Matteo Ciniselli

About Matteo Ciniselli

Matteo Ciniselli, With an exceptional h-index of 4 and a recent h-index of 4 (since 2020), a distinguished researcher at Università della Svizzera Italiana, specializes in the field of Software Engineering, Deep Learning, Natural Language Processing.

His recent articles reflect a diverse array of research interests and contributions to the field:

Evaluating Code Summarization Techniques: A New Metric and an Empirical Characterization

On the Generalizability of Deep Learning-based Code Completion Across Programming Language Versions

Towards Summarizing Code Snippets Using Pre-Trained Transformers

Code Review Automation: Strengths and Weaknesses of the State of the Art

Source code recommender systems: The practitioners' perspective

On the robustness of code generation techniques: An empirical study on github copilot

Studying strengths and weaknesses of code recommenders

To what extent do deep learning-based code recommenders generate predictions by cloning code from the training set?

Matteo Ciniselli Information

University

Position

PhD Student at

Citations(all)

151

Citations(since 2020)

151

Cited By

0

hIndex(all)

4

hIndex(since 2020)

4

i10Index(all)

4

i10Index(since 2020)

4

Email

University Profile Page

Google Scholar

Matteo Ciniselli Skills & Research Interests

Software Engineering

Deep Learning

Natural Language Processing

Top articles of Matteo Ciniselli

Evaluating Code Summarization Techniques: A New Metric and an Empirical Characterization

2024/4/12

On the Generalizability of Deep Learning-based Code Completion Across Programming Language Versions

arXiv preprint arXiv:2403.15149

2024/3/22

Towards Summarizing Code Snippets Using Pre-Trained Transformers

arXiv preprint arXiv:2402.00519

2024/2/1

Code Review Automation: Strengths and Weaknesses of the State of the Art

2024/1/1

Source code recommender systems: The practitioners' perspective

2023/5/14

On the robustness of code generation techniques: An empirical study on github copilot

2023/5/14

Studying strengths and weaknesses of code recommenders

2023

Matteo Ciniselli
Matteo Ciniselli

H-Index: 0

To what extent do deep learning-based code recommenders generate predictions by cloning code from the training set?

2022/5/23

An empirical study on the usage of transformer models for code completion

IEEE Transactions on Software Engineering

2021/11/16

An empirical study on the usage of bert models for code completion

2021/5/17

See List of Professors in Matteo Ciniselli University(Università della Svizzera Italiana)