Conference Paper

Neural Machine Based Mobile Applications Code Translation

Hassan M.H.
Mahmoud O.A.
Mohammed O.I.
Baraka A.Y.
Mahmoud A.T.
Yousef A.H.

Although many cross platform mobile development software used a trans-compiler-based approach, it was very difficult to generalize it to work in both directions. For example, to convert between Java for Android Development and Swift for iOS development and vice versa. This is due to the need of writing a specific parser for each source language, and a specific code generator for each destination language. Neural network-based models are used successfully to translate between natural languages, including English, French, German any many others by providing enough datasets and without the need of adding language specific code for understanding and generation. In this paper, a source code converter based on the Neural Machine Translation Transformer Model that can translate from Java to Swift and vice versa is introduced. A synthesized dataset is used to train the model, the pipeline used for the translation as well as the code synthesis procedure throughout the work are illustrated. Initial results are promising and give motivation to further enhance the proposed tool. © 2020 IEEE.