软件工程中的草稿链：将简洁推理应用于代码任务的挑战
Chain of Draft for Software Engineering: Challenges in Applying Concise Reasoning to Code Tasks
AI总结：
本研究将草稿链（CoD）方法拓展至软件工程，设计适配代码任务的CoD变体，经SWE-bench实验验证其token用量仅为思维链的55.4%、效率提升约45%，且保持90%以上的代码质量，为平衡开发效率与质量提供实用指导。
AI中文摘要：
大语言模型（LLMs）已成为软件开发的重要工具，但在处理复杂代码任务时往往需要冗长的中间推理，导致高延迟和高成本。本研究将草稿链（Chain of Draft, CoD）方法拓展至软件工程领域，设计并评估了多种适配代码任务的CoD变体。通过在SWE-bench基准的全部300个样本上开展综合实验，我们发现所有CoD变体的token使用量均显著低于思维链（Chain of Thought, CoT），其中基线CoD的效率最高，token用量仅为CoT的55.4%。尽管这带来了可观的效率提升——对应约45%的处理时间和API成本降低，但与原始CoD论文中针对数学推理报告的7.6%的极低token占比存在差异。这种差异源于软件任务固有的复杂性和上下文依赖性，需要更详细的推理来维持解决方案质量。我们的多维度质量评估显示，在正确性、兼容性和可维护性等关键指标上，CoD变体保持了CoT 90%以上的代码质量，使其成为注重效率的实际开发场景中的实用替代方案。本研究揭示了领域特性如何影响提示策略的有效性，并为软件工程应用中平衡效率与解决方案质量提供了框架。研究结果为根据项目需求选择合适的提示策略、优化基于LLM的开发工作流提供了实践指导。
英文摘要：
Large language models (LLMs) have become vital tools for software development, but they often require verbose intermediate reasoning for complex code tasks, leading to high latency and costs. This research extends the Chain of Draft (CoD) method to software engineering, designing and evaluating multiple CoD variants tailored for code tasks. Through comprehensive experiments on all 300 samples from the SWE-bench benchmark, we found that all CoD variants used significantly fewer tokens than Chain of Thought (CoT), with Baseline CoD being most efficient at 55.4% of CoT's tokens. While this represents substantial efficiency gains - translating to approximately 45% reduction in processing time and API costs - it differs from the extreme 7.6% reported in the original CoD paper for mathematical reasoning. This difference stems from the inherent complexity and context-dependency of software tasks, which require more detailed reasoning to maintain solution quality. Our multi-dimensional quality assessment revealed that CoD variants maintain over 90% of CoT's code quality across key metrics including correctness, compatibility, and maintainability, making them practical alternatives for real-world development scenarios where efficiency matters. This research demonstrates how domain-specific characteristics influence prompting strategy effectiveness and provides a framework for balancing efficiency with solution quality in software engineering applications. Our findings offer practical guidance for optimizing LLM-based development workflows through appropriate prompting strategy selection based on project requirements.
