面向可操作的可视化：十年之后，生成式AI带来了什么改变，又有什么是它做不到的
Towards Actionable Visualization: Ten Years Later, What Generative AI Changes and What It Cannot
- Pontificia Universidad Católica de Chile（智利天主教 Pontificia 大学）
- Tehran Institute for Advanced Studies (TEIAS)（德黑兰高等研究院）
- Khatam University（哈塔姆大学）
- Feenk
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究回顾了十年前软件可视化领域的研究现状，探讨生成式AI对该领域的改变与局限，提出未来应聚焦于帮助人类理解复杂软件系统及AI生成内容，并指出可操作可视化已成为该领域可达成的标准。
AI中文摘要：
十年前，我们开展了一项关于软件可视化研究的调查，当时的假设是，该领域面临的采用挑战在于如何让开发者的需求与技术相匹配。生成式AI可能已经让这一假设过时，因为它将按需生成可视化内容的成本推向了零。我们认为，这反映了软件工程领域正在发生的更广泛的转变：随着AI降低了人工制品生成的价值，它提升了人类对这些人工制品的感知和指导工作的重要性。回顾我们2016年的研究，我们发现当时标记为被忽视的领域（如原理领域），如今正是AI使其变得易于处理的领域；而我们当时诊断出的工具可持续性问题，可能会被生成式AI加剧，之后才会有助于解决。有一项发现完全转变：通过沉浸式环境交付的研究份额增长了约15倍，不过它仍然是该领域的一小部分。我们认为，未来的研究应聚焦于帮助人们理解大型复杂软件系统，包括AI智能体生成的推理过程以及代码本身。人类一直对自己所信任的内容负有责任，但AI可能会导致人们在没有足够审查的情况下产生信任。我们直面随之而来的风险，从AI生成可视化内容本身的可靠性，到在机器擅长的领域与它们竞争的诱惑，并得出结论：可操作的可视化不再是遥远的目标，而是该领域如今可以达到的标准。
英文摘要：
Ten years ago, we surveyed software visualization research under the assumption that the challenge for adoption was matching developers' needs with techniques. Generative AI might have made that assumption obsolete by driving the cost of producing a visualization on demand toward zero. We argue that this mirrors a broader inversion already underway in software engineering: as AI devalues the production of artifacts, it elevates the human work of perceiving and directing them. Looking back at our 2016 research, we found that domains we flagged as neglected, such as rationale, are exactly the ones AI now makes tractable, and a tool-sustainability problem we diagnosed then is one generative AI may worsen before it helps solve. One finding shifted outright: the share of studies delivered through immersive environments grew roughly fifteen-fold, though it remains a small minority of the field. We argue that future research should focus on helping people understand large and complex software systems, including the reasoning processes generated by AI agents alongside the code itself. Humans have always been accountable for what they trust, but AI may lead them to trust without enough scrutiny. We confront the risks that follow, from the reliability of AI-generated visualization itself to the temptation of competing with machines on their own terrain, and conclude that actionable visualization is no longer a distant destination but a standard the field can now meet.
