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20242026
most citedAutomatic Library Migration Using Large Language Models: First Results

14 citations · 14 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SE2026

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…

cs.SE2026

Code Smells in Clojure: Initial Findings from a Grey Literature Review

Walber Araújo, José Truta, Lucas Vegi +2

Code smells are widely used indicators of poor code quality, revealing structural problems and areas where improvement can be made. Although extensively studied in object-oriented…

cs.SE2025

Using Copilot Agent Mode to Automate Library Migration: A Quantitative Assessment

Aylton Almeida, Laerte Xavier, Marco Tulio Valente

Keeping software systems up to date is essential to avoid technical debt, security vulnerabilities, and the rigidity typical of legacy systems. However, updating libraries and fram…

cs.SE2024★ 14 cited

Automatic Library Migration Using Large Language Models: First Results

Aylton Almeida, Laerte Xavier, Marco Tulio Valente

Despite being introduced only a few years ago, Large Language Models (LLMs) are already widely used by developers for code generation. However, their application in automating othe…

cs.SE2024

On the Quality of AI-Generated Source Code Comments: A Comprehensive Evaluation

Ian Guelman, Arthur Gregório Leal, Laerte Xavier +1

This paper investigates the quality of source code comments automatically generated by Large Language Models (LLMs). While AI-based comment generation has emerged as a promising so…