Problem
Research questions and friction points this paper is trying to address.
Algorithm Debt
Technical Debt
Machine Learning
Deep Learning
Software Maintenance
A Survey of Algorithm Debt in Machine and Deep Learning Systems: Definition, Smells, and Future Work
🤖 AI Summary
This study addresses the performance degradation and limited scalability in machine learning and deep learning systems caused by algorithm debt. It presents the first systematic conceptualization of algorithm debt, drawing on a comprehensive literature review of 42 core studies. Through qualitative analysis and categorical synthesis, the work rigorously defines algorithm debt, delineates its key characteristics, and identifies representative “bad smells.” By bridging a critical gap in technical debt research at the algorithmic level, this paper establishes a theoretical foundation for detecting, identifying, and mitigating algorithm debt, while also outlining a roadmap for future research in this emerging area.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Algorithm Debt
Technical Debt
Machine Learning Maintenance
Deep Learning Systems
Code Smells
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