Improvement of Source Code Conversion for Code Completion
M.J. Turk (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Maliheh Izadi (TU Delft - Software Engineering)
Arie van Deursen (TU Delft - Software Technology)
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The code produced during the research
https://github.com/mikaturk/codefill-conversion-improvementOther than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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%.