面向结构化、状态感知和执行导向的软件工程代理推理
Towards Structured, State-Aware, and Execution-Grounded Reasoning for Software Engineering Agents
- Concordia University（康科德大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出通过结构化、状态感知和执行导向的推理方法，提升软件工程代理在长周期任务中的推理能力和可靠性。
AI中文摘要：
软件工程（SE）代理在支持各种SE任务方面展现出了有前景的能力。当前的SE代理仍然本质上是反应性的，决策主要基于对话历史和最近的响应。然而，这种反应性设计在代理的记忆中没有显式的结构或持久的状态，使得长周期推理变得困难。因此，SE代理在推理步骤中难以保持一致的理解，无法在新证据出现时调整假设，或将执行反馈纳入系统状态的思维推理模型中。在本文中，我们主张，为了进一步推进SE代理，需要超越反应性行为，转向结构化、状态感知和执行导向的推理。我们概述了显式结构、持久且演进的状态，以及执行导向反馈的整合如何帮助SE代理在长周期任务中进行更一致和可靠的推理。我们还提供了一个初步的发展路线图，用于开发下一代能够更有效执行现实任务的SE代理。
英文摘要：
Software Engineering (SE) agents have shown promising abilities in supporting various SE tasks. Current SE agents remain fundamentally reactive, making decisions mainly based on conversation history and the most recent response. However, this reactive design provides no explicit structure or persistent state within the agent's memory, making long-horizon reasoning challenging. As a result, SE agents struggle to maintain a coherent understanding across reasoning steps, adapt their hypotheses as new evidence emerges, or incorporate execution feedback into the mental reasoning model of the system state. In this position paper, we argue that, to further advance SE agents, we need to move beyond reactive behavior toward a structured, state-aware, and execution-grounded reasoning. We outline how explicit structure, persistent and evolving state, and the integration of execution-grounded feedback can help SE agents perform more coherent and reliable reasoning in long-horizon tasks. We also provide an initial roadmap for developing next-generation SE agents that can more effectively perform real-world tasks.
