LN: Bruneliere et al. (2022) — AIDOaRt: AI-augmented Automation for DevOps
Bibliographic Reference
Citation: Bruneliere, H., Muttillo, V., Eramo, R., et al. (2022). AIDOaRt: AI-augmented automation for DevOps, a model-based framework for continuous development in cyber-physical systems. Microprocessors and Microsystems, 94, 104672. https://doi.org/10.1016/j.micpro.2022.104672
Important Note
Overview
The bibliography lists “Zampetti et al.” as the first author (incorrect) and volume 90 (incorrect). The verified first author is Hugo Bruneliere and the volume is 94.
Pass 1 — Bird’s Eye View (5 Cs)
| C | Assessment |
|---|---|
| Category | Framework proposal (EU project H2020) |
| Context | EU Horizon 2020 research project on AI for DevOps in cyber-physical systems |
| Correctness | Based on deployed tools across 9 industrial partners. |
| Contributions | (1) AI-augmented automation framework for DevOps pipelines; (2) Model-based approach to integrating AI into CI/CD; (3) Covers requirements, testing, monitoring, and deployment phases |
| Clarity | Good. Framework-level paper. |
Relevance: ⭐⭐⭐
AIDOaRt addresses AI in DevOps at the pipeline level. PUMA’s scope (issue triage, estimation) is narrower but related. Reference for contextualising PUMA within the broader AI-augmented SE ecosystem.
PUMA Connection
AIDOaRt demonstrates the industrial demand for AI-assisted DevOps automation. PUMA’s triage module (Stage 1) can be seen as a lightweight, reproducible implementation of one AIDOaRt capability. Cite in Section 1.1 (existing commercial solutions and their limitations).