9 citations · 16 across the 5 of their papers we have counts for
6 papers
Revisiting Feedback-Driven LLM Code Repair: A Replication and Exploratory Java Extension
Louis Lalonde, Wassim Keddache, Thomas Perron Touchette +2
Since the advent of Large Language Models (LLMs), practitioners have increasingly leveraged them to support their software engineering tasks, including automated code repair, showi…
Refactoring with LLMs: Bridging Human Expertise and Machine Understanding
Yonnel Chen Kuang Piao, Jean Carlors Paul, Leuson Da Silva +3
Code refactoring is a fundamental software engineering practice aimed at improving code quality and maintainability. Despite its importance, developers often neglect refactoring du…
ReCatcher: Towards LLMs Regression Testing for Code Generation
Altaf Allah Abbassi, Leuson Da Silva, Amin Nikanjam +1
Large Language Models (LLMs) for code generation evolve rapidly through fine-tuning, merging, or new model releases. However, such updates can introduce regressions, not only in co…
Continuously Learning Bug Locations
Paulina Stevia Nouwou Mindom, Leuson Da Silva, Amin Nikanjam +1
Automatically locating buggy changesets associated with bug reports is crucial in the software development process. Deep Learning (DL)-based techniques show promising results by le…
LLMs and Stack Overflow Discussions: Reliability, Impact, and Challenges
Leuson Da Silva, Jordan Samhi, Foutse Khomh
Since its release in November 2022, ChatGPT has shaken up Stack Overflow, the premier platform for developers queries on programming and software development. Demonstrating an abil…
Exploring Security Practices in Infrastructure as Code: An Empirical Study
Alexandre Verdet, Mohammad Hamdaqa, Leuson Da Silva +1
Cloud computing has become popular thanks to the widespread use of Infrastructure as Code (IaC) tools, allowing the community to conveniently manage and configure cloud infrastruct…