>To study how agent success on benchmark tasks relates to real-world usefulness, we had 4 active maintainers from 3 SWE-bench Verified repositories review 296 AI-generated pull requests (PRs). We had maintainers (hypothetically) accept or request changes for patches as well as provide the core reason they were requesting changes: core functionality failure, patch breaks other code or code quality issues.
I would also advise taking a look at the rejection reasons for the PRs. For example, Figure 5 shows two rejections for "code quality" because of (and I quote) "looks like a useless AI slop comment." This is something models still do, but that is also very easily fixable. I think in that case the issue is that the level of comment wanted hasn't been properly formalized in the repo and the model hasn't been able to deduce it from the context it had.
As for the article, I think mixing all models together doesn't make sense. For example, maybe a slope describe the increasing Claude Sonnet better than a step function.