Affiliation:
1. LMU Munich, Munich, Germany
Abstract
Computational notebooks like the Jupyter programming environment have been popular, particularly for developing data-driven applications. One of its main benefits is that it easily supports different programming languages with exchangeable kernels. Thus, it makes the user interface of computational notebooks broadly accessible. While their literate programming paradigm has advantages, we can use this infrastructure to make other paradigms similarly easily and broadly accessible to developers. In our work, we demonstrate how the Jupyter infrastructure can be utilized with different interfaces for different programming paradigms, enabling even greater flexibility for programmers and making it easier for them to adopt different paradigms when they are most suitable. We present a prototype that adds graphical programming and a multi-paradigm editor on top of the Jupyter system. The multi-paradigm editor seamlessly combines the added graphical programming with the familiar notebook interface side-by-side, which can further help developers switch between programming paradigms when desired. A subsequent user evaluation demonstrates the benefits not only of alternate interfaces and paradigms but also of the flexibility of seamlessly switching between them. Finally, we discuss some of the challenges in implementing these systems and how these can enhance the software development process in the future.
Publisher
Association for Computing Machinery (ACM)
Reference53 articles.
1. Martín Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dandelion Mané Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Viégas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https://www.tensorflow.org/
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