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Large Language Models (LLMs) have demonstrated remarkable performance in codecompletion. However, due to the lack of domain-specific knowledge, they may notbe optimal in completing code that requires intensive domain knowledge forexample completing the library names. Although there are several works thathave confirmed the effectiveness of fine-tuning techniques to adapt languagemodels for code completion in specific domains. They are limited by the needfor constant fine-tuning of the model when the project is in constantiteration.  To address this limitation, in this paper, we propose $k$NM-LM, aretrieval-augmented language model (R-LM), that integrates domain knowledgeinto language models without fine-tuning. Different from previous techniques,our approach is able to automatically adapt to different language models anddomains. Specifically, it utilizes the in-domain code to build theretrieval-based database decoupled from LM, and then combines it with LMthrough Bayesian inference to complete the code. The extensive experiments onthe completion of intra-project and intra-scenario have confirmed that $k$NM-LMbrings about appreciable enhancements when compared to CodeGPT and UnixCoder. Adeep analysis of our tool including the responding speed, storage usage,specific type code completion, and API invocation completion has confirmed that$k$NM-LM provides satisfactory performance, which renders it highly appropriatefor domain adaptive code completion. Furthermore, our approach operates withoutthe requirement for direct access to the language model's parameters. As aresult, it can seamlessly integrate with black-box code completion models,making it easy to integrate our approach as a plugin to further enhance theperformance of these models.

Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases