InfCode-C++: Intent-Guided Semantic Retrieval and AST-Structured Search for C++ Issue Resolution

• URL: http://arxiv.org/abs/2511.16005v1
• Date: Thu, 20 Nov 2025 03:05:26 GMT
• Title: InfCode-C++: Intent-Guided Semantic Retrieval and AST-Structured Search for C++ Issue Resolution
• Authors: Qingao Dong, Mengfei Wang, Hengzhi Zhang, Zhichao Li, Yuan Yuan, Mu Li, Xiang Gao, Hailong Sun, Chunming Hu, Weifeng Lv,
• Abstract summary: We introduce INFCODE-C++, the first C++-aware autonomous system for end-to-end issue resolution.<n>The system combines two complementary retrieval mechanisms -- semantic code-intent retrieval and deterministic AST-structured querying.<n>It achieves a resolution rate of 25.58%, outperforming the strongest prior agent by 10.85 percentage points and more than doubling the performance of MSWE-agent.
• Score: 31.437457217953835
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language model (LLM) agents have recently shown strong performance on repository-level issue resolution, but existing systems are almost exclusively designed for Python and rely heavily on lexical retrieval and shallow code navigation. These approaches transfer poorly to C++ projects, where overloaded identifiers, nested namespaces, template instantiations, and deep control-flow structures make context retrieval and fault localization substantially more difficult. As a result, state-of-the-art Python-oriented agents show a drastic performance drop on the C++ subset of MultiSWE-bench. We introduce INFCODE-C++, the first C++-aware autonomous system for end-to-end issue resolution. The system combines two complementary retrieval mechanisms -- semantic code-intent retrieval and deterministic AST-structured querying -- to construct accurate, language-aware context for repair.These components enable precise localization and robust patch synthesis in large, statically typed C++ repositories. Evaluated on the \texttt{MultiSWE-bench-CPP} benchmark, INFCODE-C++ achieves a resolution rate of 25.58\%, outperforming the strongest prior agent by 10.85 percentage points and more than doubling the performance of MSWE-agent. Ablation and behavioral studies further demonstrate the critical role of semantic retrieval, structural analysis, and accurate reproduction in C++ issue resolution. INFCODE-C++ highlights the need for language-aware reasoning in multi-language software agents and establishes a foundation for future research on scalable, LLM-driven repair for complex, statically typed ecosystems.

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