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
- 类型：arxiv
- 标识：2608.12440
- 链接：https://arxiv.org/abs/2608.12440
- 主分类：agent
- 形态：survey
- 被引：0
- 被引来源：Semantic Scholar
- S2被引：0
- 影响力被引：0
- TLDR：This paper reports a single, fully instrumented case study of a large-scale architectural refactoring by an AI coding agent under a specification-first protocol, with no human review of the generated code and no pre-existing oracle to validate the target behaviour. The task, dismantling a central invariant across a large interdependent codebase, was assessed by the author as effectively infeasible through incremental refactoring, the kind of change that conventionally calls for a rewrite instead. Under the protocol described here, the agent completed it successfully. The system is a 717,725-li
- 待LLM分类：否
- 标题中文：规范优先收敛与 AI 编码 Agent：在一 717k 行代码库中跨 189 个文件拆除核心架构不变量的案例研究（无测试预言机、无人工代码审查）
- TLDR中文：本文报告了一项完整的、有完整记录的案例研究：在规范优先协议下，由 AI 编码 Agent 对大规模架构进行重构，期间无人工代码审查、无预先存在的预言机来验证目标行为。该任务是在一个大型相互依赖的代码库中拆除核心不变量，作者评估认为通过增量重构基本上不可行，这类变更通常需要重写。本文所述协议下，Agent 成功完成了任务。该系统包含 717,725 行
- 来源文件：
- /inbox/tom/_candidates/2026-08-15-agent-rag-longcontext-candidates.json
- /inbox/tom/_candidates/2026-08-16-agent-rag-longcontext-candidates.json
- /inbox/tom/_candidates/2026-08-17-agent-rag-longcontext-candidates.json
- /inbox/tom/_candidates/2026-08-17-agent-memory-tool-use-candidates.json
- [S2 enrich]