一句话：提出因果软件工程愿景，用因果模型与推理贯穿软件生命周期，并给出路线图与评估基准议程。
软件工程日益依赖代码、现场数据与社会技术过程信号，在高不确定性下做出高风险决策。异常检测、预测分析与 LLM 智能体等 AI 支持虽增强了模式发现与建议生成能力，但相关性模型难以回答干预性与反事实问题，例如更换负载均衡策略的预期影响，或采用不同发布计划能否避免故障。本文提出因果软件工程（CSE）愿景：将因果模型与因果推理系统性地融入软件生命周期，以显式假设、不确定性感知的效应估计和反事实诊断增强现有实践。作者给出覆盖开发与运维的因果优先工作流、分阶段工具与组织采纳路线图，以及衡量进展的评估与基准议程。
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.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2605-02454,
title = {Causal Software Engineering: A Vision and Roadmap},
author = {Roberto Pietrantuono and Luca Giamattei and Stefano Russo and Julien Siebert and Neil Walkinshaw},
booktitle = {FSE},
year = {2026},
doi = {10.1145/3803437.3805585}
}