Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
ANALYSIS OF TECHNICAL DEBT IN ML-BASED SOFTWARE DEVELOPMENT PROJECTS
Download
MasterThesis_PelinDayanAkman (METUOPEN).pdf
Date
2024-9-06
Author
Dayan Akman, Pelin
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
54
views
0
downloads
Cite This
Rapid development of Machine Learning (ML) algorithms and tools, and easier access to available frameworks and infrastructures have greatly fueled development of ML- based software solutions for real-world problems. Similar to traditional software development projects, ML-based projects have to deal with significant consequences of quick but sub-optimal solutions or shortcuts taken in the development process. Effects of these intentional or unintentional poor decisions are known as technical debt (TD). Due to structural differences of ML projects compared to traditional software projects, the TD phenomenon needs to be revisited. In this thesis, TD is defined specifically in the context of ML-based projects, and distinct categories of TD relevant to these projects are identified. The assessment of TD were examined based on the data collected through interviews from 18 industry professionals in the fields of Data Science and ML. These interviews were analyzed by using thematic analysis to identify the root causes, impacts, band-aid solutions and mitigation strategies related to TD. The findings of the study were reviewed by academic experts in multiple iterations. The study, in addition to identifying TD categories specific to ML projects such as data, model, infrastructure and deployment, also identified traditional software project-specific TD categories such as code, system design, and team, resource, and knowledge management. This research provides a detailed understanding of TD phenomenon in ML projects and offers practical recommendations for its management. This study contributes to the field by highlighting the unique nature of TD in ML context and proposes a TD-oriented structure for its assessment.
Subject Keywords
Machine Learning
,
Technical Debt
,
Machine Learning Development Lifecycle
URI
https://hdl.handle.net/11511/111423
Collections
Graduate School of Informatics, Thesis
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
P. Dayan Akman, “ANALYSIS OF TECHNICAL DEBT IN ML-BASED SOFTWARE DEVELOPMENT PROJECTS,” M.S. - Master of Science, Middle East Technical University, 2024.