Regression Test Selection for Updated Capability Modules in Compositional ML Systems via Atomic-Quality Probes
arXiv:2604.26689v4 Announce Type: replace-cross Abstract: Compositional machine-learning (ML) systems assemble runtime behavior from libraries of independently re-trained capability modules. Replacing one module raises a regression-testing question that static dependence analysis cannot answer: which existing compositions stay valid, and at what test cost? We frame capability updates as regression test selection (RTS) and contribute four results. First, a paired cross-version swap protocol isolates the marginal effect of a single module update. Second, on two contact-rich manipulation tasks we
The increasing complexity and modularity of ML systems, particularly in robotics, necessitates robust testing methodologies to ensure reliability and safety as parts are updated.
This research addresses a critical challenge in real-world deployment of advanced AI, allowing for more efficient and safer integration of new capabilities in complex robotic systems.
This research introduces methods to efficiently identify and test critical components in compositional ML systems after module updates, minimizing regression risks in AI applications like robotics.
- · AI developers
- · Robotics companies
- · Automated testing platforms
- · Industries deploying AI systems
- · Developers relying on manual regression testing
- · Companies with brittle, non-modular AI architectures
Improved reliability and faster iteration cycles for complex AI systems, especially in robotics and autonomous agents.
Accelerated deployment and broader adoption of AI in safety-critical applications due to enhanced testing assurances.
Increased public trust in AI technologies as their robustness and predictable behavior are demonstrably improved through rigorous testing.
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Read at arXiv cs.AI