Applying software engineering and machine learning practices to manage machine learning complexity

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dc.contributor.authorHIllström, Sara
dc.contributor.authorMejborn, Johan
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data och informationstekniksv
dc.contributor.examinerStrüber, Daniel
dc.contributor.supervisorTorkar, Richard
dc.date.accessioned2022-07-07T11:22:57Z
dc.date.available2022-07-07T11:22:57Z
dc.date.issued2022sv
dc.date.submitted2020
dc.description.abstractToday, both software engineering (SE) and machine learning (ML) are two fairly well-established areas within engineering. The field of software engineering for machine learning (SE4ML) addresses the issue of applying software engineering practices for software containing ML. Complexity is a term with a widespread definition, and the way of handling and defining it is something that differs between traditional software engineering and machine learning. In this thesis, complexity is defined as the measure of the resources expended by another system in interacting with a piece of software. If the interacting system is another machine we define it as resource cost, and if the interacting system is instead people (tasks such as, e.g., debugging and testing) we define it as software complexity. This thesis was conducted in close collaboration with a partner company and aims to contribute to SE4ML by providing a framework aimed to act as guidance for how software complexity and resource cost may be addressed in different parts of the ML development process. The framework should also provide insights into possible trade-offs between software complexity and resource cost. To validate the framework, validation interviews with practitioners as well as representatives from academia were held, and the framework was also applied to an existing problem at the partner company. The latter was done by tweaking an existing ML model and developing two other models for comparison purposes. In conclusion, the validation interviews and the application to an existing ML model confirmed that the framework is useful for practitioners. There are trade-offs between some of the different activities that form the framework, referred to as artifacts. This means that practitioners, to some extent, need to balance contradicting artifacts to optimize the resource cost and software complexity trade-off, depending on the specific use case at hand.sv
dc.identifier.coursecodeDATX05sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/305118
dc.language.isoengsv
dc.setspec.uppsokTechnology
dc.subjectsoftware engineeringsv
dc.subjectmachine learningsv
dc.subjectcomplexitysv
dc.subjectframeworksv
dc.titleApplying software engineering and machine learning practices to manage machine learning complexitysv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH
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Master’s thesis in Computer science and engineering
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