Luca Buratti

According to our database1, Luca Buratti authored at least 14 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Analyzing source code vulnerabilities in the D2A dataset with ML ensembles and C-BERT.
Empir. Softw. Eng., April, 2024

Ansible Lightspeed: A Code Generation Service for IT Automation.
CoRR, 2024

2023
Learning Transfers over Several Programming Languages.
CoRR, 2023

Beyond Accuracy: Evaluating Self-Consistency of Code Large Language Models with IdentityChain.
CoRR, 2023

Automated Code generation for Information Technology Tasks in YAML through Large Language Models.
CoRR, 2023

CONCORD: Clone-Aware Contrastive Learning for Source Code.
Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis, 2023

Invited: Automated Code generation for Information Technology Tasks in YAML through Large Language Models.
Proceedings of the 60th ACM/IEEE Design Automation Conference, 2023

2022
Varangian: A Git Bot for Augmented Static Analysis.
Proceedings of the 19th IEEE/ACM International Conference on Mining Software Repositories, 2022

Towards Learning (Dis)-Similarity of Source Code from Program Contrasts.
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

2021
Contrastive Learning for Source Code with Structural and Functional Properties.
CoRR, 2021

Project CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks.
CoRR, 2021

CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis.
Proceedings of the 43rd IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice, 2021

2020
Exploring Software Naturalness through Neural Language Models.
CoRR, 2020


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