Open-source maintainers have coined a term for what AI coding agents are doing to their contribution queues: "AI slop." Low-quality AI-generated code, PRs, documentation, and bug reports are flooding development workflows at a pace that review processes weren't built to handle. A new qualitative study puts

Coding agents are often described as risky for production codebases, but the risk is not uniformly distributed. A new comparative study of 7,191 agent-generated pull requests and 1,402 human-authored PRs from Python repositories in the AIDev dataset finds a counter-intuitive split: coding agents introduce fewer breaking changes than

Most research on AI-generated code quality operates under controlled conditions — short-lived experiments, synthetic benchmarks, carefully curated prompts. What happens in production repositories over time is a different question, and until now it's been largely unanswered. A new large-scale empirical study changes that by tracking 304,362 verified AI-authored