Code Preparation for Machine Learning

Jenni, Raphael (2022) Code Preparation for Machine Learning. Masters thesis, OST Ostschweizer Fachhochschule.

[thumbnail of Raphael Jenni_42100_assignsubmission_file_code-preparation-for-machine-learning-m4-v01-3db80339.pdf] Text
Raphael Jenni_42100_assignsubmission_file_code-preparation-for-machine-learning-m4-v01-3db80339.pdf - Supplemental Material

Download (1MB)


Using machine learning for images, text, or audio has become
popular and relatively mainstream. On the other hand, using
machine learning for code is a rather new field. Only a few
commercial products are available, and the research is still in its early stages. When trying to join this field, many different topics need to be explored.
This paper aims to bring a software engineer or a programming language researcher up to speed on the current state of machine learning and show the possibilities of such technologies in respect to code. It covers code2vec, code2seq, CuBERT, CoCluBERT, CodeBERT, TreeBERT, and DeepBugs AST Context Representations, with their respective backgrounds and list tools and points out available further research. It also covers a few use cases and gives a practical example that leads through the whole paper.

Item Type: Thesis (Masters)
Subjects: Technologies > Programming Languages
Metatags > IFS (Institute for Software)
Divisions: Master of Advanced Studies in Software Engineering
Depositing User: Stud. I
Thesis advisor
Mehta, Farhad
Date Deposited: 05 Sep 2022 19:08
Last Modified: 05 Sep 2022 19:08

Actions (login required)

View Item
View Item