Annie Lin | Searcharxiv

arXiv2026

Flowcode: An AI-Powered Programming Environment for Scaffolding Iteration in Creative Computing Education

Building upon found examples is a popular way people learn to code, especially in creative coding communities where sharing projects and remixing are common practices. But effectively doing so requires being able to 1) understand how existing code works, and 2) extend it by writing code that implements your own ideas, practices that can be challenging for new creative coders. We explored how to support these two processes through the design of Flowcode, a creative coding programming environment that integrates a flowchart for visualizing code structure and a chat interface tailored to support learning to code over vibe coding. We share how we iterated on the design of Flowcode over two studies with new creative coders, reflecting on the roles visualization and friction may play in enabling productive AI-use in computing education.

cs.HC↗

arXiv2025

FLAG: Formal and LLM-assisted SVA Generation for Formal Specifications of On-Chip Communication Protocols

Formal specifications of on-chip communication protocols are crucial for system-on-chip (SoC) design and verification. However, manually constructing these formal specifications from informal documents remains a tedious and error-prone task. Although recent efforts have used Large Language Models (LLMs) to generate SystemVerilog Assertion (SVA) properties from design documents for Register-Transfer Level (RTL) design verification, in our experience these approaches have not shown promise in generating SVA properties for communication protocols. Since protocol specification documents are unstructured and ambiguous in nature, LLMs often fail to extract the necessary information and end up generating irrelevant or even incorrect properties. We propose FLAG, a two-stage framework to help construct formal protocol specifications from informal documents. In the first stage, a predefined template set is used to generate candidate SVA properties. To avoid missing necessary properties, we develop a grammar-based approach to generate comprehensive template sets that capture critical signal behaviors for various communication protocols. In the second stage, we utilize unambiguous timing diagrams in conjunction with textual descriptions from the specification documents to filter out incorrect properties. A formal approach is first implemented to check the candidate properties and filter out those inconsistent with the timing diagrams. An LLM is then consulted to further remove incorrect properties with respect to the textual description, obtaining the final property set. Experiments on various open-source communication protocols demonstrate the effectiveness of FLAG in generating SVA properties from informal documents.

cs.AR↗