Online Test Synthesis From Requirements: Enhancing Reinforcement Learning with Game Theory

TL;DR

This paper introduces a novel approach combining Monte Carlo Tree Search and game theory to automatically generate test cases from automata-based requirements, improving testing efficiency for reactive systems.

Contribution

It presents a new heuristic method that biases Monte Carlo Tree Search towards promising inputs, enhancing online test synthesis from automata specifications.

Findings

Heuristic accelerates Monte Carlo Tree Search convergence.

Improved testing performance demonstrated experimentally.

Automata requirements modeled as a game between implementation and tester.

Abstract

We consider the automatic online synthesis of black-box test cases from functional requirements specified as automata for reactive implementations. The goal of the tester is to reach some given state, so as to satisfy a coverage criterion, while monitoring the violation of the requirements. We develop an approach based on Monte Carlo Tree Search, which is a classical technique in reinforcement learning for efficiently selecting promising inputs. Seeing the automata requirements as a game between the implementation and the tester, we develop a heuristic by biasing the search towards inputs that are promising in this game. We experimentally show that our heuristic accelerates the convergence of the Monte Carlo Tree Search algorithm, thus improving the performance of testing.