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However, a real implementation may still have to include code to handle the case where something happens that was assumed to be impossible, even if that handling boils down to printf("Sucks to be you") and exit(666) — i.e., letting a human operator clean up the mess [93]. (This is arguably the difference between computer science and software engineering.)
Martin Kleppmann (Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems)
THINK OF THE WAY a stretch of grass becomes a road. At first, the stretch is bumpy and difficult to drive over. A crew comes along and flattens the surface, making it easier to navigate. Then, someone pours gravel. Then tar. Then a layer of asphalt. A steamroller smooths it; someone paints lines. The final surface is something an automobile can traverse quickly. Gravel stabilizes, tar solidifies, asphalt reinforces, and now we don’t need to build our cars to drive over bumpy grass. And we can get from Philadelphia to Chicago in a single day. That’s what computer programming is like. Like a highway, computers are layers on layers of code that make them increasingly easy to use. Computer scientists call this abstraction. A microchip—the brain of a computer, if you will—is made of millions of little transistors, each of whose job is to turn on or off, either letting electricity flow or not. Like tiny light switches, a bunch of transistors in a computer might combine to say, “add these two numbers,” or “make this part of the screen glow.” In the early days, scientists built giant boards of transistors, and manually switched them on and off as they experimented with making computers do interesting things. It was hard work (and one of the reasons early computers were enormous). Eventually, scientists got sick of flipping switches and poured a layer of virtual gravel that let them control the transistors by punching in 1s and 0s. 1 meant “on” and 0 meant “off.” This abstracted the scientists from the physical switches. They called the 1s and 0s machine language. Still, the work was agonizing. It took lots of 1s and 0s to do just about anything. And strings of numbers are really hard to stare at for hours. So, scientists created another abstraction layer, one that could translate more scrutable instructions into a lot of 1s and 0s. This was called assembly language and it made it possible that a machine language instruction that looks like this: 10110000 01100001 could be written more like this: MOV AL, 61h which looks a little less robotic. Scientists could write this code more easily. Though if you’re like me, it still doesn’t look fun. Soon, scientists engineered more layers, including a popular language called C, on top of assembly language, so they could type in instructions like this: printf(“Hello World”); C translates that into assembly language, which translates into 1s and 0s, which translates into little transistors popping open and closed, which eventually turn on little dots on a computer screen to display the words, “Hello World.” With abstraction, scientists built layers of road which made computer travel faster. It made the act of using computers faster. And new generations of computer programmers didn’t need to be actual scientists. They could use high-level language to make computers do interesting things.* When you fire up a computer, open up a Web browser, and buy a copy of this book online for a friend (please do!), you’re working within a program, a layer that translates your actions into code that another layer, called an operating system (like Windows or Linux or MacOS), can interpret. That operating system is probably built on something like C, which translates to Assembly, which translates to machine language, which flips on and off a gaggle of transistors. (Phew.) So, why am I telling you this? In the same way that driving on pavement makes a road trip faster, and layers of code let you work on a computer faster, hackers like DHH find and build layers of abstraction in business and life that allow them to multiply their effort. I call these layers platforms.
Shane Snow (Smartcuts: The Breakthrough Power of Lateral Thinking)
Thompson, who designed and implemented the first UNIX system, has stated that he much prefers printf debugging.24 But as Baird put it, “If you are a genius like Ken Thompson, you are going to write good code.”25 The rest of us need to move beyond printf debugging to get our code working, as I rapidly discovered once I started working on the internals of Windows NT at Microsoft (the fundamental skill is learning to use a specialized piece of software called a debugger, which can examine the memory of another program).
Adam Barr (The Problem with Software: Why Smart Engineers Write Bad Code)
Seibel: What are the techniques that you use there? Print statements? Armstrong: Print statements. The great gods of programming said, “Thou shalt put printf statements in your program at the point where you think it's gone wrong, recompile, and run it.
Peter Seibel (Coders at Work: Reflections on the Craft of Programming)