Abstract: A neural transcompilation model is tested with a set of syntax unit tests to determine the syntax elements of a source code program written in a source programming language that fail to translate properly into a target programming language. The syntax elements having a translation defect is identified and ranked according a translation failure rate. The neural transcompilation model is then fine-tuned with training samples of the syntax elements having the highest translation failure rates and their paired correct translation in order to teach the model to learn the association between the syntax elements of a source programming language causing translation defects and its correct translation in a target programming language.

Abstract: A cloud platform includes several web services that facilitate the automated tuning and deployment of pre-trained deep learning models configured for software engineering tasks. The automated tuning and deployment allow a developer to fine-tune a pre-existing model without having access to the parameters of the pre-existing and the fine-tuned model in a manner that does not require user management input. The cloud platform provides a set of files for each pre-trained models used to automatically build a fine-tuning infrastructure to fine-tune a model and a deployment infrastructure that deploys the fine-tuned model without requiring user input.