Antonio Mastropaolo

Orcid: 0000-0002-7965-7712

According to our database1, Antonio Mastropaolo authored at least 27 papers between 2013 and 2024.

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

Timeline

Legend:

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Bibliography

2024
Log statements generation via deep learning: Widening the support provided to developers.
J. Syst. Softw., April, 2024

Code Review Automation: Strengths and Weaknesses of the State of the Art.
IEEE Trans. Software Eng., February, 2024

A Taxonomy of Self-Admitted Technical Debt in Deep Learning Systems.
CoRR, 2024

The Rise and Fall(?) of Software Engineering.
CoRR, 2024

Unveiling ChatGPT's Usage in Open Source Projects: A Mining-based Study.
Proceedings of the 21st IEEE/ACM International Conference on Mining Software Repositories, 2024

How do Hugging Face Models Document Datasets, Bias, and Licenses? An Empirical Study.
Proceedings of the 32nd IEEE/ACM International Conference on Program Comprehension, 2024

Towards Summarizing Code Snippets Using Pre-Trained Transformers.
Proceedings of the 32nd IEEE/ACM International Conference on Program Comprehension, 2024

Toward Automatically Completing GitHub Workflows.
Proceedings of the 46th IEEE/ACM International Conference on Software Engineering, 2024

Evaluating Code Summarization Techniques: A New Metric and an Empirical Characterization.
Proceedings of the 46th IEEE/ACM International Conference on Software Engineering, 2024

How the Training Procedure Impacts the Performance of Deep Learning-based Vulnerability Patching.
Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering, 2024

2023
Dataset of the paper: "How do Hugging Face Models Document Datasets, Bias, and Licenses? An Empirical Study".
Dataset, October, 2023

Using Transfer Learning for Code-Related Tasks.
IEEE Trans. Software Eng., April, 2023

Automated variable renaming: are we there yet?
Empir. Softw. Eng., March, 2023

Towards Automatically Addressing Self-Admitted Technical Debt: How Far Are We?
Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering, 2023

Automatically Generating Dockerfiles via Deep Learning: Challenges and Promises.
Proceedings of the IEEE/ACM International Conference on Software and System Processes, 2023

On the Robustness of Code Generation Techniques: An Empirical Study on GitHub Copilot.
Proceedings of the 45th IEEE/ACM International Conference on Software Engineering, 2023

2022
Replication Package for: Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks.
Dataset, January, 2022

Replication Package for: Using Deep Learning to Generate Complete Log Statements.
Dataset, January, 2022

Replication Package for: An Empirical Study on Code Comment Completion.
Dataset, January, 2022

Replication package for "An Empirical Study on the Usage of Transformer Models for Code Completion".
Dataset, January, 2022

An Empirical Study on the Usage of Transformer Models for Code Completion.
IEEE Trans. Software Eng., 2022

Using Pre-Trained Models to Boost Code Review Automation.
Proceedings of the 44th IEEE/ACM 44th International Conference on Software Engineering, 2022

Using Deep Learning to Generate Complete Log Statements.
Proceedings of the 44th IEEE/ACM 44th International Conference on Software Engineering, 2022

2021
An Adaptive Search Budget Allocation Approach for Search-Based Test Case Generation.
ACM Trans. Softw. Eng. Methodol., 2021

An Empirical Study on Code Comment Completion.
Proceedings of the IEEE International Conference on Software Maintenance and Evolution, 2021

Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks.
Proceedings of the 43rd IEEE/ACM International Conference on Software Engineering, 2021

2013
Legal documents categorization by compression.
Proceedings of the International Conference on Artificial Intelligence and Law, 2013


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