe immediate predecessors siof sj; the summand αt(i)Ai,j is the joint prob-ability of reaching siwhile observing O1through OT−1and then transitioningfrom sito sj.

ACM(2011)16. navigate here Left: an example of an HMM. Our results confirm RVPF's versatility. In: Proc. 17th International Symposium on Formal Methods (FM 2011).Springer (Jun 2011)6.

Clearly τsatisﬁes φ. Page %P Close Plain text Look **Inside Chapter Metrics Provided** by Bookmetrix Reference tools Export citation EndNote (.ENW) JabRef (.BIB) Mendeley (.BIB) Papers (.RIS) Zotero (.RIS) BibTeX (.BIB) Add to Papers Adjusting the number of particles used by RVPF provides a versatile way to tune the memory requirements, runtime overhead, and prediction accuracy. Check This Out To validate our approach, we present a case study based on the mission software for a Mars rover.

Its architecture, depicted in Figure 1, is representative,in general terms, of actual rover missions, such as the current Mars ScienceLaboratory5(MSL) mission. Springer, Heidelberg (2011)CrossRef10.Stoller, S., Bartocci, E., Seyster, J., Grosu, R., Havelund, K., Smolka, S., Zadok, E.: Runtime verification with state estimation. With this approach,the property is checked with 100% conﬁdence for the selected objects, but it isnot checked at all for other objects.

They also show that our technique is much more accuratethan simply evaluating the temporal property on the given observationsequences, ignoring the gaps.1 IntroductionRuntime veriﬁcation (RV) is the problem of, given a Smolka (19) Scott D. Application to Radiation Signals. The result appears in Figure 4.An auxiliary function piis used to calculate the probability of transitions ofMduring gaps.

An example log collected duringthe execution of this system could be: Command(A, START, 1008),Command(B,RESET, 2303),Success(A, START, 4300),Success(B, RESET, 5430).One aspect of the desired behavior of the rover system is expressed by A Hidden Markov Model (HMM) is a special kind of DBN; specifically, an HMM is a DBN with a single state variable and a single observable variable. This information can help the learning algorithm converge more quicklyand ﬁnd globally (instead of locally) optimal solutions. this contact form pp. 1–6.

In: Khurshid, S., Sen, K. (eds.) RV 2011. If the number of generated particles exceeds N p , the number is reduced in Lines 6-9 by removing individual particles from the richest states. Note that δis a total function. Sampling means that some events are not pro-cessed at all, or are processed in a limited (and thus less expensive) manner thanother events.

Full-text · Conference Paper · Oct 2016 K. The probability distributions of our learned HMMs often have large transition prob- abilities of x t− 1 associated with small observation probabilities of x t , and small tran- sition probabilities With the target overhead that we speciﬁed, theSMCO simulator replaced 47% of the events with gaps.Based on the parameters of the property φcs, each sampled trace was decom-posed into a separate In: Proc.

For example, if the system useslightweight instrumentation to count events during gaps, then the position andlength of all gaps are known. The transition and observation models are represented as a Bayesian network. Weexpect the inaccuracy to approach 0 as the fraction of events that are observedapproaches 1. This is a diﬀerent statistic than the conditionalprobabilities we compute.LarvaStat [7] is another system for collecting statistical information aboutruntime executions.

An alternative approach, applicable regardlessof whether the system is deterministic, is to write a program that reads atrace, simulates the eﬀect of sampling, and outputs a sampled version of thetrace.4. States with a double border areaccepting states.In the base case, α1(j) is the joint probability of starting in state sjand emittingO1. We succinctly represent the program model, the program monitor, their interaction, and their observations as a dynamic Bayesian network (DBN). We illustrate our work via a case study of a mobile app presenting analytic findings and discussing how they are feeding into redesign.

When learning the HMM, we abstract from the speciﬁc values ofthe parameters in each subtrace, because the values are, of course, diﬀerent ineach subtrace, and we are not attempting to learn They also show that our technique is much more accurate than simply evaluating the temporal property on the given observation sequences, ignoring the gaps.Discover the world's research11+ million members100+ million publications100k+

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