Jenni, Raphael (2022) Machine Learning for Programming Languages. Masters thesis, OST Ostschweizer Fachhochschule.
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Abstract
Artificial Intelligence, or more precisely deep learning, has become a trending topic in the broad public and software engineering circles. Some exciting technologies have arisen from it, such as voice assistants or language translation services. Also, programmatically understanding source code and supporting the developer in writing better code have been a topic for a while. In recent times, a push toward combining these two fields has been made.
For a software engineer coming from the world of tackling a
problem with the help of algorithms with a predictable outcome, deep learning can be rather challenging to grasp. This paper aims to bring a software engineer or a programming language researcher up to speed on the current state of deep learning and show the possibilities of such technologies. All this, in an easily digestible manner for someone without any profound knowledge about deep learning.
Item Type: | Thesis (Masters) |
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Subjects: | Metatags > IFS (Institute for Software) |
Divisions: | Master of Advanced Studies in Software Engineering |
Depositing User: | Stud. I |
Contributors: | Contribution Name Email Thesis advisor Mehta, Farhad UNSPECIFIED |
Date Deposited: | 19 Sep 2022 07:39 |
Last Modified: | 19 Sep 2022 07:39 |
URI: | https://eprints.ost.ch/id/eprint/1068 |