## Abstract

A novel machine learning based approach was proposed recently as a complementary technique to the acceleration based methods for verifying infinite state systems. In this method, the set of states satisfying a fixpoint property is learnt as opposed to being iteratively computed. We extend the machine learning based approach to verifying general ω-regular properties that include both safety and liveness. To achieve this, we first develop a new fixpoint based characterization for the verification of ω-regular properties. Using this characterization, we present a general framework for verifying infinite state systems. We then instantiate our approach to the context of regular model checking where states are represented as strings over a finite alphabet and the transition relation of the system is given as a finite state transducer; unlike previous learning based algorithms, we make no assumption about the transducer being length-preserving. Using Angluin's L* algorithm for learning regular languages, we develop an algorithm for verification of ω-regular properties of such infinite state systems. The algorithm is a complete verification procedure for systems for whom the fixpoint can be represented as a regular set. We have implemented the technique in a tool called LEVER and use it to analyze some examples.

Original language | English (US) |
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Pages (from-to) | 45-60 |

Number of pages | 16 |

Journal | Lecture Notes in Computer Science |

Volume | 3440 |

DOIs | |

State | Published - 2005 |

Event | 11th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2005, held as part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2005 - Edinburgh, United Kingdom Duration: Apr 4 2005 → Apr 8 2005 |

## ASJC Scopus subject areas

- Theoretical Computer Science
- General Computer Science