title: "Software Developers Learning Machine Learning: Motivations, Hurdles, and Desires" authors: Carrie J. Cai and Philip J. Guo venue: IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) year: 2019 footer: "Best Paper Award" links: - Blog post tweet: Large gaps exist between grand promises of machine learning and the code necessary to fulfill them abstract: > The growing popularity of machine learning (ML) has attracted more software developers to now want to adopt ML into their own practices, through tinkering with and learning from ML framework websites and online code examples. To investigate the motivations, hurdles, and desires of these software developers, we deployed a survey to the website of the TensorFlow.js ML framework. We found via 645 responses that many wanted to learn ML for aspirational reasons rather than for immediate job needs. Critically, developers faced hurdles due to a perceived lack of mathematical and theoretical background. They desired frameworks to provide more basic ML conceptual support, such as a curated corpus of best practices, conceptual tutorials, and a de-mystification of mathematical jargon into practical tips. These findings inform the design of ML frameworks and informal learning resources to broaden the base of people acquiring this increasingly important skill set. bibtex: > @inproceedings{CaiVLHCC2019, author = {Cai, Carrie J. and Guo, Philip J.}, title={Software Developers Learning Machine Learning: Motivations, Hurdles, and Desires}, booktitle = {Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)}, series = {VL/HCC '19}, year={2019}, month={Oct} }