Interaction with debugging tools enables large language models (LLMs) to reason over concrete runtime states, rather than relying solely on static analysis of source code. Specifically, with the help of a debugger, an LLM-based agent can observe actual program execution by inspecting intermediate variable states and st...
Jia-Xin-Zhang-Song-Yan Liu, Xing Hu, Xin Xia· Proceedings of the ACM on So...· 0 citations
Chain-of-Thought (CoT) prompting can substantially improve the reasoning ability of large language models (LLMs), but it often comes with high inference cost due to long and poorly controlled reasoning traces. This overhead is particularly problematic in software engineering tasks (e.g., code generation), where both la...
Ke-Rui Huang, Shu-Han Liu, Xing Hu et al.· Proceedings of the ACM on So...· 0 citations
Large language models (LLMs) have shown strong performance in static code tasks like code search, summarization, and generation, but remain limited in dynamic code reasoning, which involves inferring how programs behave during execution without actually running them. This limitation stems from LLMs being trained on sta...
Yan Wang, Ling Ding, Jie-Chen Sun et al.· Proceedings of the ACM on Pr...· 0 citations
This paper proposes AgentExecutor, a novel multi-agent framework for partial code execution that is Supported by the power of LLM agents who can think, act, and get feedback iteratively, and is able to autonomously explore a richer action space, enabling diverse operations such as creating resource files and resolving...
Junkai Chen, Cheng-Ran Yang, Xing Hu et al.· 0 citations
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