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A Hybrid Rule-Based and AI-Augmented Framework for Automatic Failure Recovery in DevOps Deployments — arXiv:2609.26838

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.26838
License (as archived): CC0-1.0
Archived: 2026-10-08T10:06:27.366Z
SHA-256 of the archived text: be93be9970baef83f133adb99adc0a089dee0818e10d9b50c59728e3e62e43d2
Stable URL of this page: https://metraipolis.com/documento/dc_808ebbbd0446

How to cite

A Hybrid Rule-Based and AI-Augmented Framework for Automatic Failure Recovery in DevOps Deployments — arXiv:2609.26838 — arXiv (en), https://arxiv.org/abs/2609.26838 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_808ebbbd0446 (Metraipolis archive dc_808ebbbd0446, sha256 be93be9970baef83f133adb99adc0a089dee0818e10d9b50c59728e3e62e43d2)

BibTeX

@misc{dc808ebbbd0446,
  title = {A Hybrid Rule-Based and AI-Augmented Framework for Automatic Failure Recovery in DevOps Deployments — arXiv:2609.26838},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.26838},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_808ebbbd0446; sha256 be93be9970baef83f133adb99adc0a089dee0818e10d9b50c59728e3e62e43d2},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.26838. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Automatic failure recovery is another difficult area of DevOps deployments due to the complexity of the system, high workload and the limitations of the traditional rules-based or manual approach. This study proposes a hybrid model integrating deterministic, rule-based recovery with the assistance of machine learning by fault prediction to automatically monitor failures, identify, classify and recover failures in real time. To test the architecture, a distributed log dataset of 100,000 records was used for models such as Decision Tree, Random Forest, Logistic Regression, LightGBM, Autoencoder with BiLSTM. The Decision Tree had a strong performance in defect identification with an F1 score of 80.8%, recall of 80.0%, precision of 81.6%, and accuracy of 89.4%. An impressive 83.3% success rate was achieved by the automated recovery measures, resulting in a 94.6% decrease in mean time to recovery (MTTR), a 95.4% reduction in downtime, and annual savings of 7,462. The combination of explainable rule-based logic and adaptive AI prediction in the framework means it is more resilient, operationally efficient, and scalable, and an effective, autonomous approach to DevOps environments evaluated under controlled experimental conditions.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-09-24 in cs.DC).
Categorie arXiv: Software Engineering (cs.SE) ; Distributed, Parallel, and Cluster Computing (cs.DC).
Pagina del lavoro: https://arxiv.org/abs/2609.26838 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T09:13:37.347Z dalla pagina https://arxiv.org/catchup/cs.DC/2026-09-24?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).