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2020 – today
- 2024
[j3]Mauro Pezzè, Matteo Ciniselli, Luca Di Grazia, Niccolò Puccinelli, Ketai Qiu:
The Trailer of the ACM 2030 Roadmap for Software Engineering. ACM SIGSOFT Softw. Eng. Notes 49(4): 31-40 (2024)- [j2]Rosalia Tufano
, Ozren Dabic, Antonio Mastropaolo, Matteo Ciniselli, Gabriele Bavota:
Code Review Automation: Strengths and Weaknesses of the State of the Art. IEEE Trans. Software Eng. 50(2): 338-353 (2024) - [c7]Antonio Mastropaolo, Matteo Ciniselli, Massimiliano Di Penta, Gabriele Bavota:
Evaluating Code Summarization Techniques: A New Metric and an Empirical Characterization. ICSE 2024: 218:1-218:13 - [c6]Antonio Mastropaolo, Matteo Ciniselli, Luca Pascarella, Rosalia Tufano, Emad Aghajani, Gabriele Bavota:
Towards Summarizing Code Snippets Using Pre-Trained Transformers. ICPC 2024: 1-12 - [c5]Matteo Ciniselli, Alberto Martin-Lopez, Gabriele Bavota:
On the Generalizability of Deep Learning-based Code Completion Across Programming Language Versions. ICPC 2024: 99-111 - [i10]Rosalia Tufano, Ozren Dabic, Antonio Mastropaolo, Matteo Ciniselli, Gabriele Bavota:
Code Review Automation: Strengths and Weaknesses of the State of the Art. CoRR abs/2401.05136 (2024) - [i9]Antonio Mastropaolo, Matteo Ciniselli, Luca Pascarella, Rosalia Tufano, Emad Aghajani, Gabriele Bavota:
Towards Summarizing Code Snippets Using Pre-Trained Transformers. CoRR abs/2402.00519 (2024) - [i8]Matteo Ciniselli, Alberto Martin-Lopez, Gabriele Bavota:
On the Generalizability of Deep Learning-based Code Completion Across Programming Language Versions. CoRR abs/2403.15149 (2024) - [i7]Matteo Ciniselli, Niccolò Puccinelli, Ketai Qiu, Luca Di Grazia:
From Today's Code to Tomorrow's Symphony: The AI Transformation of Developer's Routine by 2030. CoRR abs/2405.12731 (2024) - 2023
- [c4]Antonio Mastropaolo, Luca Pascarella, Emanuela Guglielmi, Matteo Ciniselli, Simone Scalabrino, Rocco Oliveto, Gabriele Bavota:
On the Robustness of Code Generation Techniques: An Empirical Study on GitHub Copilot. ICSE 2023: 2149-2160 - [c3]Matteo Ciniselli, Luca Pascarella, Emad Aghajani, Simone Scalabrino, Rocco Oliveto, Gabriele Bavota:
Source Code Recommender Systems: The Practitioners' Perspective. ICSE 2023: 2161-2172 - [i6]Antonio Mastropaolo, Luca Pascarella, Emanuela Guglielmi, Matteo Ciniselli, Simone Scalabrino, Rocco Oliveto, Gabriele Bavota:
On the Robustness of Code Generation Techniques: An Empirical Study on GitHub Copilot. CoRR abs/2302.00438 (2023) - [i5]Matteo Ciniselli, Luca Pascarella, Emad Aghajani, Simone Scalabrino, Rocco Oliveto, Gabriele Bavota:
Source Code Recommender Systems: The Practitioners' Perspective. CoRR abs/2302.04098 (2023) - [i4]Antonio Mastropaolo, Matteo Ciniselli, Massimiliano Di Penta, Gabriele Bavota:
Evaluating Code Summarization Techniques: A New Metric and an Empirical Characterization. CoRR abs/2312.15475 (2023) - 2022
- [j1]Matteo Ciniselli, Nathan Cooper, Luca Pascarella, Antonio Mastropaolo, Emad Aghajani, Denys Poshyvanyk, Massimiliano Di Penta, Gabriele Bavota:
An Empirical Study on the Usage of Transformer Models for Code Completion. IEEE Trans. Software Eng. 48(12): 4818-4837 (2022) - [c2]Matteo Ciniselli, Luca Pascarella, Gabriele Bavota:
To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set? MSR 2022: 167-178 - [d3]Matteo Ciniselli, Nathan Cooper, Luca Pascarella, Antonio Mastropaolo, Emad Aghajani, Denys Poshyvanyk, Massimiliano Di Penta, Gabriele Bavota:
Replication package for "An Empirical Study on the Usage of Transformer Models for Code Completion". Zenodo, 2022
- [d2]Matteo Ciniselli, Nathan Cooper, Luca Pascarella, Denys Poshyvanyk, Massimiliano Di Penta, Gabriele Bavota:
Replication package for "An Empirical Study on the Usage of BERT Models for Code Completion". Zenodo, 2022 - [d1]Matteo Ciniselli, Luca Pascarella, Gabriele Bavota:
Replication package for "To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set? Zenodo, 2022 - [i3]Matteo Ciniselli, Luca Pascarella, Gabriele Bavota:
To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set? CoRR abs/2204.06894 (2022) - 2021
- [c1]Matteo Ciniselli, Nathan Cooper, Luca Pascarella, Denys Poshyvanyk, Massimiliano Di Penta, Gabriele Bavota:
An Empirical Study on the Usage of BERT Models for Code Completion. MSR 2021: 108-119 - [i2]Matteo Ciniselli, Nathan Cooper, Luca Pascarella, Denys Poshyvanyk, Massimiliano Di Penta, Gabriele Bavota:
An Empirical Study on the Usage of BERT Models for Code Completion. CoRR abs/2103.07115 (2021) - [i1]Matteo Ciniselli, Nathan Cooper, Luca Pascarella, Antonio Mastropaolo, Emad Aghajani, Denys Poshyvanyk, Massimiliano Di Penta, Gabriele Bavota:
An Empirical Study on the Usage of Transformer Models for Code Completion. CoRR abs/2108.01585 (2021)
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