Name: Predicting Silent Behavioral Drift Severity: From Default-Argument Breaking Changes in Python Machine Learning Libraries (en Inglés)
SKU: 68944863
Price: 46.34 USD
Availability: InStock
Author: Ahmed Jobu, Rahat
ISBN: 9789999352161

portada Predicting Silent Behavioral Drift Severity: From Default-Argument Breaking Changes in Python Machine Learning Libraries (en Inglés)

Predicting Silent Behavioral Drift Severity: From Default-Argument Breaking Changes in Python Machine Learning Libraries (en Inglés)

Formato

Libro Físico

Editorial

Eliva Press

Autor

Ahmed Jobu, Rahat

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

ISBN13

9789999352161

Predicting Silent Behavioral Drift Severity: From Default-Argument Breaking Changes in Python Machine Learning Libraries (en Inglés)

Ahmed Jobu, Rahat (Autor) · Eliva Press · Tapa Blanda

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Reseña del libro "Predicting Silent Behavioral Drift Severity: From Default-Argument Breaking Changes in Python Machine Learning Libraries (en Inglés)"

Every practitioner who has maintained a machine learning pipeline has a story like this: a routine library upgrade, no errors, no warnings — and yet the model's output quietly changed. This is the story of default-argument breaking changes (DABCs): silent library updates that alter a function's behavior without ever raising an exception. This book introduces a differential-testing methodology that measures, for the first time, how severely these silent changes actually affect real output — not just whether client code happens to call the changed function. Applying it to a catalog of 88 documented breaking changes across scikit-learn and pandas, the author verified 13 directly, and used the results to train an explainable classifier that predicts severity for the rest with 97% cross-validated accuracy. Along the way, the book uncovers a precisely quantified limitation of differential testing itself: 85% of an eight-year breaking-change catalog can no longer be dynamically verified, due to the disappearance of compatible Python interpreters — a finding with implications far beyond this one study. Written for software-evolution researchers and working ML practitioners alike, this book pairs rigorous methodology with a practical, deployable tool: SilentDrift, which lets any developer check their exposure before the next dependency upgrade.