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Carnegie Mellon AI · 2026/7/28 16:00:00

You Can't Remove Humans From Software Engineering
AI 中文解读
核心亮点:卡内基梅隆大学教授直言,AI编程再厉害,软件工程的核心依然是“人”,没法靠机器全包。
通俗解读:最近AI写代码火得不行,几秒钟就能生成一段程序,看着很省事。但这位教授提醒,软件工程不只是敲键盘——你得先搞明白要做什么、怎么做对、出了问题谁负责。AI能帮忙写代码,却没法替人类定义目标、理解复杂需求、保证系统安全可靠。就像盖房子,机器人能砌砖,但设计师、监理、业主的决策和判断才是决定房子好坏的关键。所以未来软件行业不是“把人赶走”,而是让人类和AI合作,人负责更高级的思考和把关。
实际影响:对普通人来说,以后学编程可能不再死记硬背语法,而是更注重逻辑思维和沟通能力;工作中用到的软件也会更智能,但背后仍有工程师为安全负责。如果你想转行做软件,最值得培养的不是拼写速度,而是定义问题、解决复杂场景的“人味儿”能力。AI是工具,人类才是主角。
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July 28, 2026
You Can't Remove Humans From Software Engineering
CMU Professor Says Future of the Field Depends on People as Much as AI
By Mallory LindahlMedia InquiriesAaron Aupperlee
Chief Marketing and Communications Officer, School of Computer Science
aaupperlee@cmu.edu
412-268-9068
There's no doubt that artificial intelligence is transforming how software is built, and tools that can generate code in seconds have sparked excitement about the future of programming. But they're also raising questions about trust, accountability, the role of human expertise and the future of software engineering education.For Claire Le Goues, a professor in Carnegie Mellon University's School of Computer Science, those questions sit at the heart of software engineering. Her research program in the Software and Societal Systems Department explores how people can build confidence in increasingly complex software systems, especially as AI becomes more integral to their creation. While advances in AI may make programming more accessible and automate some of the more tedious aspects of software development, Le Goues argues that the field's biggest challenges remain deeply human: defining goals, understanding intent and determining whether a system is actually doing what people want it to do.SCS faculty member Claire Le Goues argues that no matter what the AI hype might tell you, people will always be at the heart of software engineering.Most people don't think much about software engineering. Why should they care about research on trustworthy software? Software touches nearly every part of modern life, from healthcare and finance to everyday things like the Spotify app on your phone. But the challenge is that software is incredibly difficult to get completely right. In practice, no software is perfect. Instead, we use a variety of techniques to build confidence that it will behave the way we expect. I study the kinds of evidence we need to trust software that was written, at least in part, by AI. That's ultimately what my research is about.AI can quickly generate software. How does that change the way we think about trust, correctness and reliability? Entire areas of computer science ar
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