Large Language Models (LLMs) have demonstrated remarkable performance in code generation, offering new possibilities for translating natural language into executable programs. To further enhance LLMs’ code generation capabilities, Retrieval-Augmented Generation (RAG) has emerged as a promising strategy by retrieving code examples aligned with the generation intent to guide the process. However, existing RAG-based methods often suffer from unnecessary augmentation, preference misalignment, and surface-level mimicry, which undermine the effectiveness of retrieved examples in guiding LLMs toward accurate code generation. To address these challenges, we propose SRACG, a Selective Retrieval-Augmented Code Generation framework. SRACG begins with a necessity-aware selection mechanism to identify generation intents that genuinely require retrieval support, thereby avoiding degradation from indiscriminate augmentation. For intents identified as needing enhancement, it first employs a multi-objective retrieval strategy to select examples that are semantically aligned with the intent. These candidates are then further filtered by assessing their consistency with the LLM’s inherent generation preferences, ensuring alignment in both style and structure. Finally, it extracts execution plans from the filtered examples to uncover their underlying logic, guiding the LLM to better comprehend the examples instead of merely mimicking surface-level content. Experimental results on widely used benchmarks show that SRACG significantly improves the success rate of LLM-generated code and outperforms existing approaches.

Repository-level code generation aims to generate target code conditioned on the context of the specified repository. Existing approaches generally adopt the Retrieval-Augmented Generation (RAG) framework to retrieve relevant contextual information from the repository, thereby mitigating the challenge of long context windows. However, these methods typically struggle to identify truly relevant information for the current generation requirement, and they generally overlook the imperative need for understanding the repository-level code structure during the retrieval process. To address these issues, we propose RepoMind, a novel framework that advances repository-level code generation by leveraging a repository documentation library and LLM reasoning capabilities for API retrieval. Specifically, RepoMind first introduces the RepoDocs Agent. This component constructs multi-granularity, hierarchical structural documentation in a bottom-up manner, providing LLMs with a high-quality knowledge base for understanding repository functionality and structure. Built upon this hierarchical knowledge base, RepoMind further integrates the Reasoning-Retrieval Agent. This agent mimics human developer patterns by utilizing the repository documentation for layer-by-layer exploration, and ultimately precisely localizes the relevant API sets. Finally, the framework combines the functionally relevant API set retrieved via LLM reasoning with the semantically similar API set retrieved by the vector retriever, utilizing this hybrid context to boost code generation performance. Evaluation on widely-used repository-level code generation benchmarks, CoderEval and DevEval, demonstrates that RepoMind surpasses state-of-the-art methods, achieving up to a 13.50% relative improvement in Pass@1 scores. The open-source code will be released at https://github.com/ABigTrouble/RepoMind.

Repository-level code generation has attracted growing interest, yet most benchmarks and methods remain maintainer-centric, emphasizing bug fixing and feature implementation. In contrast, a common yet underexplored scenario is repository usage: external users want to build applications by correctly invoking repository-internal APIs, composing them into runnable end-to-end workflows rather than modifying the codebase. To support this setting, we introduce RUCCE, a benchmark for repository-usage code generation built from real-world Python repositories. Each instance pairs a natural-language usage instruction with grounded target APIs and a verified reference script, enabling evaluation of both API retrieval and repository-usage code generation. Building on RUCCE, we propose RUCACoder, a closed-loop multi-agent framework with a Retriever for hierarchical repository exploration, a Verifier for reranking and validation, and a Coder for feedback-driven script synthesis. Experiments across multiple backbone LLMs show that RUCACoder consistently outperforms strong retrieval and generation baselines.

Code search aims to quickly locate target code snippets from databases using natural language queries, which promotes code reusability. Existing methods can effectively obtain aligned token-level and query word-level features. However, these studies usually represent the semantics of code and query by averaging the features of each token and word respectively, which makes it difficult to accurately capture the code details that are closely related to the query. To address this issue, we propose a fine-grained code search model that consists of a cross-modal encoder, a mapping layer, and a classification layer. Specifically, we utilize a pre-trained model, GraphCodeBERT, in the cross-modal encoder to align features. In the mapping layer, we introduce a co-attention network to capture the fine-grained interactions between code and query, ensuring a model can precisely identify key code segments relevant to the query. Finally, in the classification layer, we incorporate instruction learning techniques that leverage contextual reasoning to improve the accuracy of query-code matching. Experimental results show that our proposed model significantly outperforms existing methods across multiple programming language datasets.