JetBrains 研究：编程智能体基准应匹配真实任务流
JetBrains 研究团队分析 4,782 个真实编程智能体会话后发现，功能开发与 bug 修复在真实请求中占比远低于一半，用户会在长会话中不断切换提问、规划、审查与编辑任务。相比之下，SWE-Bench Pro 中这两类任务占比高达 93.8%，研究认为编程智能体基准应匹配真实任务流。
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：Rohan Paul · x.com