Contract-Coding: Towards Repo-Level Generation via Structured Symbolic Paradigm

2024-11-20 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

Contract-Coding introduces a structured symbolic paradigm for repository-level code generation, addressing the "Context-Fidelity Trade-off" in intent-driven software engineering. This framework, developed by researchers at Beijing University of Posts and Telecommunications, utilizes an "Autonomous Symbolic Grounding" mechanism to project ambiguous user intents into a formal "Language Contract." This contract acts as a Single Source of Truth (SSOT), enabling architectural parallelism by isolating inter-module implementation details and reducing topological execution depth. Empirically, Contract-Coding achieved a 47% functional success rate on the Greenfield-5 benchmark, maintaining near-perfect structural integrity, while state-of-the-art agents suffered from various hallucinations. The approach demonstrates a 4.6x token compression ratio, effectively managing context for complex systems and transitioning from strict specification-following to robust, intent-driven architecture synthesis.

Key takeaway

For research scientists developing multi-agent code generation systems, Contract-Coding offers a robust alternative to sequential or brute-force context scaling approaches. You should consider adopting a "Language Contract" and "Contract-Driven Hierarchical Graph" to manage architectural complexity and enable parallel execution. This method prioritizes structural integrity, making generated codebases easier to debug and maintain, even if initial functional success rates are slightly lower than some commercial black-box solutions.

Key insights

Contract-Coding uses a formal "Language Contract" to enable parallel, intent-driven repository generation, mitigating context and hallucination issues.

Principles

• Decouple control complexity from implementation volume.
• Utilize information hiding via a structured-symbolic lens.
• Prioritize architectural integrity over algorithmic fidelity.

Method

The framework autonomously synthesizes a "Language Contract" from user intent, then uses a "Contract-Driven Hierarchical Graph" for parallel code generation, and a "Contract-Guided Auditing" mechanism for consistency and dynamic self-correction.

In practice

• Employ a "Language Contract" as an SSOT for multi-agent systems.
• Implement "Contract-Guided Auditing" for dynamic conflict resolution.
• Use a "Union-First Strategy" for semantic conflict resolution in contracts.

Topics

• Contract-Coding
• Repo-Level Code Generation
• Language Contract
• Autonomous Symbolic Grounding
• Architectural Parallelism

Code references

• imliinyi/Contract-Coding

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.