Design and Implementation of Code Completion System Based on LLM and CodeBERT Hybrid Subsystem

2025-09-09

AI Summary

Existing code completion tools suffer from limitations in semantic understanding and generative capability. To address this, we propose a hybrid code completion framework that synergistically integrates CodeBERT and the large language model GPT-3.5. Our method leverages CodeBERT for precise contextual semantic encoding and harnesses GPT-3.5’s strong, diverse code generation capacity, underpinned by a context-aware two-stage inference mechanism optimized for low-latency, high-accuracy real-time completion within IDEs. The key contributions include a tightly coupled architecture unifying semantic understanding and generation modules, and a lightweight hybrid scheduling strategy. Experimental results demonstrate significant improvements over CodeBERT, GPT-3.5, and state-of-the-art baselines across accuracy, generated code quality, and response latency. The system maintains robust performance across diverse deployment environments, establishing a scalable, efficient paradigm for intelligent programming assistants.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

In the rapidly evolving industry of software development, coding efficiency and accuracy play significant roles in delivering high-quality software. Various code suggestion and completion tools, such as CodeBERT from Microsoft and GPT-3.5 from OpenAI, have been developed using deep learning techniques and integrated into IDEs to assist software engineers' development. Research has shown that CodeBERT has outstanding performance in code summarization and capturing code semantics, while GPT-3.5 demonstrated its adept capability at code generation. This study focuses on implementing a hybrid model that integrates CodeBERT and GPT-3.5 models to accomplish code suggestion and autocomplete tasks, leveraging the context-aware effectiveness of CodeBERT and taking advantage of advanced code generation abilities of GPT-3.5. Evaluated in three main metrics: accuracy, quality of generated code and performance efficiency with various software and hardware, the hybrid model outperforms benchmarks, demonstrating its feasibility and effectiveness. Robustness testing further confirms the reliability and stability of the hybrid model. This study not only emphasizes the importance of deep learning in the software development industry, but also reveals the potential of synthesizing complementary deep learning models to fully exploit strengths of each model.

Problem

• Integrating CodeBERT and GPT-3.5 for code completion
• Leveraging context-awareness and code generation capabilities
• Improving accuracy, quality, and efficiency of suggestions

Innovation

• Hybrid model combining CodeBERT and GPT-3.5
• Leverages CodeBERT's context-aware effectiveness
• Utilizes GPT-3.5's advanced code generation