A Two Phase Smart Code Editor - Kalyon, Melike Serra; Akgül, Yusuf Sinan (Institute of Electrical and Electronics Engineers Inc., 2021)
Intelligent code completion has become an area of interest to accelerate software development. In this study, novel methods for code completion are proposed and the performance of these methods on code completion is demonstrated. Deep Learning models are applied over large datasets to achieve code completion in dynamically written programming languages. In this study, an LSTM model that performs next word prediction is trained on the JavaScript programming language. The code completion system consists of two phases. In the first phase, multiple completion suggestions are generated from the LSTM model. In the second phase, these suggestions are checked by the JavaScript Parser and the best results are achieved. This system is an alternative solution where the model can be trained without preprocessing the training set with a Parser. The proposed method has been tested on the test dataset and the success of the code completion is shown in a quantitative way. © 2021 Elsevier B.V., All rights reserved.
3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2021 -- Ankara -- 171163
Keywords: Code completion; Code editors.; Deep Learning; LSTM; Parser