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
Download file(s):
File Description SizeFormat 
CSE 22-31 Tran.pdfMaster’s thesis in Computer science and engineering2.37 MBAdobe PDFView/Open
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTran, Erik-
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data och informationstekniksv
dc.date.accessioned2022-07-07T11:52:38Z-
dc.date.available2022-07-07T11:52:38Z-
dc.date.issued2022sv
dc.date.submitted2020-
dc.identifier.urihttps://hdl.handle.net/20.500.12380/305123-
dc.description.abstractThis 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.sv
dc.language.isoengsv
dc.setspec.uppsokTechnology-
dc.subjectsoftware engineeringsv
dc.subjectquery languagesv
dc.subjectmachine learningsv
dc.subjectassetsv
dc.subjectversioningsv
dc.titleDevelopment of a Query Language for Improved Versioning Support for Machine- Learning-Based Systemssv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH-
dc.contributor.examinerHorkoff, Daniel-
dc.contributor.supervisorStrüber, Daniel-
dc.identifier.coursecodeDATX05sv
Collection:Examensarbeten för masterexamen // Master Theses



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.