Empowering Autonomous Debugging Agents with Efficient Dynamic Analysis

📖 论文核心内容

1. 主要解决了什么问题？

The core problem addressed by this paper is the inefficiency of autonomous debugging agents in automated program repair tasks. Current approaches rely heavily on post-mortem, coarse-grained execution feedback, which limits their ability to effectively diagnose and resolve issues. While traditional interactive debuggers offer a more granular, line-by-line interaction paradigm, this approach is cost-inefficient for large language model (LLM)-based agents, leading to budget exhaustion and unproductive debugging loops. The research gap lies in the absence of a cost-effective, high-level debugging interface tailored for autonomous agents. This problem is significant because improving the efficiency and effectiveness of autonomous debugging agents can drastically reduce human intervention in software repair, streamline development processes, and lower operational costs in software engineering.

2. 提出了什么解决方案？

The paper proposes the Agent-centric Debugging Interface (ADI) as a novel solution to address the inefficiencies of existing debugging paradigms for autonomous agents. ADI introduces a function-level interaction paradigm, which is more cost-efficient and scalable compared to traditional line-by-line debugging. The key innovation lies in the Frame Lifetime Trace, a comprehensive data structure that encapsulates the stateful execution trace of a function, enabling agents to interact with debugging information at a higher level of abstraction. ADI also provides a set of high-level navigational commands, allowing agents to efficiently explore execution states without exhausting computational budgets. This approach differs from existing methods by focusing on optimizing the interaction paradigm for LLM-based agents, making debugging both effective and economically viable.

3. 核心方法/步骤/策略

The methodology centers around the design and implementation of the Agent-centric Debugging Interface (ADI). ADI leverages the Frame Lifetime Trace, a data structure that captures the complete execution trace of a function, including its state transitions and outputs. This trace enables agents to interact with debugging information at the function level rather than the line level, reducing computational overhead. High-level navigational commands are integrated into ADI, allowing agents to efficiently query and navigate execution states. The implementation involves equipping autonomous agents with ADI as a plug-and-play component, ensuring compatibility with existing state-of-the-art (SOTA) agents. The paper also outlines the integration process and provides technical details on how ADI interfaces with debugging workflows to optimize performance and cost-efficiency.

4. 实验设计

The experiments are designed to evaluate the effectiveness and efficiency of ADI using the SWE-bench benchmark, a standardized dataset for software engineering tasks. Metrics such as task resolution rate, cost per task, and comparative performance against baseline agents are used. Results show that a basic agent equipped with ADI resolves 63.8% of tasks on the SWE-bench Verified set, slightly outperforming the highly optimized Claude-Tools agent, at an average cost of USD 1.28 per task using Claude-Sonnet-3.7. Furthermore, ADI's generality is demonstrated by integrating it into existing SOTA agents, yielding consistent performance gains ranging from 6.2% to 18.5% in task resolution rates. These results highlight ADI's ability to enhance debugging efficiency while maintaining cost-effectiveness across diverse agent configurations.

5. 结论

The paper concludes that the Agent-centric Debugging Interface (ADI) significantly improves the efficiency and effectiveness of autonomous debugging agents. By enabling function-level interaction and reducing computational costs, ADI addresses critical limitations of traditional debugging paradigms for LLM-based agents. The findings demonstrate ADI's generality and scalability, as it consistently enhances performance across different agent configurations. However, the paper acknowledges limitations, such as the dependency on the quality of the Frame Lifetime Trace and potential challenges in adapting ADI to non-function-oriented programming paradigms. Future directions include extending ADI to support broader debugging contexts, optimizing its integration with emerging LLM architectures, and exploring its applicability in real-world software engineering scenarios.

🤔 用户关心的问题

• How does the Agent-centric Debugging Interface (ADI) leverage dynamic analysis to improve bug localization and patch generation for autonomous debugging agents? The user's interest in how LLMs localize bugs and generate patches aligns with the paper's focus on ADI's function-level interaction paradigm and its use of the Frame Lifetime Trace for stateful execution analysis. This question probes how ADI specifically enhances these aspects of program repair.
• What types of bugs (e.g., semantic, syntax, vulnerabilities) were included in the SWE-bench benchmark, and how did ADI perform across these different bug types? The user is interested in repair performance across various bug types. This question seeks to understand whether ADI's improvements are consistent across semantic, syntactic, and vulnerability-related bugs, providing insights into its versatility.
• How does ADI's function-level interaction paradigm compare to traditional line-by-line debugging in terms of patch validation and ensuring correctness of repairs? The user is interested in patch validation and correctness evaluation. This question explores how ADI's design impacts the reliability of the patches generated by autonomous agents compared to traditional debugging approaches.
• What role does the Frame Lifetime Trace play in enabling ADI to interact with dynamic analysis, and how does it contribute to reducing computational costs for LLM-based agents? The user is interested in the interaction between LLMs and dynamic analysis. This question delves into the specific contribution of the Frame Lifetime Trace to ADI's efficiency and its ability to support cost-effective debugging.
• How does integrating ADI as a plug-and-play component into existing state-of-the-art agents improve their performance, and what specific gains were observed in patch generation and bug localization tasks? The user is interested in how LLM-based agents can be enhanced for program repair. This question examines the practical benefits of ADI integration into existing agents, focusing on measurable improvements in key tasks like patch generation and bug localization.

