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}
}