🤖 AI Summary
Existing regression testing approaches for Robot Operating System–based Autonomous Systems (ROSAS) lack systematic optimization strategies to address challenges arising from dynamic behaviors, multimodal perception, asynchronous architectures, and real-time safety constraints.
Method: This work proposes the first taxonomy for ROSAS regression test optimization, structured along three dimensions—test prioritization, minimization, and selection—and introduces novel techniques: frame-to-vector coverage metrics, multi-source foundation model–driven test generation, and neuro-symbolic reasoning–based verification. The framework is developed through a systematic literature review of 122 papers, taxonomy modeling, and technology roadmap design.
Contribution/Results: The study delivers a scalable, reusable regression testing optimization framework; establishes the first theoretical taxonomy for ROSAS testing research; and provides industry with the inaugural practical guideline for ROSAS regression testing—bridging the gap between academic rigor and industrial applicability.
📝 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.