Code Completion

Abstract # Background: Integrated Development Environments (IDEs) have become central to modern software development, offering features like code completion to boost developer productivity and efficiency. Code completion tools rely on predictive models to suggest relevant methods or functions as developers write code. While recent advances, such as the Pythia model, have leveraged natural language processing and recurrent neural networks (RNNs) with long short-term memory (LSTM) to improve prediction accuracy, these models tend to perform significantly better on Python’s standard libraries than on third-party libraries. This disparity arises because training datasets are typically dominated by standard library usage, leaving third-party libraries underrepresented and their code completion predictions less accurate.