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AI Summary: TraceCoder uses a multi-agent swarm equipped with live execution traces to autonomously identify, debug, and patch logical errors in AI-generated code.

Paper2026-02-28•Source ↗•28 attns494 checkouts
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TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging of LLM-Generated Code

Jiangping Huang·
Wenguang Ye·
Weisong Sun·
Jian Zhang·
Mingyue Zhang·
Yang Liu

ABSTRACT

While LLMs excel at generating initial code snippets, they struggle immensely with resolving complex runtime errors. TraceCoder introduces a multi-agent framework that utilizes actual execution traces to dynamically debug generated code. By providing the agent swarm with real-time stack traces and memory states, the system autonomously identifies and patches logical errors that static code reviewers miss.

#debugging#ai-engineering#code-generation#agentic-ai/paper/year/2026#agentic-ai/year/2026#agentic-ai/paper/month/202602#agentic-ai/month/202602#software-engineering#agentic-ai/paper#agentic-ai

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