Approximate Model Checking of Real-Time Systems for Linear Duration Invariants

Choe, Changil; O., Hyong-Chol; Han, Song

Serdica Journal of Computing (2013)

  • Volume: 7, Issue: 1, page 1-12
  • ISSN: 1312-6555

Abstract

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Real-time systems are usually modelled with timed automata and real-time requirements relating to the state durations of the system are often specifiable using Linear Duration Invariants, which is a decidable subclass of Duration Calculus formulas. Various algorithms have been developed to check timed automata or real-time automata for linear duration invariants, but each needs complicated preprocessing and exponential calculation. To the best of our knowledge, these algorithms have not been implemented. In this paper, we present an approximate model checking technique based on a genetic algorithm to check real-time automata for linear durration invariants in reasonable times. Genetic algorithm is a good optimization method when a problem needs massive computation and it works particularly well in our case because the fitness function which is derived from the linear duration invariant is linear. ACM Computing Classification System (1998): D.2.4, C.3.

How to cite

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Choe, Changil, O., Hyong-Chol, and Han, Song. "Approximate Model Checking of Real-Time Systems for Linear Duration Invariants." Serdica Journal of Computing 7.1 (2013): 1-12. <http://eudml.org/doc/252267>.

@article{Choe2013,
abstract = {Real-time systems are usually modelled with timed automata and real-time requirements relating to the state durations of the system are often specifiable using Linear Duration Invariants, which is a decidable subclass of Duration Calculus formulas. Various algorithms have been developed to check timed automata or real-time automata for linear duration invariants, but each needs complicated preprocessing and exponential calculation. To the best of our knowledge, these algorithms have not been implemented. In this paper, we present an approximate model checking technique based on a genetic algorithm to check real-time automata for linear durration invariants in reasonable times. Genetic algorithm is a good optimization method when a problem needs massive computation and it works particularly well in our case because the fitness function which is derived from the linear duration invariant is linear. ACM Computing Classification System (1998): D.2.4, C.3.},
author = {Choe, Changil, O., Hyong-Chol, Han, Song},
journal = {Serdica Journal of Computing},
keywords = {Approximate Model Checking; Verification; Real-Time System; Linear Duration Invariant; Genetic Algorithm; approximate model checking; verification; real-time system; linear duration invariant; genetic algorithm},
language = {eng},
number = {1},
pages = {1-12},
publisher = {Institute of Mathematics and Informatics Bulgarian Academy of Sciences},
title = {Approximate Model Checking of Real-Time Systems for Linear Duration Invariants},
url = {http://eudml.org/doc/252267},
volume = {7},
year = {2013},
}

TY - JOUR
AU - Choe, Changil
AU - O., Hyong-Chol
AU - Han, Song
TI - Approximate Model Checking of Real-Time Systems for Linear Duration Invariants
JO - Serdica Journal of Computing
PY - 2013
PB - Institute of Mathematics and Informatics Bulgarian Academy of Sciences
VL - 7
IS - 1
SP - 1
EP - 12
AB - Real-time systems are usually modelled with timed automata and real-time requirements relating to the state durations of the system are often specifiable using Linear Duration Invariants, which is a decidable subclass of Duration Calculus formulas. Various algorithms have been developed to check timed automata or real-time automata for linear duration invariants, but each needs complicated preprocessing and exponential calculation. To the best of our knowledge, these algorithms have not been implemented. In this paper, we present an approximate model checking technique based on a genetic algorithm to check real-time automata for linear durration invariants in reasonable times. Genetic algorithm is a good optimization method when a problem needs massive computation and it works particularly well in our case because the fitness function which is derived from the linear duration invariant is linear. ACM Computing Classification System (1998): D.2.4, C.3.
LA - eng
KW - Approximate Model Checking; Verification; Real-Time System; Linear Duration Invariant; Genetic Algorithm; approximate model checking; verification; real-time system; linear duration invariant; genetic algorithm
UR - http://eudml.org/doc/252267
ER -

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