NB2P: Generating Data Science Pipelines from Computational Notebooks
Open Access
- 12 April 2026
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 932-943
Abstract
No abstract availableThis publication has 27 references indexed in Scilit:
- Retrieval-Based Prompt Selection for Code-Related Few-Shot Learning2023
- Visualising data science workflows to support third-party notebook comprehension: an empirical studyEmpirical Software Engineering, 2023
- Workflow analysis of data science code in public GitHub repositoriesEmpirical Software Engineering, 2022
- A Static Analysis Framework for Data Science Notebooks2022
- ReSplit: Improving the Structure of Jupyter Notebooks by Re-Splitting Their Cells2022
- VizSmith: Automated Visualization Synthesis by Mining Data-Science Notebooks2021
- Restoring the Executability of Jupyter Notebooks by Automatic Upgrade of Deprecated APIs2021
- Restoring Execution Environments of Jupyter Notebooks2021
- An Empirical Study of Refactorings and Technical Debt in Machine Learning Systems2021
- Framewise phoneme classification with bidirectional LSTM and other neural network architecturesNeural Networks, 2005