3 citations · 3 across the 6 of their papers we have counts for
6 papers · 1 filter
Empirical Characterization of Logging Smells in Machine Learning Code
Patrick Loic Foalem, Leuson Da Silva, Foutse Khomh +2
Logging plays a central role in ensuring reproducibility, observability, and reliability in machine learning (ML) systems. While logging is generally considered a good engineering…
An Empirical Study of Policy-as-Code Adoption in Open-Source Software Projects
Patrick Loic Foalem, Foutse Khomh, Leuson Da Silva +1
\textbf{Context:} Policy-as-Code (PaC) has become a foundational approach for embedding governance, compliance, and security requirements directly into software systems. While orga…
Empirical Characterization of Logging Smells in Machine Learning Code
Patrick Loic Foalem, Leuson Da Silva, Foutse Khomh +2
\underline{Context:} Logging is a fundamental yet complex practice in software engineering, essential for monitoring, debugging, and auditing software systems. With the increasing…
Performance Smells in ML and Non-ML Python Projects: A Comparative Study
François Belias, Leuson Da Silva, Foutse Khomh +1
Python is widely adopted across various domains, especially in Machine Learning (ML) and traditional software projects. Despite its versatility, Python is susceptible to performanc…
A Taxonomy of Inefficiencies in LLM-Generated Python Code
Altaf Allah Abbassi, Leuson Da Silva, Amin Nikanjam +1
Large Language Models (LLMs) are widely adopted for automated code generation with promising results. Although prior research has assessed LLM-generated code and identified various…
Impact of LLM-based Review Comment Generation in Practice: A Mixed Open-/Closed-source User Study
Doriane Olewicki, Leuson Da Silva, Suhaib Mujahid +6
We conduct a large-scale empirical user study in a live setup to evaluate the acceptance of LLM-generated comments and their impact on the review process. This user study was perfo…