Google Boosts Developer Productivity with Hybrid Semantic ML Code Completion
Google researchers have developed a novel Transformer-based hybrid semantic machine learning code completion system that combines machine learning (ML) and rule-based semantic engines (SEs) to significantly improve developer productivity. The system integrates ML and SEs in three ways: 1) re-ranking SE's single-token suggestions using ML; 2) applying single and multi-line completions using ML and checking correctness with the SE; and 3) using single and multi-line continuation by ML of single-token semantic suggestions. A three-month study with 10,000+ Google internal developers showed a 6% reduction in coding iteration time with single-line ML completion. Currently, over 3% of new code is generated from accepting ML completion suggestions. The system supports eight programming languages and incorporates semantic checks to ensure code correctness, significantly boosting developer trust and efficiency.
