Understanding Misconfigurations in ROS: An Empirical Study and Current Approaches

TL;DR

This paper investigates misconfigurations in ROS by analyzing developer questions and existing detection methods, identifying gaps and proposing directions for future improvements to enhance robot software reliability.

Contribution

It provides a comprehensive categorization of ROS misconfigurations and evaluates the coverage of current detection techniques, highlighting significant gaps.

Findings

Identified 12 high-level misconfiguration categories and 50 sub-categories.

27 categories of misconfigurations are not addressed by existing detection techniques.

Provides a foundation for future research to improve ROS configuration reliability.

Abstract

The Robot Operating System (ROS) is a popular framework and ecosystem that allows developers to build robot software systems from reusable, off-the-shelf components. Systems are often built by customizing and connecting components via configuration files. While reusable components theoretically allow rapid prototyping, ensuring proper configuration and connection is challenging, as evidenced by numerous questions on developer forums. Developers must abide to the often unchecked and unstated assumptions of individual components. Failure to do so can result in misconfigurations that are only discovered during field deployment, at which point errors may lead to unpredictable and dangerous behavior. Despite misconfigurations having been studied in the broader context of software engineering, robotics software (and ROS in particular) poses domain-specific challenges with potentially disastrous consequences. To understand and improve the reliability of ROS projects, it is critical to identify the types of misconfigurations faced by developers. To that end, we perform a study of ROS Answers, a Q&A platform, to identify and categorize misconfigurations that occur during ROS development. We then conduct a literature review to assess the coverage of these misconfigurations by existing detection techniques. In total, we find 12 high-level categories and 50 sub-categories of misconfigurations. Of these categories, 27 are not covered by existing techniques. To conclude, we discuss how to tackle those misconfigurations in future work.