@inproceedings{SiddikSCAM2023,

title = {Do Code Quality and Style Issues Differ Across (Non-)Machine Learning Notebooks? Yes!},

author = {Md Saeed Siddik and Cor-Paul Bezemer},

year  = {2023},

date = {2023-10-03},

urldate = {2023-10-03},

booktitle = {23nd IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM)},

pages = {1--12},

publisher = {IEEE},

abstract = {The popularity of computational notebooks is

rapidly increasing because of their interactive code-output vi-

sualization and on-demand non-sequential code block execution.

These notebook features have made notebooks especially popular

with machine learning developers and data scientists. However,

as prior work shows, notebooks generally contain low quality

code. In this paper, we investigate whether the low quality code

is inherent to the programming style in notebooks, or whether

it is correlated with the use of machine learning techniques.

We present a large-scale empirical analysis of 246,599 open-

source notebooks to explore how machine learning code quality

in Jupyter Notebooks differs from non-machine learning code,

thereby focusing on code style issues. We explored code style

issues across the Error, Convention, Warning, and Refactoring

categories. We found that machine learning notebooks are of

lower quality regarding PEP-8 code standards than non-machine

learning notebooks, and their code quality distributions signifi-

cantly differ with a small effect size. We identified several code

style issues with large differences in occurrences between machine

learning and non-machine learning notebooks. For example,

package and import-related issues are more prevalent in machine

learning notebooks. Our study shows that code quality and code

style issues differ significantly across machine learning and non-

machine learning notebooks.},

keywords = {Computational notebooks, Empirical software engineering, Mining software repositories},

pubstate = {published},

tppubtype = {inproceedings}

}