An Empirical Study on the Usage of BERT Models for Code Completion. - Matteo Ciniselli; Nathan Cooper; Luca Pascarella; Denys Poshyvanyk; Massimiliano Di Penta; Gabriele Bavota (MSR, 2021)

To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set? - Matteo Ciniselli; Luca Pascarella; Gabriele Bavota (MSR, 2022)

An Empirical Study on the Usage of Transformer Models for Code Completion. - Matteo Ciniselli; Nathan Cooper; Luca Pascarella; Antonio Mastropaolo; Emad Aghajani; Denys Poshyvanyk; Massimiliano Di Penta; Gabriele Bavota (IEEE Trans. Software Eng., 2022)

Replication package for "An Empirical Study on the Usage of Transformer Models for Code Completion". - Matteo Ciniselli; Nathan Cooper; Luca Pascarella; Antonio Mastropaolo; Emad Aghajani; Denys Poshyvanyk; Massimiliano Di Penta; Gabriele Bavota (Zenodo, 2022)

Replication package for "An Empirical Study on the Usage of BERT Models for Code Completion". - Matteo Ciniselli; Nathan Cooper; Luca Pascarella; Denys Poshyvanyk; Massimiliano Di Penta; Gabriele Bavota (Zenodo, 2022)

Replication package for "To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set? - Matteo Ciniselli; Luca Pascarella; Gabriele Bavota (Zenodo, 2022)

Source Code Recommender Systems: The Practitioners' Perspective. - Matteo Ciniselli; Luca Pascarella; Emad Aghajani; Simone Scalabrino; Rocco Oliveto; Gabriele Bavota (ICSE, 2023)

On the Robustness of Code Generation Techniques: An Empirical Study on GitHub Copilot. - Antonio Mastropaolo; Luca Pascarella; Emanuela Guglielmi; Matteo Ciniselli; Simone Scalabrino; Rocco Oliveto; Gabriele Bavota (ICSE, 2023)

Evaluating Code Summarization Techniques: A New Metric and an Empirical Characterization. - Antonio Mastropaolo; Matteo Ciniselli; Massimiliano Di Penta; Gabriele Bavota (ICSE, 2024)

Deep Learning-Based Code Completion: On the Impact on Performance of Contextual Information. - Matteo Ciniselli; Luca Pascarella; Gabriele Bavota (ICSME, 2024)

On the Generalizability of Deep Learning-based Code Completion Across Programming Language Versions. - Matteo Ciniselli; Alberto Martin-Lopez; Gabriele Bavota (ICPC, 2024)

Towards Summarizing Code Snippets Using Pre-Trained Transformers. - Antonio Mastropaolo; Matteo Ciniselli; Luca Pascarella; Rosalia Tufano; Emad Aghajani; Gabriele Bavota (ICPC, 2024)

Code Review Automation: Strengths and Weaknesses of the State of the Art. - Rosalia Tufano; Ozren Dabic; Antonio Mastropaolo; Matteo Ciniselli; Gabriele Bavota (IEEE Trans. Software Eng., 2024)

The Trailer of the ACM 2030 Roadmap for Software Engineering. - Mauro Pezzè; Matteo Ciniselli; Luca Di Grazia; Niccolò Puccinelli; Ketai Qiu (ACM SIGSOFT Softw. Eng. Notes, 2024)

From Today's Code to Tomorrow's Symphony: The AI Transformation of Developer's Routine by 2030. - Ketai Qiu; Niccolò Puccinelli; Matteo Ciniselli; Luca Di Grazia (ACM Trans. Softw. Eng. Methodol., 2025)