An Empirical Study of Interaction Bugs in ROS-based Software

TL;DR

This empirical study investigates interaction bugs in ROS-based robotic systems, analyzing 121 bugs across ten projects to understand their causes, types, and impacts, aiming to improve system robustness.

Contribution

The paper provides a comprehensive empirical analysis of interaction bugs in ROS robotics, categorizing bug types, root causes, and fixing strategies, which was previously underexplored.

Findings

Identified three major types of interaction bugs: intra-system, hardware, and environmental.

Analyzed root causes and fixing strategies for 121 bugs across ten ROS projects.

Provided insights to improve detection and prevention of interaction bugs in robotics.

Abstract

Modern robotic systems integrate multiple independent software and hardware components, each responsible for distinct functionalities such as perception, decision-making, and execution. These components interact extensively to accomplish complex end-to-end tasks. As a result, the overall system reliability depends not only on the correctness of individual components, but also on the correctness of their interactions. Failures often manifest at the boundaries between components, yet interaction-related reliability issues in robotics--referred to here as interaction bugs (iBugs)--remain underexplored. This work presents an empirical study of iBugs within robotic systems built using the Robot Operating System (ROS), a widely adopted open-source robotics framework. A total of 121 iBugs were analyzed across ten actively maintained and representative ROS projects. The identified iBugs are categorized into three major types: intra-system iBugs, hardware iBugs, and environmental iBugs, covering a broad range of interaction scenarios in robotics. The analysis includes an examination of root causes, fixing strategies, and the impact of these bugs. Several findingsa are derived that shed light on the nature of iBugs and suggest directions for improving their prevention and detection. These insights aim to inform the design of more robust and safer robotic systems.