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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Denne innførselen finnes i følgende samling(er)
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Master's theses in Information and Communication Technology [505]
MM500, IKT590, IKT591