You had to imagine how the codes were going to be interpreted by the computer—that is, you had to play computer in your head.
If you wanted to change the margin, you could drag a ruler at the top of the screen—and see the effect of that change. The document thereby came to feel like something real, something you could poke and prod at. For him, the idea that people were doing important work, like designing adaptive cruise-control systems or trying to understand cancer, by staring at a text editor, was appalling.
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Photoshop, for instance, puts powerful image-processing algorithms in the hands of people who might not even know what an algorithm is. Squarespace, a company that is perhaps best known for advertising aggressively on podcasts, makes a tool that lets users build websites by pointing and clicking, instead of by writing code in HTML and CSS.
It is powerful enough to do work that once would have been done by a professional web designer. But those were just a handful of examples. The overwhelming reality was that when someone wanted to do something interesting with a computer, they had to write code. Victor, who is something of an idealist, saw this not so much as an opportunity but as a moral failing of programmers at large. His talk was a call to arms.
At the heart of it was a series of demos that tried to show just how primitive the available tools were for various problems—circuit design, computer animation, debugging algorithms—and what better ones might look like.
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His demos were virtuosic. It showed a split screen with a game that looked like Mario on one side and the code that controlled it on the other. Suppose you wanted to design a level where Mario, jumping and bouncing off of a turtle, would just make it into a small passageway. Victor wanted something more immediate. The whole problem had been reduced to playing with different parameters, as if adjusting levels on a stereo receiver, until you got Mario to thread the needle. When the audience first saw this in action, they literally gasped. Most software involved behavior that unfolded, in complex ways, over time, and Victor had shown that if you were imaginative enough, you could develop ways to see that behavior and change it, as if playing with it in your hands.
Chris Granger, who had worked at Microsoft on Visual Studio, was likewise inspired. It was like taking off a blindfold.
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In April of , he sought funding for Light Table on Kickstarter. In programming circles, it was a sensation. The ideas spread.
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The notion of liveness , of being able to see data flowing through your program instantly, made its way into flagship programming tools offered by Google and Apple. The default language for making new iPhone and Mac apps, called Swift, was developed by Apple from the ground up to support an environment, called Playgrounds, that was directly inspired by Light Table. But seeing the impact that his talk ended up having, Bret Victor was disillusioned.
He knew something was wrong when people began to invite him to conferences to talk about programming tools. Victor suggested that the same trick could be pulled for nearly every problem where code was being written today. Only then would people with the most urgent computational problems be able to grasp those problems directly, without the intermediate muck of code.
In a recent essay, Victor implored professional software developers to stop pouring their talent into tools for building apps like Snapchat and Uber. If you were making the control system for an elevator, for instance, one rule might be that when the door is open, and someone presses the button for the lobby, you should close the door and start moving the car. In model-based design, by contrast, the picture on your screen is more like a blueprint.
Still, making software this way is qualitatively different than traditional programming. In traditional programming, your task is to take complex rules and translate them into code; most of your energy is spent doing the translating, rather than thinking about the rules themselves. In the model-based approach, all you have is the rules.
The people know how to code.
The problem is what to code. On this view, software becomes unruly because the media for describing what software should do—conversations, prose descriptions, drawings on a sheet of paper—are too different from the media describing what software does do, namely, code itself. Too much is lost going from one to the other. The idea behind model-based design is to close the gap.
The very same model is used both by system designers to express what they want and by the computer to automatically generate code. Of course, for this approach to succeed, much of the work has to be done well before the project even begins. They have to make a program that turns these models into real code. Esterel Technologies, which was acquired by ANSYS in , grew out of research begun in the s by the French nuclear and aerospace industries, who worried that as safety-critical code ballooned in complexity, it was getting harder and harder to keep it free of bugs.
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And the people in charge of integrating the systems, and debugging them, had noticed that the number of bugs was increasing. Instead of a single flight computer, there were now dozens, each responsible for highly specialized tasks related to control, navigation, and communications. Coordinating these systems to fly the plane as data poured in from sensors and as pilots entered commands required a symphony of perfectly timed reactions. Ledinot decided that writing such convoluted code by hand was no longer sustainable.
It was too hard to understand what it was doing, and almost impossible to verify that it would work correctly. He went looking for something new. And a mess in control software was dangerous. Esterel was designed to make the computer handle this complexity for you. The model becomes the detailed blueprint that the computer would use to do the actual programming.
Ledinot and Berry worked for nearly 10 years to get Esterel to the point where it could be used in production. Part of the draw for customers, especially in aviation, is that while it is possible to build highly reliable software by hand, it can be a Herculean effort. Ravi Shivappa, the VP of group software engineering at Meggitt PLC, an ANSYS customer which builds components for airplanes, like pneumatic fire detectors for engines, explains that traditional projects begin with a massive requirements document in English, which specifies everything the software should do.
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And when the customer changes the requirements, the code has to be changed, too, and tested extensively to make sure that nothing else was broken in the process. The cost is compounded by exacting regulatory standards. The FAA is fanatical about software safety. The agency mandates that every requirement for a piece of safety-critical software be traceable to the lines of code that implement it, and vice versa.
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So every time a line of code changes, it must be retraced to the corresponding requirement in the design document, and you must be able to demonstrate that the code actually satisfies the requirement. Much of the benefit of the model-based approach comes from being able to add requirements on the fly while still ensuring that existing ones are met; with every change, the computer can verify that your program still works. Still, most software, even in the safety-obsessed world of aviation, is made the old-fashioned way, with engineers writing their requirements in prose and programmers coding them up in a programming language like C.
As Bret Victor made clear in his essay, model-based design is relatively unusual. Most programmers feel the same way. They like code. At least they understand it. It is a pattern that has played itself out before. Whenever programming has taken a step away from the writing of literal ones and zeros, the loudest objections have come from programmers. Emmanuel Ledinot, of Dassault Aviation, pointed out that when assembly language was itself phased out in favor of the programming languages still popular today, like C, it was the assembly programmers who were skeptical this time.
I n , Chris Newcombe had been working at Amazon for almost seven years, and had risen to be a principal engineer. He is one of those engineers whose work quietly keeps the internet running. But all he could think about was that buried deep in the designs of those systems were disasters waiting to happen.