因果软件工程：愿景与路线图
Causal Software Engineering: A Vision and Roadmap
AI总结：
研究软件工程在不确定性下决策的关键问题，提出因果软件工程范式，通过因果模型和推理为软件生命周期活动提供信息，还给出工作流程视图、路线图及评估议程。
摘要：
软件工程越来越多地涉及在不确定性下做出高风险决策，利用来自代码、现场数据和社会技术过程的信号。最近人工智能驱动的支持（如异常检测、预测分析、AIOps以及基于大语言模型的智能体）增强了工程师检测模式、合成内容和建议的能力，但许多关键问题是干预性或反事实性的。相关模型难以回答“如果我们采取行动会怎样”的问题。我们提出因果软件工程（CSE）作为未来范式，其中因果模型和因果推理系统地为软件生命周期中的活动提供信息，通过明确假设、不确定性感知效应估计和反事实诊断增强现有实践。我们概述了（i）跨越开发和运营的因果优先工作流程视图，（ii）工具和组织采用的分阶段路线图，以及（iii）衡量进展的评估和基准议程。
英文摘要：
Software engineering increasingly involves making high-stakes decisions under uncertainty, using signals from code, field data, and socio-technical processes. Recent AI-driven support (e.g., anomaly detection, predictive analytics, AIOps, as well as LLM-based agents) has amplified engineers' ability to detect patterns and synthesize content and recommendations, but many critical questions are interventional or counterfactual: What is the expected impact of changing a load-balancing strategy? Would an outage have been avoided under a different release plan? Correlational models answer "what tends to co-occur"; they struggle to answer "what would happen if we act." We propose Causal Software Engineering (CSE) as a future paradigm in which causal models and causal reasoning systematically inform activities across the software lifecycle, augmenting existing practices with explicit assumptions, uncertainty-aware effect estimates, and counterfactual diagnosis. We outline (i) a causal-first workflow view spanning development and operations, (ii) a staged roadmap for tools and organizational adoption, and (iii) an evaluation and benchmark agenda for measuring progress.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
