Automated Performance and Dependability Evaluation Using Model Checking
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ACM SIGPLAN Notices
Model checking meets performance evaluation
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CSRL model checking with closed-form bounding distributions
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TACAS'07 Proceedings of the 13th international conference on Tools and algorithms for the construction and analysis of systems
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Theoretical Computer Science
SFM'07 Proceedings of the 7th international conference on Formal methods for performance evaluation
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The ins and outs of the probabilistic model checker MRMC
Performance Evaluation
Synthesis for PCTL in parametric Markov decision processes
NFM'11 Proceedings of the Third international conference on NASA Formal methods
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Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools
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EPEW'05/WS-FM'05 Proceedings of the 2005 international conference on European Performance Engineering, and Web Services and Formal Methods, international conference on Formal Techniques for Computer Systems and Business Processes
Verification of linear duration properties over continuous-time markov chains
Proceedings of the 15th ACM international conference on Hybrid Systems: Computation and Control
Expressive statistical model checking of genetic networks with delayed stochastic dynamics
CMSB'12 Proceedings of the 10th international conference on Computational Methods in Systems Biology
Expressing and computing passage time measures of GSPN models with HASL
PETRI NETS'13 Proceedings of the 34th international conference on Application and Theory of Petri Nets and Concurrency
Verification of linear duration properties over continuous-time markov chains
ACM Transactions on Computational Logic (TOCL)
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Model checking has been introduced as an automated technique to verify whether functional properties, expressed in a formal logic like computational tree logic (CTL), do holdin a formally-specified system.In recent years, we have extended CTL such that it allows for the specification of properties over finite-state continuous-time Markov chains (CTMCs). Computational techniques for model checking have been developed and successfully applied in the dependability context. Further work in this area has recently led to the continuous stochastic reward logic (CSRL), a logic to specify measures over CTMCs extended with a reward structure (so-called Markov reward models). Well-known performability measures, most notably also Meyer's performability distribution, can be easily defined with CSRL. However, using CSRL it is possible to specify performability measures that have not yet been addressed in the literature, hence, for which no computational procedures have been developed yet.In this paper we present a number of computational procedures to perform model checking of CSRL over finite Markov reward models, thereby stressing their computationalcomplexity (time and space) and applicability from a practical point of view (accuracy, stability). A case study in the area of ad hoc mobile computing under power constraints shows the merits of CSRL and the new computational procedures.