Logo des Repositoriums
 

Bidirectional Transformer Language Models for Smart Autocompletion of Source Code

dc.contributor.authorBinder, Felix
dc.contributor.authorVillmow, Johannes
dc.contributor.authorUlges, Adrian
dc.contributor.editorReussner, Ralf H.
dc.contributor.editorKoziolek, Anne
dc.contributor.editorHeinrich, Robert
dc.date.accessioned2021-01-27T13:34:30Z
dc.date.available2021-01-27T13:34:30Z
dc.date.issued2021
dc.description.abstractThis paper investigates the use of transformer networks – which have recently become ubiquitous in natural language processing – for smart autocompletion on source code. Our model JavaBERT is based on a RoBERTa network, which we pretrain on 250 million lines of code and then adapt for method ranking, i.e. ranking an object's methods based on the code context. We suggest two alternative approaches, namely unsupervised probabilistic reasoning and supervised fine-tuning. The supervised variant proves more accurate, with a top-3 accuracy of up to 98%. We also show that the model – though trained on method calls' full contexts – is quite robust with respect to reducing context.en
dc.identifier.doi10.18420/inf2020_83
dc.identifier.isbn978-3-88579-701-2
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/34796
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2020
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-307
dc.subjectsmart autocompletion
dc.subjectdeep learning
dc.subjecttransformer networks
dc.titleBidirectional Transformer Language Models for Smart Autocompletion of Source Codeen
gi.citation.endPage922
gi.citation.startPage915
gi.conference.date28. September - 2. Oktober 2020
gi.conference.locationKarlsruhe
gi.conference.sessiontitle3rd Workshop on Smart Systems for Better Living Environments

Dateien

Originalbündel
1 - 1 von 1
Lade...
Vorschaubild
Name:
C20-6.pdf
Größe:
291.24 KB
Format:
Adobe Portable Document Format