Igor Buzhinsky

Automatic Inference of Finite-State Plant Models From Traces and Temporal Properties

Closed-loop model checking, a formal verification technique for industrial automation systems, increases the richness of specifications to be checked and reduces the state space to be verified compared to the open-loop case. To be applied, it needs the controller and the plant formal models to be coupled. There are approaches for controller synthesis, but little has been done regarding plant model construction. While manual plant modeling is time consuming and error-prone, discretizing a simulation model of the plant leads to state excess. This paper aims to solve the problem of automatic plant model construction from existing specification, which is represented in the form of plant behavior examples, or traces, and temporal properties. The proposed method, which is based on the translation of the problem to the Boolean satisfiability problem, is evaluated and shown to be applicable on several case study plant model synthesis tasks and on randomly generated problem instances.

Inducing finite state machines from training samples using ant colony optimization

A method for control finite state machine (FSM) induction in which an ant colony optimization algorithm is used for search optimization is proposed. The efficiency of this method is estimated using the generation of FSMs for controlling a model of an unmanned aerial vehicle (UAV). It is shown that the proposed method outperforms the method based on genetic algorithms both in terms of performance and quality.

Plant model inference for closed-loop verification of control systems: Initial explorations

Closed-loop model checking, a formal verification technique for industrial automation systems, increases the richness of specifications to be checked and often helps to reduce size of the state space to be verified compared with the open-loop case. To be applied, it needs two components — the controller and the plant models — to be coupled. While there are approaches for obtaining controller models from implementation, specification or behavior examples, little has been done regarding automation of plant model construction. This paper aims to solve the problem of automatic plant model construction from existing specification, which is represented in the form of plant behavior examples and temporal properties.

Exact finite-state machine identification from scenarios and temporal properties

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.

Test-Based Induction of Finite-State Machines with Continuous Output Actions

Abstract In this paper we improve an earlier developed method of finite-state machine (FSM) induction for controlling objects with complex behavior. This method allows to construct FSMs with continuous (real-valued) output actions. A set of human-created training samples serves as input data for it. We apply an ant colony optimization algorithm and a (μ, λ)-evolution strategy for solving the problem as more effective than a genetic algorithm used in the initial method. The modification of the method is evaluated on the problem of unmanned aircraft control.

Modification of the method of generation of control finite-state machines with continuous actions based on training examples

Control finite-state machines can be used in the development of reliable control systems due to their clarity and because it is possible to formally verify them. The paper deals with resolving the problem of the generation of machines that control plants with complex behavior based on training examples. The input and output actions of the machines are given by real numbers. A method for the generation of machines is proposed. It is a modification of the previously proposed approaches based on the genetic and ant colony optimization algorithms. Changes include a new way of representing machines and improving the fitness function. The method makes it possible to generate machines whose behavior is more consistent with training examples than the behavior of machines generated by the known approaches.

Search-based construction of finite-state machines with real-valued actions: new representation model

In this paper a search-based method for constructing finite-state machines (FSMs) with continuous (real-valued) output actions is improved. A more flexible FSM representation model is presented and compared with the previous one on the problem of unmanned aircraft control.

A study on user-friendly formal specification languages for requirements formalization

Formal methods and languages are used to prove the correctness of various industrial systems, especially mission-critical ones. They can also be viewed as a means to provide safety and correctness demonstration to the stakeholders of such systems. In domains such as nuclear power plant engineering, the benefits from structured safety evidences would seem obvious. However, most stakeholders in nuclear power industry are not even familiar with formal notations. As a result, to promote the applications of formal methods in practice, the first step is to make formal specification languages (FSLs) more accessible. With user-friendly FSLs, users can focus on safety requirements rather than on their sophisticated formalization. This paper, as a preliminary work towards an integrated framework supporting transparent safety demonstration, reviews existing approaches applied to facilitate requirements formalization and formal specifications. Moreover, the common features of user-friendly languages and their tool supports are also summarized.

Improving the quality of supervised finite-state machine construction using real-valued variables

The use of finite-state machines (FSMs) is a reliable choice for control system design since they can be formally verified. In this paper a problem of constructing FSMs with real-valued input and control parameters is considered. It is supposed that a set of human-created behavior examples, or tests, is available. One of the earlier approaches for solving the problem suggested using genetic algorithms together with a transition labeling algorithm. This paper improves this approach via the use of real-valued variables which are calculated using the FSMs input data. FSMs with real-valued variables are represented as systems of linear controllers. The new approach allows to synthesize FSMs of better quality than it was possible earlier.