Work

ROSCallBaX: Statically Detecting Inconsistencies in Callback Function Setup of Robotic Systems

Year: 2025

Type: article

Abstract: Increasingly popular Robot Operating System (ROS) framework allows building robotic systems by integrating newly developed and/or reused modules, where the modules can use different versions of the framework (e.g., ROS1 or ROS2) and programming language (e.g. C++ or Python). The majority of such robotic systems’ work happens in callbacks. The framework provides various elements for initializing callbacks and for setting up the execution of callbacks. It is the responsibility of developers to compose callbacks and their execution setup elements, and hence can lead to inconsistencies related to the setup of callback execution due to developer’s incomplete knowledge of the semantics of elements in various versions of the framework. Some of these inconsistencies do not throw errors at runtime, making their detection difficult for developers. We propose a static approach to detecting such inconsistencies by extracting a static view of the composition of robotic system’s callbacks and their execution setup, and then checking it against the composition conventions based on the elements’ semantics. We evaluate our ROSCallBaX prototype on the dataset created from the posts on developer forums and ROS projects that are publicly available. The evaluation results show that our approach can detect real inconsistencies.

Source: Proceedings of the ACM on software engineering.

DOI: 10.1145/3715748

Authors: Sayali Kate, Yifei Gao, Shiwei Feng, Xiangyu Zhang

Institution: Purdue University West Lafayette

Language: English

Topic: Software Engineering Research

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, Defense Advanced Research Projects Agency, Intelligence Advanced Research Projects Activity, Office of Naval Research