Improvement of Source Code Conversion for Code Completion

Bachelor thesis (2022)

Authors

M.J. Turk Electrical Engineering, Mathematics and Computer Science

Contributors

M. Izadi Software Engineering - EEMCS (mentor)
A. van Deursen Software Technology (mentor)
A. Lukina Algorithmics - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Mika Turk
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Published Date

24-06-2022

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Code Completion is advancing constantly, with new research coming out all the time. One such advancement is CodeFill, which converts source files into token sequences for type prediction. To train the CodeFill model, a lot of source files are needed which take a long time to convert before training can begin. Converting the file the end-user is working on for completions is also essential for the total latency as longer files can affect the experience of using the model. In this study we aimed to improve the performance and success rate of this conversion. Our results indicate that we increased both the performance by 83 times or more depending on the input file length and the success rate by up to 45%.