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

By Rahat Ahmed Jobu

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.

• Format: Paperback
• Language: English
• ISBN: 9999352162
• ISBN13: 9789999352161
• Release Date: August 2026
• Publisher: Eliva Press
• Length: 42 Pages
• Weight: 0.16 lbs.
• Dimensions: 9.0" x 0.1" x 6.0"