A Myhill-Nerode Theorem for Register Automata and Symbolic Trace Languages

TL;DR

This paper extends the classical Myhill-Nerode theorem to register automata with symbolic trace semantics, enabling the characterization and learning of regular symbolic languages that incorporate input constraints.

Contribution

It introduces a generalized Myhill-Nerode theorem for register automata using three relations, establishing a foundation for grey-box learning algorithms in data-parameter constrained settings.

Findings

Proves the symbolic language of a register automaton is regular.

Constructs automata for any regular symbolic language.

Provides a basis for scalable grey-box learning algorithms.

Abstract

We propose a new symbolic trace semantics for register automata (extended finite state machines) which records both the sequence of input symbols that occur during a run as well as the constraints on input parameters that are imposed by this run. Our main result is a generalization of the classical Myhill-Nerode theorem to this symbolic setting. Our generalization requires the use of three relations to capture the additional structure of register automata. Location equivalence $\equiv_l$ captures that symbolic traces end in the same location, transition equivalence $\equiv_t$ captures that they share the same final transition, and a partial equivalence relation $\equiv_r$ captures that symbolic values $v$ and $v'$ are stored in the same register after symbolic traces $w$ and $w'$, respectively. A symbolic language is defined to be regular if relations $\equiv_l$, $\equiv_t$ and $\equiv_r$ exist that satisfy certain conditions, in particular, they all have finite index. We show that the symbolic language associated to a register automaton is regular, and we construct, for each regular symbolic language, a register automaton that accepts this language. Our result provides a foundation for grey-box learning algorithms in settings where the constraints on data parameters can be extracted from code using e.g. tools for symbolic/concolic execution or tainting. We believe that moving to a grey-box setting is essential to overcome the scalability problems of state-of-the-art black-box learning algorithms.