Improvement of Source Code Conversion for Code Completion

Improvement of Source Code Conversion for Code Completion

Turk, Mika (TU Delft Electrical Engineering, Mathematics and Computer Science)

Izadi, M. (mentor)
van Deursen, A. (mentor)
Lukina, A. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2022-06-24

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%.

code completion
performance improvement
python
JavaScript

http://resolver.tudelft.nl/uuid:9acaa0c3-ba8d-443a-8d06-c296f65c6895

Student theses

bachelor thesis

© 2022 Mika Turk