[saidnooneever]

interesting data and results. it kind of looks like they are good at coding plainly no buffer overflows or such things, but as noted application logic is the trick. Things that are difficult for humans (authentication, paralelism) seems also tricky for them.

Id wonder if they are badly putting together the logic from good instructions or that the prompting was missing pieces and it followed correctly but provided some broken code due to missing requirements / details.

[pyryt]

Good question. We don't collect the 'what lead to this code' data, such as agent traces, so I can't say for sure.

From my own experience, I would attribute most of bugs that I write to the agent not considering one of the many constraints it should consider. This could be it assuming a different shape of data, or not browsing the codebase thoroughly enough at some point during its work and missing a parallel, or it not testing an edge case because it didn't think of it, and so on.

So, I would place this more under the latter, 'missing requirements / details', bucket. But its not clear to me that I should be giving the requirements as a prompt. Similarly to a good engineer, an ideal agent would spend a good amount of time understanding all the requirements before completing work. This could include browsing the codebase more extensively, running more tests, asking me for details it cannot find, and so on.

[moose6912]

Not surprisingly as the AI is trained on code written by humans, so things that are difficult for humans will also be difficult for them to catch