Development of a Query Language for Improved Versioning Support for Machine- Learning-Based Systems

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/305123
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Type: Examensarbete för masterexamen
Title: Development of a Query Language for Improved Versioning Support for Machine- Learning-Based Systems
Authors: Tran, Erik
Abstract: This thesis is about development of a query language for improved versioning support for machine-learning-based systems, focusing on the perspective of software engineers. The motivation is to combine the worlds of software engineers and data scientists as they have to work together effectively. Different existing tools that support management of machine learning assets are described and the reason why they are not fit for software engineers are explained. A design science approach method is applied for this thesis. Methods such as requirement elicitation, artifact feature elicitation and artifact feature prioritizations have been applied. Requirements were formed through independent research and are evaluated in four interviews. Features were implemented based on the requirements, and are evaluated as well in another four interviews. The artifact feature prioritization method includes construction of a traceability matrix. The final evaluation results indicated that the population who would hypothetically use this query language in its current state are users who are less experienced in management of machine learning assets. Discussions regarding future work such are related to scalability and data analysability are discussed in the report. The query language could expand to more advanced users if more advanced features are implemented e.g. features that supports data analysability or features that supports other models so that it is not restricted to only Scikit-learn classes that currently are the only classes that are able to be created by using the query language.
Keywords: software engineering;query language;machine learning;asset;versioning
Issue Date: 2022
Publisher: Chalmers tekniska högskola / Institutionen för data och informationsteknik
URI: https://hdl.handle.net/20.500.12380/305123
Collection:Examensarbeten för masterexamen // Master Theses



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