32 citations · 39 across the 9 of their papers we have counts for
7 papers · 1 filter
Comparing Generative Chatbots Based on Process Requirements
Luis Fernando Lins, Nathalia Nascimento, Paulo Alencar +2
Business processes are commonly represented by modelling languages, such as Event-driven Process Chain (EPC), Yet Another Workflow Language (YAWL), and the most popular standard no…
Extending Variability-Aware Model Selection with Bias Detection in Machine Learning Projects
Cristina Tavares, Nathalia Nascimento, Paulo Alencar +1
Data science projects often involve various machine learning (ML) methods that depend on data, code, and models. One of the key activities in these projects is the selection of a m…
GPT in Data Science: A Practical Exploration of Model Selection
Nathalia Nascimento, Cristina Tavares, Paulo Alencar +1
There is an increasing interest in leveraging Large Language Models (LLMs) for managing structured data and enhancing data science processes. Despite the potential benefits, this i…
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…
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…
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…
Supporting Contextual Conversational Agent-Based Software Development
Glaucia Melo, Luis Fernando Lins, Paulo Alencar +1
Software Development (SD) is remarkably dynamic and is critically dependent on the knowledge acquired by the project's software developers as the project progresses. Software devel…