output
20142026
most citedNaming the Pain in Requirements Engineering: Contemporary Problems, Causes, and Effects in Practice

279 citations

27 papers

cs.CR2026

Characterizing and Modeling the GitHub Security Advisories Review Pipeline

Claudio Segal, Paulo Segal, Carlos Eduardo Banjar +8

GitHub Security Advisories (GHSA) have become a central component of open-source vulnerability disclosure and are widely used by developers and security tools. A distinctive featur…

cs.NI2025★ 10 cited

Energy Efficiency in Network Slicing: Survey and Taxonomy

Adnei Willian Donatti, Marcia Cristina Machado, Marvin Alexander Lopez Martinez +7

Network Slicing (NS) is a fundamental feature of 5G, 6G, and future mobile networks, enabling logically isolated virtual networks over shared infrastructure. As data demand increas…

cs.ET2025★ 1 cited

AI-driven Orchestration at Scale: Estimating Service Metrics on National-Wide Testbeds

Rodrigo Moreira, Rafael Pasquini, Joberto S. B. Martins +2

Network Slicing (NS) realization requires AI-native orchestration architectures to efficiently and intelligently handle heterogeneous user requirements. To achieve this, network sl…

cs.CR2024★ 14 cited

An Intelligent Native Network Slicing Security Architecture Empowered by Federated Learning

Rodrigo Moreira, Rodolfo S. Villaca, Moises R. N. Ribeiro +4

Network Slicing (NS) has transformed the landscape of resource sharing in networks, offering flexibility to support services and applications with highly variable requirements in a…

cs.NI2023★ 32 cited

Enhancing Network Slicing Architectures with Machine Learning, Security, Sustainability and Experimental Networks Integration

Joberto S. B. Martins, Tereza C. Carvalho, Rodrigo Moreira +19

Network Slicing (NS) is an essential technique extensively used in 5G networks computing strategies, mobile edge computing, mobile cloud computing, and verticals like the Internet…

cs.LG2022

Reinforcement Learning Agent Design and Optimization with Bandwidth Allocation Model

Rafael F. Reale, Joberto S. B. Martins

Reinforcement learning (RL) is currently used in various real-life applications. RL-based solutions have the potential to generically address problems, including the ones that are…