activity
20182023
most citedComparing Software Developers with ChatGPT: An Empirical Investigation

32 citations · 85 across the 9 of their papers we have counts for

collaborators

10 papers

cs.MA2023★ 1 cited

GPT-in-the-Loop: Adaptive Decision-Making for Multiagent Systems

Nathalia Nascimento, Paulo Alencar, Donald Cowan

This paper introduces the "GPT-in-the-loop" approach, a novel method combining the advanced reasoning capabilities of Large Language Models (LLMs) like Generative Pre-trained Trans…

cs.MA2023★ 4 cited

Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems

Nathalia Nascimento, Paulo Alencar, Donald Cowan

In autonomic computing, self-adaptation has been proposed as a fundamental paradigm to manage the complexity of multiagent systems (MASs). This achieved by extending a system with…

cs.SE2023★ 32 cited

Comparing Software Developers with ChatGPT: An Empirical Investigation

Nathalia Nascimento, Paulo Alencar, Donald Cowan

The advent of automation in particular Software Engineering (SE) tasks has transitioned from theory to reality. Numerous scholarly articles have documented the successful applicati…

cs.AI2021

A Reference Model for IoT Embodied Agents Controlled by Neural Networks

Nathalia Nascimento, Paulo Alencar, Donald Cowan +1

Embodied agents is a term used to denote intelligent agents, which are a component of devices belonging to the Internet of Things (IoT) domain. Each agent is provided with sensors…

cs.SE2021★ 11 cited

Machine Learning Model Development from a Software Engineering Perspective: A Systematic Literature Review

Giuliano Lorenzoni, Paulo Alencar, Nathalia Nascimento +1

Data scientists often develop machine learning models to solve a variety of problems in the industry and academy but not without facing several challenges in terms of Model Develop…

cs.MA2019★ 8 cited

Testing Self-Organizing Multiagent Systems

Nathalia Nascimento, Carlos Lucena, Paulo Alencar +1

Multiagent Systems (MASs) involve different characteristics, such as autonomy, asynchronous and social features, which make these systems more difficult to understand. Thus, there…

cs.AI2018★ 4 cited

An IoT Analytics Embodied Agent Model based on Context-Aware Machine Learning

Nathalia Nascimento, Paulo Alencar, Carlos Lucena +1

Agent-based Internet of Things (IoT) applications have recently emerged as applications that can involve sensors, wireless devices, machines and software that can exchange data and…

cs.MA2018

Machine Learning-based Variability Handling in IoT Agents

Nathalia Nascimento, Paulo Alencar, Carlos Lucena +1

Agent-based IoT applications have recently been proposed in several domains, such as health care, smart cities and agriculture. Deploying these applications in specific settings ha…

cs.SE2018★ 5 cited

Software Engineers vs. Machine Learning Algorithms: An Empirical Study Assessing Performance and Reuse Tasks

Nathalia Nascimento, Carlos Lucena, Paulo Alencar +1

Several papers have recently contained reports on applying machine learning (ML) to the automation of software engineering (SE) tasks, such as project management, modeling and deve…

cs.AI2018★ 20 cited

Engineering Cooperative Smart Things based on Embodied Cognition

Nathalia Moraes do Nascimento, Carlos Jose Pereira de Lucena

The goal of the Internet of Things (IoT) is to transform any thing around us, such as a trash can or a street light, into a smart thing. A smart thing has the ability of sensing, p…