让工具从预测转向因果推理，提升软件工程中的智能辅助能力
Reasoning Beyond Prediction: From Data-Driven to Causal Software Engineering
- 用因果推理替代单纯模式预测，增强工具对开发决策的支持
- 提出新范式：机器主动辅助工程师的逻辑推演而非仅执行任务
- 适合关注AI赋能开发流程、追求深度智能支持的研究者与工程师
软件工程是一门高度智力化、创造性的学科，需协调众多相互依赖的任务来设计、构建并保障日益复杂系统的质量。随着对软件的期望不断提升——涵盖AI驱动的产品、广泛分布且云原生的架构，以及深度嵌入的软硬件融合环境——其复杂性持续上升。为此，以深度学习为驱动的新一代协同工程方法与工具应运而生，旨在提升自动化水平与决策支持能力。然而，这些进展仍远未达到现代软件开发所需求的智能支持水平。本文呼吁一种新的“人机协作”范式：机器不仅自动化常规任务或基于学习模式进行预测，更应通过因果视角主动增强工程师的推理能力。随着软件日益智能，所需的辅助也必须更智能。
原文摘要 · Abstract (English)
Software engineering is an intellectually demanding, creative discipline that juggles a web of interdependent tasks to design, build, and assure the quality of increasingly complex systems. As our expectations from software soar - with demands spanning AI-driven products, pervasively distributed and cloud-native architectures, and deeply embedded cyber-physical environments - its complexity steadily increases. In response, a new wave of co-engineering methods and tools, fueled by deep learning, has emerged to augment the process, enhancing automation and decision support. Yet, these advances remain far from delivering the kind of intelligent support that modern software development demands. We call for a new paradigm of human-machine cooperation: one where machines don't just automate routine tasks or predict from learned patterns, but actively amplify engineers' reasoning through the lens of causation. As software becomes smarter, a smarter support is needed.
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