Quantum Machine Learning-based Test Oracle for Autonomous Mobile Robots

TL;DR

This paper introduces QuReBot, a quantum machine learning-based test oracle for autonomous mobile robots, which improves prediction accuracy and convergence over classical methods, aiding regression testing in complex environments.

Contribution

Development of a hybrid quantum-classical test oracle framework for robot software regression testing, demonstrating improved accuracy and convergence over classical neural networks.

Findings

QuReBot achieves 15% lower prediction error than classical neural networks.

QRC alone fails to converge, highlighting the need for hybrid approaches.

Practical guidance for optimal QuReBot configurations is provided.

Abstract

Robots are increasingly becoming part of our daily lives, interacting with both the environment and humans to perform their tasks. The software of such robots often undergoes upgrades, for example, to add new functionalities, fix bugs, or delete obsolete functionalities. As a result, regression testing of robot software becomes necessary. However, determining the expected correct behavior of robots (i.e., a test oracle) is challenging due to the potentially unknown environments in which the robots must operate. To address this challenge, machine learning (ML)-based test oracles present a viable solution. This paper reports on the development of a test oracle to support regression testing of autonomous mobile robots built by PAL Robotics (Spain), using quantum machine learning (QML), which enables faster training and the construction of more precise test oracles. Specifically, we propose a hybrid framework, QuReBot, that combines both quantum reservoir computing (QRC) and a simple neural network, inspired by residual connection, to predict the expected behavior of a robot. Results show that QRC alone fails to converge in our case, yielding high prediction error. In contrast, QuReBot converges and achieves 15% reduction of prediction error compared to the classical neural network baseline. Finally, we further examine QuReBot under different configurations and offer practical guidance on optimal settings to support future robot software testing.