Enhancing Model Robustness Using Differential Debugging in Jupyter Notebooks: A Case Study with Noisy Breast Cancer Data

Differential debugging is a powerful technique used to identify and resolve discrepancies in software behavior by comparing multiple execution traces. Jupyter Notebook, an open-source web application, has gained prominence in various fields for its interactive and user-friendly interface. Its interactive nature and ability to combine code, visualizations, and narrative text have made it a preferred tool for exploratory data analysis, machine learning, and research. This paper explores the potential of Jupyter Notebook as a platform for differential debugging for comparing the behavior of a program under different inputs or conditions to identify errors. We discuss the inherent advantages of Jupyter Notebook for differential debugging, including its interactive environment, rich visualization capabilities, and support for multiple programming languages.