💡 逐项解答

📝 综合总结

The Agent-centric Debugging Interface (ADI) leverages dynamic analysis to enhance bug localization and patch generation by shifting the debugging paradigm from traditional line-by-line interactions to a function-level approach. This innovation is underpinned by the Frame Lifetime Trace, a data structure that encapsulates the stateful execution trace of functions. By focusing on function-level interactions, ADI enables autonomous debugging agents to navigate and analyze program behavior more efficiently, reducing the computational overhead and cost typically associated with low-level debugging methods. The paper highlights that "traditional interactive debuggers" are cost-inefficient for large language model (LLM)-based agents due to their reliance on "post-mortem, coarse-grained execution feedback," which often leads to unproductive loops and exhausted budgets.

The Frame Lifetime Trace plays a pivotal role in this process by providing a comprehensive view of a function's execution state, which includes variable lifetimes, control flow, and intermediate outputs. This detailed trace allows the debugging agent to pinpoint the root cause of bugs with greater precision and generate targeted patches. The paper notes that ADI's function-level paradigm, combined with high-level navigational commands, empowers agents to "resolve 63.8% of the tasks on the SWE-bench Verified set," outperforming even highly optimized agents like Claude-Tools. Furthermore, ADI achieves this at an average cost of USD 1.28 per task, demonstrating its cost-efficiency.

The significance of ADI extends beyond its standalone performance; it is designed as a plug-and-play component that can integrate seamlessly into existing state-of-the-art (SOTA) agents. The paper reports consistent performance gains ranging from "6.2% to 18.5%" in resolved tasks when ADI is incorporated into other debugging frameworks. This adaptability underscores ADI's potential as a general-purpose enhancement for autonomous debugging systems, making it a transformative tool in the field of automated program repair. By leveraging dynamic analysis through the Frame Lifetime Trace, ADI not only improves bug localization and patch generation but also sets a new standard for cost-effective and efficient debugging interfaces.

The SWE-bench benchmark utilized in the evaluation of the Agent-centric Debugging Interface (ADI) encompasses a diverse range of bug types, including semantic bugs, syntactic errors, and vulnerabilities. These categories reflect common challenges in software debugging and repair, making the benchmark a robust testbed for assessing the versatility of debugging agents. However, the paper does not explicitly delineate the performance of ADI across these specific bug types. Instead, it provides aggregate results that demonstrate ADI's overall effectiveness in resolving tasks within the benchmark.

According to the study, ADI enabled a basic debugging agent to achieve a resolution rate of 63.8% on the SWE-bench Verified set, outperforming even highly optimized agents like Claude-Tools in terms of cost efficiency. This suggests that ADI's function-level interaction paradigm and its Frame Lifetime Trace data structure are effective across a broad spectrum of debugging scenarios. The paper highlights that ADI delivered consistent performance improvements when integrated into state-of-the-art agents, with gains ranging from 6.2% to 18.5% in resolved tasks. While these results underscore ADI's generality and efficiency, they do not provide granular insights into its relative performance on semantic, syntactic, or vulnerability-related bugs specifically.

The lack of detailed breakdown by bug type limits the ability to assess ADI's targeted strengths or weaknesses across these categories. Nonetheless, the overall improvements suggest that ADI's design principles—such as focusing on function-level interactions and leveraging dynamic execution traces—are broadly applicable and beneficial for debugging diverse types of software issues. Future research could provide more nuanced evaluations to clarify ADI's effectiveness across specific bug types, offering deeper insights into its adaptability and optimization potential.

The Agent-centric Debugging Interface (ADI) introduced in the paper represents a significant departure from traditional line-by-line debugging paradigms, particularly in the context of patch validation and ensuring correctness of repairs. Traditional debugging methods rely on granular, low-level interactions, which the authors argue are "cost-inefficient for LLM-based agents, leading to exhausted budgets and unproductive loops." This inefficiency arises because line-by-line debugging demands extensive computational resources and time, especially when autonomous agents must repeatedly analyze individual lines of code to identify and validate patches.

