Source Code Auto Completion with different approaches: Text Classification, N-Gram Models, Long Short-Term Memory

dc.contributor.advisor	Goodwin, Morten
dc.contributor.author	Ibrahim, Pavel
dc.date.accessioned	2022-09-21T16:24:40Z
dc.date.available	2022-09-21T16:24:40Z
dc.date.issued	2022
dc.identifier	no.uia:inspera:106884834:10071482
dc.identifier.uri	https://hdl.handle.net/11250/3020374
dc.description	Full text not available
dc.description.abstract	Et gjennomgang av 2 ulike Source Code Auto Completion metoder med Text Classification og N-Gram Modeller. I tillegg et forsøk på implementasjon av Long Short-Term Memory for Auto Completion med Python.
dc.description.abstract
dc.language
dc.publisher	University of Agder
dc.title	Source Code Auto Completion with different approaches: Text Classification, N-Gram Models, Long Short-Term Memory
dc.type	Master thesis

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