ARC: Compiling Hundreds of Requirement Scenarios into A Runnable Web System
Weiyu Kong, Yun Lin, Xiwen Teoh, Duc-Minh Nguyen, Ruofei Ren, Jiaxin Chang, Haoxu Hu, Haoyu Chen

TL;DR
ARC is a novel technique that compiles hundreds of requirement scenarios from multi-modal documents into fully runnable web systems, including code, design, tests, and traceability, significantly improving testing success and user productivity.
Contribution
ARC introduces a requirement compilation approach that generates complete, maintainable web systems from multi-modal requirement documents, surpassing existing code generation methods.
Findings
ARC systems pass 50.6% more GUI tests than baselines
Generated systems are maintainable and verifiable
Novice users can produce complex requirement documents in hours
Abstract
Large Language Models (LLMs) have improved programming efficiency, but their performance degrades significantly as requirements scale; when faced with multi-modal documents containing hundreds of scenarios, LLMs often produce incorrect implementations or omit constraints. We propose Agentic Requirement Compilation (ARC), a technique that moves beyond simple code generation to requirement compilation, enabling the creation of runnable web systems directly from multi-modal DSL documents. ARC generates not only source code but also modular designs for UI, API, and database layers, enriched test suites (unit, modular, and integration), and detailed traceability for software maintenance. Our approach employs a bidirectional test-driven agentic loop: a top-down architecture phase decomposes requirements into verifiable interfaces, followed by a bottom-up implementation phase where agents…
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Taxonomy
TopicsModel-Driven Software Engineering Techniques · Software Engineering Research · Software System Performance and Reliability
