# Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases

arXiv: https://arxiv.org/abs/2308.09313 · HTML: https://mlsanity.com/paper/2308.09313/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2023.

## Built on by (most cited first, 19 of 19)

- [Retrieval-Augmented Generation for AI-Generated Content: A Survey](https://mlsanity.com/paper/2402.19473/) (2024)
- [GraphCoder: Enhancing Repository-Level Code Completion via Code Context Graph-based Retrieval and Language Model](https://mlsanity.com/paper/2406.07003/) (2024)
- [Retrieval-Augmented Code Generation: A Survey with Focus on Repository-Level Approaches](https://mlsanity.com/paper/2510.04905/) (2025)
- [What to Retrieve for Effective Retrieval-Augmented Code Generation? An Empirical Study and Beyond](https://mlsanity.com/paper/2503.20589/) (2025)
- [LLM4PLC: Harnessing Large Language Models for Verifiable Programming of PLCs in Industrial Control Systems](https://mlsanity.com/paper/2401.05443/) (2024)
- [Correctness Assessment of Code Generated by Large Language Models Using Internal Representations](https://mlsanity.com/paper/2501.12934/) (2025)
- [Prompt-based Code Completion via Multi-Retrieval Augmented Generation](https://mlsanity.com/paper/2405.07530/) (2024)
- [A Simple yet Efficient Ensemble Approach for AI-generated Text Detection](https://mlsanity.com/paper/2311.03084/) (2023)
- [RAMBO: Enhancing RAG-based Repository-Level Method Body Completion](https://mlsanity.com/paper/2409.15204/) (2024)
- [Marking Code Without Breaking It: Code Watermarking for Detecting LLM-Generated Code](https://mlsanity.com/paper/2502.18851/) (2025)
- [FT2Ra: A Fine-Tuning-Inspired Approach to Retrieval-Augmented Code Completion](https://mlsanity.com/paper/2404.01554/) (2024)
- [Understanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2411.19463/) (2024)
- [AlignCoder: Aligning Retrieval with Target Intent for Repository-Level Code Completion](https://mlsanity.com/paper/2601.19697/) (2026)
- [RAG or Fine-tuning? A Comparative Study on LCMs-based Code Completion in Industry](https://mlsanity.com/paper/2505.15179/) (2025)
- [KOCO-BENCH: Can Large Language Models Leverage Domain Knowledge in Software Development?](https://mlsanity.com/paper/2601.13240/) (2026)
- [AI-generated Text Detection: A Multifaceted Approach to Binary and Multiclass Classification](https://mlsanity.com/paper/2505.11550/) (2025)
- [CCCI: Code Completion with Contextual Information for Complex Data Transfer Tasks Using Large Language Models](https://mlsanity.com/paper/2503.23231/) (2025)
- [Optimizing LLM Code Suggestions: Feedback-Driven Timing with Lightweight State Bounds](https://mlsanity.com/paper/2511.18842/) (2025)
- [SaraCoder: Orchestrating Semantic and Structural Cues for Resource-Optimized Repository-Level Code Completion](https://mlsanity.com/paper/2508.10068/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) have demonstrated remarkable performance in code completion. However, due to the lack of domain-specific knowledge, they may not be optimal in completing code that requires intensive domain knowledge for example completing the library names. Although there are several works that have confirmed the effectiveness of fine-tuning techniques to adapt language models for code completion in specific domains. They are limited by the need for constant fine-tuning of the model when the project is in constant iteration. To address this limitation, in this paper, we propose kNM-LM, a retrieval-augmented language model (R-LM), that integrates domain knowledge into language models without fine-tuning. Different from previous techniques, our approach is able to automatically adapt to different language models and domains. Specifically, it utilizes the in-domain code to build the retrieval-based database decoupled from LM, and then combines it with LM through Bayesian inference to complete the code. The extensive experiments on the completion of intra-project and intra-scenario have confirmed that kNM-LM brings about appreciable enhancements when compared to CodeGPT and UnixCoder. A deep analysis of our tool including the responding speed, storage usage, specific type code completion, and API invocation completion has confirmed that kNM-LM provides satisfactory performance, which renders it highly appropriate for domain adaptive code completion. Furthermore, our approach operates without the requirement for direct access to the language model's parameters. As a result, it can seamlessly integrate with black-box code completion models, making it easy to integrate our approach as a plugin to further enhance the performance of these models.