Neural Code Completion

Neural code completion uses deep learning models, such as LSTMs and large language models, to predict and generate code snippets, aiming to boost programmer productivity. Current research focuses on improving efficiency (e.g., through dynamic inference to reduce computational costs) and addressing ethical concerns (e.g., membership inference attacks to detect unauthorized use of training data and watermarking techniques to protect code datasets). These advancements are crucial for responsible development and wider adoption of code completion tools, impacting software development workflows and the broader software engineering field.