A Survey of Algorithm Debt in Machine and Deep Learning Systems: Definition, Smells, and Future Work
This work addresses the issue of system reliability for developers and researchers in ML/DL, but it is incremental as it builds upon existing technical debt concepts.
The paper tackles the problem of maintenance challenges in machine and deep learning systems by defining and analyzing Algorithm Debt, a type of technical debt that affects performance and scalability, based on a review of 42 studies to identify its characteristics and future research directions.
The adoption of Machine and Deep Learning (ML/DL) technologies introduces maintenance challenges, leading to Technical Debt (TD). Algorithm Debt (AD) is a TD type that impacts the performance and scalability of ML/DL systems. A review of 42 primary studies expanded AD's definition, uncovered its implicit presence, identified its smells, and highlighted future directions. These findings will guide an AD-focused study, enhancing the reliability of ML/DL systems.