Exact Finite-State Machine Identification from Scenarios and Temporal Properties

TL;DR

This paper introduces four exact methods for identifying the smallest finite-state machine from scenarios and temporal logic, with implementations based on SAT, QSAT, and backtracking, evaluated through case studies and benchmarks.

Contribution

It presents novel exact algorithms for minimum FSM identification from scenarios and temporal properties, including SAT and QSAT-based approaches, and provides an open-source implementation.

Findings

Iterative SAT-based method outperforms others

Methods successfully identify minimal FSMs in case studies

Compared approaches show advantages over inexact methods

Abstract

Finite-state models, such as finite-state machines (FSMs), aid software engineering in many ways. They are often used in formal verification and also can serve as visual software models. The latter application is associated with the problems of software synthesis and automatic derivation of software models from specification. Smaller synthesized models are more general and are easier to comprehend, yet the problem of minimum FSM identification has received little attention in previous research. This paper presents four exact methods to tackle the problem of minimum FSM identification from a set of test scenarios and a temporal specification represented in linear temporal logic. The methods are implemented as an open-source tool. Three of them are based on translations of the FSM identification problem to SAT or QSAT problem instances. Accounting for temporal properties is done via counterexample prohibition. Counterexamples are either obtained from previously identified FSMs, or based on bounded model checking. The fourth method uses backtracking. The proposed methods are evaluated on several case studies and on a larger number of randomly generated instances of increasing complexity. The results show that the Iterative SAT-based method is the leader among the proposed methods. The methods are also compared with existing inexact approaches, i.e. the ones which do not necessarily identify the minimum FSM, and these comparisons show encouraging results.