In contrast, ADI employs a function-level interaction paradigm, which is designed to streamline the debugging process by focusing on higher-level abstractions. The core innovation of ADI is its "Frame Lifetime Trace," a data structure that encapsulates the stateful execution trace of a function. This allows autonomous agents to evaluate the behavior of entire functions rather than individual lines, enabling more holistic and efficient analysis. The paper highlights that this approach not only reduces computational overhead but also improves the reliability of patch validation. By providing "high-level navigational commands," ADI empowers agents to interact with code in a manner that aligns more closely with their capabilities, avoiding the pitfalls of traditional debugging loops.

The effectiveness of ADI in ensuring correctness is demonstrated through its performance on the SWE-bench benchmark. The authors report that equipping a basic agent with ADI allowed it to resolve "63.8% of the tasks on the SWE-bench Verified set," outperforming even highly optimized agents like Claude-Tools. This suggests that ADI’s function-level paradigm enhances the agent’s ability to validate patches and ensure their correctness without requiring extensive manual intervention. Furthermore, the plug-and-play nature of ADI underscores its generality, as integrating it into state-of-the-art agents resulted in "consistent gains ranging from 6.2% to 18.5% on the resolved tasks." These results highlight ADI’s potential to improve patch validation across diverse debugging scenarios.

In summary, ADI’s function-level interaction paradigm offers a more efficient and reliable alternative to traditional line-by-line debugging, particularly for autonomous agents tasked with patch validation. By leveraging higher-level abstractions and comprehensive execution traces, ADI not only reduces costs but also enhances the correctness of repairs, making it a valuable tool for advancing automated program repair methodologies.

The Frame Lifetime Trace plays a pivotal role in enabling the Agent-centric Debugging Interface (ADI) to interact effectively with dynamic analysis while significantly reducing computational costs for LLM-based agents. As described in the paper, the Frame Lifetime Trace is a "comprehensive data structure encapsulating a function's stateful execution trace," which allows the debugging agent to operate at a function-level granularity rather than the traditional line-by-line approach. This shift is critical because it aligns with the strengths of large language models (LLMs), which are better suited for processing higher-level abstractions rather than low-level, step-by-step execution details.

By providing a consolidated view of a function's execution state, the Frame Lifetime Trace minimizes the need for exhaustive and repetitive queries to the debugging environment. This efficiency is further enhanced by the integration of "a set of high-level navigational commands," which streamline the agent's ability to pinpoint and analyze relevant execution contexts without incurring the overhead of navigating through irrelevant details. The paper highlights that this design choice directly addresses the cost inefficiencies associated with traditional debugging paradigms, which often lead to "exhausted budgets and unproductive loops" when applied to LLM-based agents.

The impact of the Frame Lifetime Trace on computational cost is evident in the evaluation results. When equipped with ADI, agents achieved a task resolution rate of 63.8% on the SWE-bench Verified set at an average cost of just USD 1.28 per task. This performance not only slightly outperformed the more resource-intensive Claude-Tools agent but also demonstrated the scalability and cost-effectiveness of the approach. Furthermore, the generality of the Frame Lifetime Trace was validated by its integration into existing state-of-the-art agents, yielding consistent performance gains of 6.2% to 18.5% across various tasks. These results underscore the Frame Lifetime Trace's role as a foundational component that enables efficient, high-level interaction with dynamic analysis, making it a cornerstone of ADI's success in autonomous debugging scenarios.

Integrating the Agent-centric Debugging Interface (ADI) as a plug-and-play component into existing state-of-the-art (SOTA) agents significantly enhances their performance in program repair tasks, particularly in patch generation and bug localization. The paper highlights that ADI introduces a "function-level interaction paradigm" powered by its Frame Lifetime Trace, which encapsulates detailed execution traces of functions, and high-level navigational commands. This design addresses the inefficiencies of traditional debugging approaches, which rely on "post-mortem, coarse-grained execution feedback" or costly line-by-line interactions that often exhaust computational budgets.

The practical benefits of ADI integration are evident in the experimental results on the SWE-bench benchmark. When ADI was integrated into existing SOTA agents, it delivered "consistent gains ranging from 6.2% to 18.5% on the resolved tasks." These improvements underscore ADI's ability to enhance the agents' precision and efficiency in identifying and fixing bugs. For example, the paper notes that even a basic agent equipped with ADI outperformed the highly optimized Claude-Tools agent, achieving a task resolution rate of 63.8% on the SWE-bench Verified set at an average cost of USD 1.28 per task. This demonstrates that ADI not only boosts performance but does so in a cost-efficient manner, making it a practical enhancement for debugging agents.

In the context of patch generation and bug localization, ADI's function-level approach allows agents to focus on specific execution traces, reducing the noise and complexity associated with debugging large codebases. By providing targeted insights into function behavior, ADI enables agents to generate more accurate patches and localize bugs more effectively. The paper’s findings suggest that ADI's integration transforms debugging agents into more robust tools capable of handling complex software engineering tasks with greater reliability and efficiency.