@inbook{10.1145/3411763.3451679,
author = {Weber, Thomas and Winiker, Christina and Hussmann, Heinrich},
title = {A Closer Look at Machine Learning Code},
year = {2021},
isbn = {9781450380959},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3411763.3451679},
abstract = { Software using Machine Learning algorithms is becoming ever more ubiquitous making it equally important to have good development processes and practices. Whether we can apply insights from software development research remains open though, since it is not yet clear, whether data-driven development has the same requirements as its traditional counterpart. We used eye tracking to investigate whether the code reading behaviour of developers differs between code that uses Machine Learning and code that does not. Our data shows that there are differences in what parts of the code people consider of interest and how they read it. This is a consequence of differences in both syntax and semantics of the code. This reading behaviour already shows that we cannot take existing solutions as universally applicable. In the future, methods that support Machine Learning must iterate on existing knowledge to meet the challenges of data-driven development.},
booktitle = {Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems},
articleno = {338},
numpages = {6}
}