5 papers
Teamwork makes the dream work: LLMs-Based Agents for GitHub README.MD Summarization
Duc S. H. Nguyen, Bach G. Truong, Phuong T. Nguyen +2
The proliferation of Large Language Models (LLMs) in recent years has realized many applications in various domains. Being trained with a huge of amount of data coming from various…
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…
Exploring User Privacy Awareness on GitHub: An Empirical Study
Costanza Alfieri, Juri Di Rocco, Paola Inverardi +1
GitHub provides developers with a practical way to distribute source code and collaboratively work on common projects. To enhance account security and privacy, GitHub allows its us…
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…