65 citations · 71 across the 11 of their papers we have counts for
13 papers · 1 filter
Type Hints in Python Libraries and Frameworks: An Empirical Analysis of Adoption and Maintenance
Thiago Roberto Magalhães, Fabio Petrillo, João Eduardo Montandon
Context: In Python, type hints allow developers to annotate variables and functions with explicit type information, improving code clarity and reliability. Although type hints are…
Legacy System Modernization with Coding Agents: A Case Study
Iago da Silva Rodrigues Alves, Cristiano Politowski, João Eduardo Montandon
Legacy systems built on discontinued platforms are a recurring technological liability in organizations that depend on these applications to sustain critical business processes. Al…
Configuration Smells in AGENTS.md Files: Common Mistakes in Configuring Coding Agents
Helio Victor F. dos Santos, Vitor Costa, Joao Eduardo Montandon +2
Coding agents are increasingly used to automate software engineering tasks. To guide their behavior, these agents commonly rely on configuration files, typically named AGENTS. md o…
What's Inside a GitHub Repository? An Empirical Study on the Contents of 10K Projects
Andre Hora, João Eduardo Montandon, Diego Elias Costa
GitHub is the largest code hosting platform, with millions of repositories spanning multiple technologies. Despite this, little is known about the actual contents of GitHub's repos…
Testing Framework Migration with Large Language Models
Altino Alves, João Eduardo Montandon, Andre Hora
Python developers rely on two major testing frameworks: \texttt{unittest} and \texttt{Pytest}. While \texttt{Pytest} offers simpler assertions, reusable fixtures, and better intero…
Beyond Strict Rules: Assessing the Effectiveness of Large Language Models for Code Smell Detection
Saymon Souza, Amanda Santana, Eduardo Figueiredo +3
Code smells are symptoms of potential code quality problems that may affect software maintainability, thus increasing development costs and impacting software reliability. Large la…