Comment by nerdy

Programs writing other programs is an interesting concept, but isn't it naive in most cases?

If program X can write program Y, program X is probably unnecessarily complex with regards to solving problem Y. Program X could simply execute program Y's operations at their abstract level, which would save it all of the outputted language understanding and translation. The only tangible difference between the two would be the ability to "save" the commands, at which point program X is a scripting language and the input commands are the script.

Wouldn't this solution be better in most cases? If program X at runtime requires significant configuration to produce program Y but those configuration inputs are not saved. Then if program Y were to be changed, program X must be rerun with all of the configuration input again. In the scripting language scenario, the script is modified.

For a programming language to receive something simpler than the language it is written in, the ideas must be abstracted to reduce the amount of code required to perform the task. For example simple addition of multiple elements can be reduced to a "sum" function. The "programmer" using this must still have knowledge of the sum function and its use. Abstractions require the system to have knowledge of that domain, so with each new domain a whole other set of abstractions and complications are introduced.

So if a machine were to write the entire program, how does it differ from machine learning? While machine learning might be often used for smaller algorithms, this case would apply ML to the entire problem as sets of smaller problems. If a human must still program in some abstracted sense, isn't it just a scripting/programming language? How would a component of this suggested project not fit into the "machine learning" or "scripting/programming language" categories?