Machine Learning for Translating Pseudocode to Python: A Comprehensive Review

Translating pseudocode to a programming language can be difficult and time-consuming, especially for individuals unfamiliar with the languages involved. However, recent advances in Natural language processing (NLP) have made it possible to create a model that can automatically convert pseudocode written in natural language to code in a specific programming language. This research study proposes a solution to this problem by using an NLP transformer model to translate pseudocode into Python code automatically. The model will be trained on pseudocode examples and corresponding Python code translations. This study leverages a large dataset of pseudocode examples and their corresponding Python code translations to achieve this. By training the model on this dataset, it will be able to learn the patterns and structures present in the pseudocode and match them with their corresponding Python syntax. This will allow the model to translate pseudocode accurately into Python code, even for complex algorithms and structures.