Generating entire functions has been possible for a long time before Copilot. The discipline that studies algorithms that generate code or programs is called Pr

Generating entire functions has been possible for a long time before Copilot. The discipline that studies algorithms that generate code or programs is called Program Synthesis. For a recent review see Gulwani et al: Program Synthesis https://www.semanticscholar.org/paper/Program-Synthesis-Gulwani-Polozov/f0a79cb16c3e53e9c536b65ec7b2157db1d352f1 https://www.semanticscholar.org/paper/Program-Synthesis-Gulw... Copilot and similar systems (like DeepMind's AlphaCode) are a step backwards compared with earlier systems most of which are capable of not only generating code, but generating correct programs, where correctness is measured with respect to a specification, which can be complete or incomplete (as in input/output examples etc). Systems like Copilot instead use a large language model to generate code by completing an initial "prompt" and have no way to control what kind of code is generated or whether it does what the programmer expects it to do. The task of checking correctness is left to the user. The result is code that may or may not contain bugs to an extent difficult to predict beforehand. Specifically about Copilot, it's based on Codex, a large language model that's a variant of GPT-3 fine-tuned on all of github. In formal testing, OpenAI found that Codex could generate the correct program about 30% of the time, which is pretty low: Evaluating Large Language Models Trained on Code https://arxiv.org/abs/2107.03374 https://arxiv.org/abs/2107.03374 Interestingly, if Codex was allowed 100 "guesses", the correct program was in there 75% of the time, instead. But that only goes to show that these kinds of systems are code firehoses without any ability to tell a correct program from garbage.