Do Code Quality and Style Issues Differ Across (Non-)Machine Learning Notebooks? Yes!
Bibliographic record
Abstract
The popularity of computational notebooks is rapidly increasing because of their interactive code-output visualization 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 opensource 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 significantly 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.188 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".