A qualitative assessment of using ChatGPT as large language model for scientific workflow development.

ABSTRACT:

Background

Scientific workflow systems are increasingly popular for expressing and executing complex data analysis pipelines over large datasets, as they offer reproducibility, dependability, and scalability of analyses by automatic parallelization on large compute clusters. However, implementing workflows is difficult due to the involvement of many black-box tools and the deep infrastructure stack necessary for their execution. Simultaneously, user-supporting tools are rare, and the number of available examples is much lower than in classical programming languages.

Results

To address these challenges, we investigate the efficiency of large language models (LLMs), specifically ChatGPT, to support users when dealing with scientific workflows. We performed 3 user studies in 2

SUBMITTER: Sanger M 

PROVIDER: S-EPMC11186067 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature