6 papers
Simplicity by Obfuscation: Evaluating LLM-Driven Code Transformation with Semantic Elasticity
Lorenzo De Tomasi, Claudio Di Sipio, Antinisca Di Marco +1
Code obfuscation is the conversion of original source code into a functionally equivalent but less readable form, aiming to prevent reverse engineering and intellectual property th…
Detecting Malicious Source Code in PyPI Packages with LLMs: Does RAG Come in Handy?
Motunrayo Ibiyo, Thinakone Louangdy, Phuong T. Nguyen +2
Malicious software packages in open-source ecosystems, such as PyPI, pose growing security risks. Unlike traditional vulnerabilities, these packages are intentionally designed to d…
On the use of Large Language Models in Model-Driven Engineering
Juri Di Rocco, Davide Di Ruscio, Claudio Di Sipio +2
Model-Driven Engineering (MDE) has seen significant advancements with the integration of Machine Learning (ML) and Deep Learning (DL) techniques. Building upon the groundwork of pr…
Towards Synthetic Trace Generation of Modeling Operations using In-Context Learning Approach
Vittoriano Muttillo, Claudio Di Sipio, Riccardo Rubei +2
Producing accurate software models is crucial in model-driven software engineering (MDE). However, modeling complex systems is an error-prone task that requires deep application do…
Automatic Categorization of GitHub Actions with Transformers and Few-shot Learning
Phuong T. Nguyen, Juri Di Rocco, Claudio Di Sipio +3
In the GitHub ecosystem, workflows are used as an effective means to automate development tasks and to set up a Continuous Integration and Delivery (CI/CD pipeline). GitHub Actions…
Automated categorization of pre-trained models for software engineering: A case study with a Hugging Face dataset
Claudio Di Sipio, Riccardo Rubei, Juri Di Rocco +2
Software engineering (SE) activities have been revolutionized by the advent of pre-trained models (PTMs), defined as large machine learning (ML) models that can be fine-tuned to pe…