Regression Test Selection for Updated Capability Modules in Compositional ML Systems via Atomic-Quality Probes
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,
Record details
Published: 29 April 2026
Source: arXiv
Category: Research
Topics: unclassified
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
ECM Contracts: Contract-Aware, Versioned, and Governable Capability Interfaces for Embodied Agents
arXiv · 10 April 2026
Episodic-to-Semantic Consolidation Without Identity Drift
arXiv · 2 July 2026
Governed Individuation: Cryptographically Decoupling an Agent's Learning from Its Authority
arXiv · 6 July 2026
How to cite this record
ethics.ai (29 April 2026), “Regression Test Selection for Updated Capability Modules in Compositional ML Systems via Atomic-Quality Probes,” evidence record 5217, https://ethics.ai/record/5217 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.