Problem
Research questions and friction points this paper is trying to address.
vibe-coded applications
silent behavioral failures
user-visible information flow
FlowCheck: Helping End-Users Specify and Verify Intent in Vibe-Coded Web Apps
🤖 AI Summary
为解决振动编码应用中用户可见信息流错误问题，提出FlowCheck约束语言，通过界面直接指定并验证信息流，并转换为CodeQL分析以准确检测违规。
📝 Abstract
Vibe-coded applications often contain silent behavioral failures in which the interface appears functional even though user-visible information does not flow to the expected state or output. We introduce FlowCheck, a constraint language to specify these user-visible information flows directly through the application interface, where constraints can also be displayed and inspected without reading code, and are structured enough for reliable LLM generation. FlowCheck translates the constraints into deterministic CodeQL analyses, and we evaluate it across four applications generated via Claude Code, and compare with three coding models as bug-finding baselines. We find that FlowCheck correctly translates and flags all 30 of our injected constraint violations with no false positives. In contrast, frontier models (Claude Opus 4.7, DeepSeek V3, and Gemini Pro) showed significantly lower accuracy when prompted to find bugs in the same code, with none achieving full accuracy. This approach lets vibe coders state intent in terms of the interface they understand, and checks it deterministically against the code they do not.
Innovation
Methods, ideas, or system contributions that make the work stand out.
constraint language
information flow
deterministic CodeQL analyses
vibe-coded applications
LLM generation
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