ML-Enhanced Code Completion Improves Developer Productivity

Posted by Maxim Tabachnyk, Staff Software Engineer and Stoyan Nikolov, Senior Engineering Manager, Google Research

The increasing complexity of code poses a key challenge to productivity in software engineering. Code completion has been an essential tool that has helped mitigate this complexity in integrated development environments (IDEs). Conventionally, code completion suggestions are implemented with rule-based semantic engines (SEs), which typically have access to the full repository and understand its semantic structure. Recent research has demonstrated that large language models (e.g., …

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