Characterising Algorithm Debt in Machine and Deep Learning Systems
type: CreativeWork
description: The integration of Machine and Deep Learning (ML/DL) into modern software systems has transformed application domains such as finance, healthcare, and autonomous technologies. However, the complexity of ML/DL algorithms, the unique development pipeline, and the stochastic training process introduce challenges beyond those found in traditional software development, compromising the long-term reliability of ML/DL systems. These challenges, often due to design choices, lead to Technical Debt (TD). Algorithm Debt (AD) in ML/DL systems is a TD type caused by inefficient algorithmic decisions, leading to poor scalability and model degradation. Despite its impact, AD remains underexplored in ML/DL systems due to its novelty, with limited and fragmented studies, creating a knowledge gap. To address this gap, this thesis aims to characterise AD in ML/DL systems. It defines AD, investigates its causes, effects, and mitigation strategies, using a sequential mixed-methods approach. The first study, a Systematic Literature Review (SLR), synthesised evidence from 44 included studies published across IEEE, Springer, ACM, and ScienceDirect. This review produced an extended definition for AD in ML/DL systems and revealed that AD manifests in three ways: poor model scalability, inefficiency, and degradation. The SLR also identified nine ``AD smells'' (i.e., indicators) that lay the foundation for subsequent studies. Next, an experimental study was conducted to explore automated AD detection in code comments using ML/DL approaches. A labelled dataset of self-admitted TD from seven open-source repositories, identified through the SLR, was used to train and evaluate ML/DL classifiers. However, the results were constrained by the limited nature of the available dataset, which was labelled based on pre-existing, narrower definitions of AD. This underscored the inadequacy of current data for understanding AD and justified the subsequent need for comprehensive studies. Following the ML/DL e…
abstract: The integration of Machine and Deep Learning (ML/DL) into modern software systems has transformed application domains such as finance, healthcare, and autonomous technologies. However, the complexity of ML/DL algorithms, the unique development pipeline, and the stochastic training process introduce challenges beyond those found in traditional software development, compromising the long-term reliability of ML/DL systems. These challenges, often due to design choices, lead to Technical Debt (TD). Algorithm Debt (AD) in ML/DL systems is a TD type caused by inefficient algorithmic decisions, leading to poor scalability and model degradation. Despite its impact, AD remains underexplored in ML/DL systems due to its novelty, with limited and fragmented studies, creating a knowledge gap. To address this gap, this thesis aims to characterise AD in ML/DL systems. It defines AD, investigates its causes, effects, and mitigation strategies, using a sequential mixed-methods approach. The first study, a Systematic Literature Review (SLR), synthesised evidence from 44 included studies published across IEEE, Springer, ACM, and ScienceDirect. This review produced an extended definition for AD in ML/DL systems and revealed that AD manifests in three ways: poor model scalability, inefficiency, and degradation. The SLR also identified nine ``AD smells'' (i.e., indicators) that lay the foundation for subsequent studies. Next, an experimental study was conducted to explore automated AD detection in code comments using ML/DL approaches. A labelled dataset of self-admitted TD from seven open-source repositories, identified through the SLR, was used to train and evaluate ML/DL classifiers. However, the results were constrained by the limited nature of the available dataset, which was labelled based on pre-existing, narrower definitions of AD. This underscored the inadequacy of current data for understanding AD and justified the subsequent need for comprehensive studies. Following the ML/DL e…
author: Simon, Iko-Ojo
language: en_AU
date published: 2026
date modified: 2026-07-24T01:07:19Z
provider: Australian Catholic University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Australian Catholic University

PropertyValue
property ID: Handle
value: https://hdl.handle.net/1885/733806747