Regression Testing Optimization for ROS-based Autonomous Systems: A Comprehensive Review of Techniques

TL;DR

This paper provides a comprehensive survey of regression testing optimization techniques specifically designed for ROS-based autonomous systems, addressing unique challenges and proposing future research directions.

Contribution

It is the first systematic review categorizing and analyzing regression testing methods tailored for ROS autonomous systems, highlighting challenges and proposing future research avenues.

Findings

Analyzed 122 studies on regression testing for ROSAS

Categorized techniques into prioritization, minimization, and selection methods

Identified key challenges and proposed future research directions

Abstract

Regression testing plays a critical role in maintaining software reliability, particularly for ROS-based autonomous systems (ROSAS), which frequently undergo continuous integration and iterative development. However, conventional regression testing techniques face significant challenges when applied to autonomous systems due to their dynamic and non-deterministic behaviors, complex multi-modal sensor data, asynchronous distributed architectures, and stringent safety and real-time constraints. Although numerous studies have explored test optimization in traditional software contexts, regression testing optimization specifically for ROSAS remains largely unexplored. To address this gap, we present the first comprehensive survey systematically reviewing regression testing optimization techniques tailored for ROSAS. We analyze and categorize 122 representative studies into regression test case prioritization, minimization, and selection methods. A structured taxonomy is introduced to clearly illustrate their applicability and limitations within ROSAS contexts. Furthermore, we highlight major challenges specific to regression testing for ROSAS, including effectively prioritizing tests in response to frequent system modifications, efficiently minimizing redundant tests, and difficulty in accurately selecting impacted test cases. Finally, we propose research insights and identify promising future directions, such as leveraging frame-to-vector coverage metrics, multi-source foundation models, and neurosymbolic reasoning to enhance regression testing efficiency and effectiveness. This survey provides a foundational reference and practical roadmap for advancing the state-of-the-art in regression testing optimization for ROSAS.