ECLAIR：一种用于实证软件工程科学发现的因果基础AI框架
ECLAIR: A Causally-Grounded AI Framework for Scientific Discovery in Empirical Software Engineering
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中文总结 AI 辅助
该研究提出因果基础AI框架ECLAIR，将LLMs整合到软件工程科学过程各阶段，通过案例研究揭示提示设计对LLMs代码生成准确率的影响，为AI辅助软件工程实证研究提供方法论基础。
中文摘要 AI 辅助
科学方法长期以来指导着软件工程（SE）领域的实证研究，但现代软件系统的复杂性往往阻碍其系统应用。本文介绍ECLAIR，一种因果基础的AI框架，将大语言模型（LLMs）整合到科学过程的每个阶段，从假设生成到分析与解释。ECLAIR将LLMs视为在因果推断原则下运作的主动科学智能体，采用人在回路设计以防范不合理自动推理的风险。我们通过案例研究展示该框架，探究提示设计对两个LLMs代码生成准确率的影响。结果表明，对于这两个模型，指令式、更长的少样本及签名增强提示对准确率产生小的负因果效应，说明因果推理为解释软件现象发生的原因提供了有原则的基础。本研究提出了首个用于将LLMs嵌入SE领域科学方法的因果基础结构化方法论，围绕实证SE研究的认识论需求设计，为严谨的AI辅助研究奠定了基础。
英文摘要
The scientific method has long guided empirical research in Software Engineering (SE), but the complexity of modern software systems often hinders its systematic application. This paper introduces ECLAIR, a causally grounded AI framework that integrates Large Language Models (LLMs) into every stage of the scientific process, from hypothesis generation to analysis and interpretation. ECLAIR treats LLMs as active scientific agents operating under the principles of causal inference, within a human-in-the-loop design that safeguards against the risks of unsound automated reasoning. We demonstrate the framework through a case study examining how prompt design influences code generation accuracy in two LLMs. Results show that, for both models, instruction-style, longer few-shot, and signature-augmented prompts yield small negative causal effects on accuracy, illustrating how causal reasoning provides a principled foundation for explaining why software phenomena occur. This study presents the first causally grounded structured methodology for embedding LLMs within the scientific method in SE, designed around the epistemological demands of empirical SE research, establishing a basis for rigorous AI-assisted research.
