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Laura A. Heymann

dblp:372/4961 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
0000-0001-9258-2105ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Empirical software engineering · 87% Software maintenance and evolution · 13%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Empirical software engineering
developer studies
1.012026
Developer Perspectives on Licensing and Copyright Issues Arising from Generative AI for Software Development · ACM Trans. Softw. Eng. Methodol. 2026
Empirical software engineering › practitioner studies
practitioner survey
1.012026
Developer Perspectives on Licensing and Copyright Issues Arising from Generative AI for Software Development · ACM Trans. Softw. Eng. Methodol. 2026

Methods — techniques the papers use, named apart from their topics

survey · 1.0interviews · 1.0
YearPublicationVenuePosition
2026 Developer Perspectives on Licensing and Copyright Issues Arising from Generative AI for Software Development
abstract
Despite the utility that Generative AI (GenAI) tools provide for tasks such as writing code, the use of these tools raises important legal questions and potential risks, particularly those associated with copyright law. As lawmakers and regulators respond to these questions, the views of users can offer relevant perspectives. In this article, we provide: (1) a survey of 574 developers on the licensing and copyright aspects of GenAI for coding, as well as follow-up interviews; (2) a snapshot of developers’ views at a time when GenAI and perceptions of it were rapidly evolving; and (3) an analysis of developers’ perspectives, yielding insights and recommendations that can inform future regulatory decisions in this evolving field. Our results show the benefits developers derive from GenAI, how they view the use of AI-generated code as similar to using other existing code, the varied opinions they have on who should own or be compensated for such code, that they are concerned about data leakage via GenAI, and other findings, providing organizations and policymakers with valuable insights into how the technology is being used and the concerns that stakeholders believe warrant attention.
Trevor Stalnaker, Nathan Wintersgill, Oscar Chaparro, Laura A. Heymann, Massimiliano Di Penta, Daniel M. Germán, Denys Poshyvanyk
ACM Trans. Softw. Eng. Methodol.4