Antonio Piccinno
Biographic Data
| ID | 9669058 |
|---|---|
| NAME | Antonio Piccinno |
| GIVEN NAMES | Antonio |
| FAMILY NAME | Piccinno |
| SIGNATURE | PICCINNO A |
| AFFILIATIONS | University of Bari Aldo Moro |
| ORCID | 0000-0003-1561-7073 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Understanding user mental models in AI-driven code completion tools
Integrated Development Environments increasingly implement AI-powered code completion tools (CCTs), which promise to enhance developer efficiency, accuracy, and productivity. However, interaction challenges with CCTs persist, mainly due to mismatches between developers’ mental models and the unpredictable behavior of AI-generated suggestions, which is an aspect underexplored in the literature. We conducted an elicitation study with 56 developers …
Exploring the implementation of sustainable development goals
This study explores the extent of the United Nations Sustainability Development Goals (UN SDGs) implementations by listed companies in Indonesia. It also evaluates whether the SDG practices differ between state-owned enterprises (SOEs) and private companies listed in the Indonesia Stock Exchange (IDX). The main contributions of this paper are twofold: (1) it compares SGD reports and disclosures of listed SOEs vis-à-vis listed private companies in…
No prominent works on this page.
Exploring the implementation of sustainable development goals
This study explores the extent of the United Nations Sustainability Development Goals (UN SDGs) implementations by listed companies in Indonesia. It also evaluates whether the SDG practices differ between state-owned enterprises (SOEs) and private companies listed in the Indonesia Stock Exchange (IDX). The main contributions of this paper are twofold: (1) it compares SGD reports and disclosures of listed SOEs vis-à-vis listed private companies in…
Understanding user mental models in AI-driven code completion tools
Integrated Development Environments increasingly implement AI-powered code completion tools (CCTs), which promise to enhance developer efficiency, accuracy, and productivity. However, interaction challenges with CCTs persist, mainly due to mismatches between developers’ mental models and the unpredictable behavior of AI-generated suggestions, which is an aspect underexplored in the literature. We conducted an elicitation study with 56 developers …
Computer Science (2 works) · Cognitive science (1 work) · Data science (1 work) · Human–computer interaction (1 work) · Mental model (1 work) · Online Learning and Analytics (1 work) · Programming language (1 work) · Psychology (1 work) · Semantic Web and Ontologies (1 work)