Search Results for author: Paulo Alencar

Found 16 papers, 1 papers with code

Comparing Generative Chatbots Based on Process Requirements

no code implementations28 Nov 2023 Luis Fernando Lins, Nathalia Nascimento, Paulo Alencar, Toacy Oliveira, Donald Cowan

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 notation for modelling business processes, the Business Process Model and Notation (BPMN).

Language Modelling

Extending Variability-Aware Model Selection with Bias Detection in Machine Learning Projects

no code implementations23 Nov 2023 Cristina Tavares, Nathalia Nascimento, Paulo Alencar, Donald Cowan

ML model selection depends on several factors, which include data-related attributes such as sample size, functional requirements such as the prediction algorithm type, and non-functional requirements such as performance and bias.

Bias Detection Model Selection

GPT in Data Science: A Practical Exploration of Model Selection

no code implementations20 Nov 2023 Nathalia Nascimento, Cristina Tavares, Paulo Alencar, Donald Cowan

There is an increasing interest in leveraging Large Language Models (LLMs) for managing structured data and enhancing data science processes.

Decision Making Model Selection

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

no code implementations21 Aug 2023 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 Transformers (GPT) with multiagent (MAS) systems.

Decision Making

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

no code implementations12 Jul 2023 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).

Language Modelling Large Language Model

Comparing Software Developers with ChatGPT: An Empirical Investigation

no code implementations19 May 2023 Nathalia Nascimento, Paulo Alencar, Donald Cowan

The advent of automation in particular Software Engineering (SE) tasks has transitioned from theory to reality.

Chatbot Fairness +1

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

no code implementations15 Feb 2021 Giuliano Lorenzoni, Paulo Alencar, Nathalia Nascimento, Donald Cowan

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 Development.

BIG-bench Machine Learning

A Reference Model for IoT Embodied Agents Controlled by Neural Networks

no code implementations15 Feb 2021 Nathalia Nascimento, Paulo Alencar, Donald Cowan, Carlos Lucena

In this paper, we propose a reference model based on statecharts that offers abstractions tailored to the development of IoT applications.

A Cognitive and Machine Learning-Based Software Development Paradigm Supported by Context

no code implementations10 Feb 2021 Glaucia Melo, Paulo Alencar, Donald Cowan

First, as software development is a complex and dynamic activity, these tasks are highly dependent on the characteristics of the software project and its context, and developers need comprehensive support in terms of information and guidance based on the task context.

Recommendation Systems Software Engineering

KryptoOracle: A Real-Time Cryptocurrency Price Prediction Platform Using Twitter Sentiments

1 code implementation21 Feb 2020 Shubhankar Mohapatra, Nauman Ahmed, Paulo Alencar

Cryptocurrencies, such as Bitcoin, are becoming increasingly popular, having been widely used as an exchange medium in areas such as financial transaction and asset transfer verification.

Decision Making Sentiment Analysis

Testing Self-Organizing Multiagent Systems

no code implementations3 Apr 2019 Nathalia Nascimento, Carlos Lucena, Paulo Alencar, Carlos Juliano Viana

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

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

no code implementations14 Dec 2018 Nathalia Nascimento, Paulo Alencar, Carlos Lucena, Donald Cowan

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 be accessed remotely.

BIG-bench Machine Learning

Machine Learning-based Variability Handling in IoT Agents

no code implementations12 Feb 2018 Nathalia Nascimento, Paulo Alencar, Carlos Lucena, Donald Cowan

Agent-based IoT applications have recently been proposed in several domains, such as health care, smart cities and agriculture.

BIG-bench Machine Learning feature selection

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

no code implementations4 Feb 2018 Nathalia Nascimento, Carlos Lucena, Paulo Alencar, Donald Cowan

Third, we compared how software engineers fare against machine-learning algorithms when accomplishing the performance and reuse tasks based on criteria such as energy consumption and safety.

BIG-bench Machine Learning Management

A Survey on Domain-Specific Languages for Machine Learning in Big Data

no code implementations24 Feb 2016 Ivens Portugal, Paulo Alencar, Donald Cowan

Machine Learning algorithms can be used in Big Data to make better and more accurate inferences.

BIG-bench Machine Learning

The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review

no code implementations17 Nov 2015 Ivens Portugal, Paulo Alencar, Donald Cowan

This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies research opportunities for software engineering research.

BIG-bench Machine Learning Recommendation Systems

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