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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.13656 |
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| _version_ | 1866911158306340864 |
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| author | Yao, Yingao Elaine Nimje, Vedant Viswanath, Varun Dutta, Saikat |
| author_facet | Yao, Yingao Elaine Nimje, Vedant Viswanath, Varun Dutta, Saikat |
| contents | Notebooks have become the de-facto choice for data scientists and machine learning engineers for prototyping and experimenting with machine learning (ML) pipelines. Notebooks provide an interactive interface for code, data, and visualization. However, notebooks provide very limited support for testing. Thus, during continuous development, many subtle bugs that do not lead to crashes often go unnoticed and cause silent errors that manifest as performance regressions.
To address this, we introduce NBTest - the first regression testing framework that allows developers to write cell-level assertions in notebooks and run such notebooks in pytest or in continuous integration (CI) pipelines. NBTest offers a library of assertion APIs, and a JupyterLab plugin that enables executing assertions. We also develop the first automated approach for generating cell-level assertions for key components in ML notebooks, such as data processing, model building, and model evaluation. NBTest aims to improve the reliability and maintainability of ML notebooks without adding developer burden.
We evaluate NBTest on 592 Kaggle notebooks. Overall, NBTest generates 21163 assertions (35.75 on average per notebook). The generated assertions obtain a mutation score of 0.57 in killing ML-specific mutations. NBTest can catch regression bugs in previous versions of the Kaggle notebooks using assertions generated for the latest versions. Because ML pipelines involve non deterministic computations, the assertions can be flaky. Hence, we also show how NBTest leverages statistical techniques to minimize flakiness while retaining high fault-detection effectiveness. NBTest has been adopted in the CI of a popular ML library. Further, we perform a user study with 17 participants that shows that notebook users find NBTest intuitive (Rating 4.3/5) and useful in writing assertions and testing notebooks (Rating 4.24/5). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13656 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Regression Testing Framework with Automated Assertion Generation for Machine Learning Notebooks Yao, Yingao Elaine Nimje, Vedant Viswanath, Varun Dutta, Saikat Software Engineering Notebooks have become the de-facto choice for data scientists and machine learning engineers for prototyping and experimenting with machine learning (ML) pipelines. Notebooks provide an interactive interface for code, data, and visualization. However, notebooks provide very limited support for testing. Thus, during continuous development, many subtle bugs that do not lead to crashes often go unnoticed and cause silent errors that manifest as performance regressions. To address this, we introduce NBTest - the first regression testing framework that allows developers to write cell-level assertions in notebooks and run such notebooks in pytest or in continuous integration (CI) pipelines. NBTest offers a library of assertion APIs, and a JupyterLab plugin that enables executing assertions. We also develop the first automated approach for generating cell-level assertions for key components in ML notebooks, such as data processing, model building, and model evaluation. NBTest aims to improve the reliability and maintainability of ML notebooks without adding developer burden. We evaluate NBTest on 592 Kaggle notebooks. Overall, NBTest generates 21163 assertions (35.75 on average per notebook). The generated assertions obtain a mutation score of 0.57 in killing ML-specific mutations. NBTest can catch regression bugs in previous versions of the Kaggle notebooks using assertions generated for the latest versions. Because ML pipelines involve non deterministic computations, the assertions can be flaky. Hence, we also show how NBTest leverages statistical techniques to minimize flakiness while retaining high fault-detection effectiveness. NBTest has been adopted in the CI of a popular ML library. Further, we perform a user study with 17 participants that shows that notebook users find NBTest intuitive (Rating 4.3/5) and useful in writing assertions and testing notebooks (Rating 4.24/5). |
| title | A Regression Testing Framework with Automated Assertion Generation for Machine Learning Notebooks |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2509.13656 |