{
  "id": 5217,
  "url": "https://arxiv.org/abs/2604.26689v4",
  "title": "Regression Test Selection for Updated Capability Modules in Compositional ML Systems via Atomic-Quality Probes",
  "summary": "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,",
  "authors": "Xue Qin, Simin Luan, Cong Yang, Zhijun Li",
  "category": "research",
  "topics": null,
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-29T13:56:11.000Z",
  "fetched_at": "2026-07-14T16:31:35.575Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5217",
  "original_url": "https://arxiv.org/abs/2604.26689v4",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}