activity
20242026
most citedA Survey on Failure Analysis and Fault Injection in AI Systems

3 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SE2026

Software Aging in LLM-Generated Applications: Runtime Evidence, Static Analysis, and Human-Written Comparisons

Cesar Santos, Michele Vitagliano, Roberto Natella +1

Large Language Models (LLMs) are increasingly used to generate executable software systems from natural language specifications, accelerating development and reducing manual implem…

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.SE2025★ 2 cited

Investigating Software Aging in LLM-Generated Software Systems

César Santos, Ermeson Andrade, Roberto Natella

Automatically generated software, especially code produced by Large Language Models (LLMs), is increasingly adopted to accelerate development and reduce manual effort. However, lit…

cs.CY2024★ 2 cited

Evaluation of Systems Programming Exercises through Tailored Static Analysis

Roberto Natella

In large programming classes, it takes a significant effort from teachers to evaluate exercises and provide detailed feedback. In systems programming, test cases are not sufficient…

cs.SE2024★ 3 cited

A Survey on Failure Analysis and Fault Injection in AI Systems

Guangba Yu, Gou Tan, Haojia Huang +4

The rapid advancement of Artificial Intelligence (AI) has led to its integration into various areas, especially with Large Language Models (LLMs) significantly enhancing capabiliti…