Empowering Autonomous Debugging Agents with Efficient Dynamic Analysis

Autonomous agents for automated program repair represent a promising frontier in software engineering, yet their effectiveness is often hindered by reliance on post-mortem, coarse-grained execution feedback. While integrating traditional interactive debuggers seems a natural solution, their low-level, line-by-line interaction paradigm turns to be cost-inefficient for LLM-based agents, leading to exhausted budgets and unproductive loops. To mitigate this, we introduce Agent-centric Debugging Interface (ADI), a novel agent-centric debugging interface designed for cost-efficient, end-to-end autonomous interaction. Specifically, Agent-centric Debugging Interface realizes a function-level interaction paradigm, powered by our Frame Lifetime Trace—a comprehensive data structure encapsulating a function's stateful execution trace—and a set of high-level navigational commands. Our extensive evaluation on the SWE-bench benchmark demonstrates the effectiveness and efficiency of ADI. By simply equipping a basic agent with ADI, it successfully resolves 63. 8% of the tasks on the SWE-bench Verified set, even slightly outperforming the highly-optimized and high-investment Claude-Tools agent, at an average cost of 1. 28 per task with Claude-Sonnet-3. 7. Furthermore, we demonstrate ADI's generality by integrating it as a plug-and-play component into the existing SOTA agents, delivering consistent gains ranging from 6. 2% to 18. 5% on the resolved tasks. These results indicate that Agent-centric Debugging Interface could achieve a general and efficient enhancement for the existing autonomous agents.