Reproduce “Specification-first convergence with an AI coding agent: a case study of dismantling a core architectural invariant across 189 files in a 717k-line codebase with no test oracle and no human code review” on OpenResearch
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About this paper
Joel Abenhaim
AI Sovereign Labs developed a "specification-first convergence" protocol, allowing an AI coding agent to perform a complex architectural refactoring across 189 files in a 717k-line TypeScript codebase. The agent corrected 201 defects and ambiguities within the specification and code before any human execution, delivering a functional, correct implementation without human code review or a pre-existing test oracle.
Key insights
- Shifting the primary audit focus from reviewing generated code to refining a formal specification can mitigate the human cognitive bottleneck in large-scale AI-assisted software development.
- A detailed, iteratively refined specification can effectively serve as a primary correctness reference for novel behaviors in the absence of pre-existing test oracles.
- An extensive system prompt, coupled with a structured multi-phase workflow, enables an AI agent to achieve high autonomy in identifying and correcting both specification and code defects prior to human execution.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.