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From the 1 of 10 linked papers with an AI index.

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10 papers

cs.LG2026

AIGen: Automating AI Bill of Materials Generation Through Hybrid MLOps Integration

Federica Pepe, Daniele Bifolco, Costantino Martignetti +4

AIGen is a modular tool that automatically creates SPDX‑compliant AI Bills of Materials by integrating with MLflow, using heuristics and large language models, and offering extensi…

cs.SE2026

Tiny Machine-Learning Operations within Cyber-Physical Systems: a Field Study

Filippo Scaramuzza, Damian A. Tamburri

Machine-Learning Operations (MLOps) is maturing into a software-engineering discipline, yet its tiny-scale variant (TinyMLOps)-targeting the resource-constrained microcontrollers e…

cs.SE2026

A Taxonomy of Runtime Faults in Model Context Protocol Servers

Joshua Owotogbe, Indika Kumara, Willem-Jan van den Heuvel +3

MCP (Model Context Protocol) enables LLMs (Large Language Models) to interact with external tools and data sources via a standardized protocol. Its rapid adoption in tool-augmented…

cs.SE2026

"Show Me You Comply... Without Showing Me Anything": Zero-Knowledge Software Auditing for AI-Enabled Systems

Filippo Scaramuzza, Renato Cordeiro Ferreira, Giovanni Quattrocchi +2

Classical software verification and validation techniques, such as procedural audits, formal methods, or model documentation, are the traditional mechanisms used to achieve the ver…

cs.SE2026

Privacy Engineering: A Systematic Literature Review

Nemania Borovits, Damian Andrew Tamburri, Willem-Jan van den Heuvel

Privacy obligations under GDPR increasingly shape software engineering. We synthesize 90 studies from 2018 to 2025 using a systematic review with thematic synthesis to chart privac…

cs.AI2025

IaC Generation with LLMs: An Error Taxonomy and A Study on Configuration Knowledge Injection

Roman Nekrasov, Stefano Fossati, Indika Kumara +2

Large Language Models (LLMs) currently exhibit low success rates in generating correct and intent-aligned Infrastructure as Code (IaC). This research investigated methods to improv…