JetBrains Research 论文主张编码智能体基准应匹配真实用户任务流
JetBrains Research 论文指出编码智能体基准应模仿真实用户的使用方式，因为真实会话会在多条消息中混合并切换任务类型。研究者分析了 4,782 次真实智能体会话，发现功能开发与缺陷修复占比远低于一半，而 SWE-Bench Pro 中两者占 93.8% 的任务。
New JetBrains Research paper argues that coding-agent benchmarks should copy how their real users actually work, because real sessions mix and switch task types across many messages.
Real developers using coding agents ask questions, plan, review, and switch tasks mid-session, but benchmarks like SWE-Bench Pro hand the agent 1 detailed issue and grade 1 final patch. In that benchmark, new features and bug fixes make up 93.8% of tasks.
JetBrains researchers studied 4,782 real agent sessions, where features and bug fixes were well under half of requests. In longer sessions, users kept switching between questions, planning, reviews, and edits.
They built SWE-TaskFlow, which splits each benchmark issue into several turns and adds checkable questions about the code. On 700 SWE-Bench Pro tasks, splitting roughly doubled agent cost with no stable change in success rate.
If you evaluate coding agents, first measure what your own users ask for, then shape the benchmark's conversation to match it.
– arxiv. org/abs/2610.09633
Title: "Coding-Agent Benchmarks Should Match Their Users' Task Flows"
来源：rohanpaul_ai · x.com