A Survey of Algorithm Debt in Machine and Deep Learning Systems: Definition, Smells, and Future Work
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1866910110088953856 |
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| author | Simon, Emmanuel Iko-Ojo Hettiarachchi, Chirath Fard, Fatemeh Potanin, Alex Suominen, Hanna |
| author_facet | Simon, Emmanuel Iko-Ojo Hettiarachchi, Chirath Fard, Fatemeh Potanin, Alex Suominen, Hanna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_06363 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Survey of Algorithm Debt in Machine and Deep Learning Systems: Definition, Smells, and Future Work Simon, Emmanuel Iko-Ojo Hettiarachchi, Chirath Fard, Fatemeh Potanin, Alex Suominen, Hanna Software Engineering D.2 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. |
| title | A Survey of Algorithm Debt in Machine and Deep Learning Systems: Definition, Smells, and Future Work |
| topic | Software Engineering D.2 |
| url | https://arxiv.org/abs/2604.06363 |