Mutation Testing for Industrial Robotic Systems

TL;DR

This paper adapts mutation testing for industrial robotic systems by creating domain-specific operators that generate meaningful mutants, improving test effectiveness and safety in robotic software.

Contribution

It introduces domain-specific mutation operators tailored for robotic actions and sensors, enhancing mutation testing applicability to IRS.

Findings

More informative mutants generated

Reduced invalid or equivalent mutants

Improved test suite quality for IRS

Abstract

Industrial robotic systems (IRS) are increasingly deployed in diverse environments, where failures can result in severe accidents and costly downtime. Ensuring the reliability of the software controlling these systems is therefore critical. Mutation testing, a technique widely used in software engineering, evaluates the effectiveness of test suites by introducing small faults, or mutants, into the code. However, traditional mutation operators are poorly suited to robotic programs, which involve message-based commands and interactions with the physical world. This paper explores the adaptation of mutation testing to IRS by defining domain-specific mutation operators that capture the semantics of robot actions and sensor readings. We propose a methodology for generating meaningful mutants at the level of high-level read and write operations, including movement, gripper actions, and sensor noise injection. An empirical study on a pick-and-place scenario demonstrates that our approach produces more informative mutants and reduces the number of invalid or equivalent cases compared to conventional operators. Results highlight the potential of mutation testing to enhance test suite quality and contribute to safer, more reliable industrial robotic systems.