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Articles by Di Penta
Computer Science > Machine Learning
The responsible development and deployment of artificial intelligence (AI) systems requires rigorous documentation of their constituent artifacts, e.g., datasets, model weights, training pipelines, and runtime dependencies. Although the Software Package Data Exchange (SPDX) 3.0 standard introduced native support for AI and dataset profiles, practical tooling capable of generating standards-compliant AI Bills of Materials (AIBoMs) in an automated and extensible manner remains scarce.
Computer Science > Software Engineering
Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias. While this behavior has been widely studied for general text generation, its impact on code generation quality and programming conventions remains largely unexplored.
Computer Science > Software Engineering
Software development is increasingly moving toward agentic-first workflows. This includes AI agents responsible for generating initial fixes for submitted issue reports. In this setting, issue reports are no longer merely documentation for human maintainers; they become the primary task specification for the agent.
Computer Science > Software Engineering
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Computer Science > Software Engineering
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Developers' Perspectives on Software Licensing: Current Practices, Challenges, and Tools
[Submitted on 1 Oct 2025 ( v1 ), last revised 23 Jan 2026 (this version, v2)] Title:Developers' Perspectives on Software Licensing: Current Practices, Challenges, and Tools View a PDF of the paper titled Developers' Perspectives on Software Licensing: Current Practices, Challenges, and Tools, by Nathan Wintersgill and 7 other authors View PDF HTML (experimental) Abstract:Most modern software products incorporate open-source components, requiring development teams to maintain compliance with...
Optimizing Datasets for Code Summarization: Is Code-Comment Coherence Enough?
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
[2308.16774] Toward Automatically Completing GitHub Workflows
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
[2307.09381] Is this Snippet Written by ChatGPT? An Empirical Study with a CodeBERT-Based Classifier
[Submitted on 18 Jul 2023 ( v1 ), last revised 7 Aug 2023 (this version, v2)] Title:Is this Snippet Written by ChatGPT? An Empirical Study with a CodeBERT-Based Classifier Download a PDF of the paper titled Is this Snippet Written by ChatGPT? An Empirical Study with a CodeBERT-Based Classifier, by Phuong T.
[2304.10409] Dealing with Popularity Bias in Recommender Systems for Third-party Libraries: How far Are We?
Recommender systems for software engineering (RSSEs) assist software engineers in dealing with a growing information overload when discerning alternative development solutions. While RSSEs are becoming more and more effective in suggesting handy recommendations, they tend to suffer from popularity bias, i.e., favoring items that are relevant mainly because several developers are using them.
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