Programmers who want to change how we code before catastrophe strikes

Most of the problems described are already tackled in modern software development. TDD, Correct-by-Construction and input validations are some tools to ensure resilience. The car acceleration issue could also have been avoided with a default to safe approach. Spaghetti code should have been refactored. It is not programming itself that is the issue, it is the auto maker that is at fault here.

In ETL there are some tools to visually manipulate the data flow from one end to another. Some ETL software even allow you to visualize the effect of the changes on the fly, much like the Mario game. Solutions developed like this are easily understandable and maintainable by others even with minimal documentation (but require understanding of the business problem). But, much like normal programming, once you want to do something that exceeds the capabilities of the ETL software you are using, or when performance is an issue, you have to understand how the underlying software works under the hood. You can become really good at solving one set of problems with a ETL software once you have mastered it, but this is limited to one domain of problems. Likewise, specialized software allowing easy visualization and manipulation is usually very domain specific.

You can tackle complexity in programming hiding it behind libraries and databases that do the heavy lifting while the programmer integrates the pieces and accounts for particularities of the problem he is solving. I could envision representing library functions as black boxes and connecting arrows to integrate them, having input validation and strong typing or automatic typecasting. Still, when you get too far from the machine, you miss the edge cases, the things you can't imagine when you visualize whatever you are creating in your head, the problems that only arise when you externalize and codify the knowledge. I think this is at the core of the issue; in order to instruct the computer to do something, you have to externalize tacit knowledge. In doing so you come across problems you just can't see from too high up.

If you tell someone to bring you your socks, you don't need to plan for it. If they bring the wrong socks, you just fix it. If you tell them to invade the beaches at Normandy, you might want to work out more of the details in advance. You can tell someone to remove a splinter or remove a brain tumor, and your part of the instructions might be roughly equivalent if you are telling an "API" that has already been adequately told how to do what you ask.

The problem of unintended consequences of instructions has been with us far longer than computer software. In any story of a magic jini granting three wishes, the third wish always ended up a version of `git reset --hard`. I love having direct manipulation tools that simulate your proposed solution, giving you much faster feedback. Midas with VR goggles would have quickly seen something unintended turn to gold and canceled before committing. That's extremely helpful.

But this isn't the ultimate solution for how to deal with software complexity. It's a very helpful tool in some cases. Some software should still just be coded immediately and fixed as needed (takes less time to do it again than to create a simulator), some would benefit most from a good, debugged library (I'd rather the robot already know how to remove the tumor than show me direct feedback of my fumbling), some from direct manipulation tools, some from mathematical techniques (remembering that mathematically proven software is buggy if an errant RAM chip violates the axiom that `0=0`), some from better testing, some from better runtime monitoring, and so on.

But as with humans' verbal instructions, there will always be leftover unanticipated consequences due to flaws in the spec, bugs in the code, and breakage in the implementation.