1 citations · 2 across the 4 of their papers we have counts for
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Designing Empirical Studies on LLM-Based Code Generation: Towards a Reference Framework
Nathalia Nascimento, Everton Guimaraes, Paulo Alencar
The rise of large language models (LLMs) has introduced transformative potential in automated code generation, addressing a wide range of software engineering challenges. However,…
Automated Non-Functional Requirements Generation in Software Engineering with Large Language Models: A Comparative Study
Jomar Thomas Almonte, Santhosh Anitha Boominathan, Nathalia Nascimento
Neglecting non-functional requirements (NFRs) early in software development can lead to critical challenges. Despite their importance, NFRs are often overlooked or difficult to ide…
LLM4DS: Evaluating Large Language Models for Data Science Code Generation
Nathalia Nascimento, Everton Guimaraes, Sai Sanjna Chintakunta +1
The adoption of Large Language Models (LLMs) for code generation in data science offers substantial potential for enhancing tasks such as data manipulation, statistical analysis, a…