规范驱动的数据架构重构：从物理代码到逻辑与概念规范
Specification-Driven Data Architecture Reconstruction: From Physical Code to Logical and Conceptual Specifications
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中文总结 AI 辅助
本研究提出溯源感知、确定性优先的流水线，从Oracle DDL重构逻辑与概念数据规范，区分观察事实、推导与LLM建议；在249个样本上完成97.99%，LLM候选父表可接受率仅约17.5%，验证了防止模型假设被静默提升的架构基线。
中文摘要 AI 辅助
遗留数据库迁移通常始于不完整或过时的文档，这使得物理数据定义语言（DDL）成为数据架构的主要证据。然而，DDL并不能完全编码概念意图，且模型生成的补全可能看似合理却不正确。本研究提出并评估了一种具有溯源感知、确定性优先的流水线，用于从面向Oracle的DDL中重构逻辑和概念数据规范，同时明确区分观察事实、确定性推导和大语言模型（LLM）建议。该流水线在可选的LLM辅助增强之前，执行DDL调查、解析、整合、主键回填、类型规范化和声明关系图构建。它在确定性目录和声明关系图中保留源溯源，并将推断的主键和外键候选记录在单独的可审查覆盖层中。我们在249个包含1,225个SQL文件的工件化模式样本上评估了该实现。流水线完成了244个样本（97.99%）；其中52个完成的样本不包含可提取的DDL。在完成的样本中，确定性路径重构了208个表并恢复了278条声明的外键记录；其中168个重构的表在回填前缺少显式解析的主键。LLM增强在36个样本中生成了100个外键候选，但逻辑规范候选的父表可接受率仅为17.9%，概念规范候选的为17.5%。这些发现表明，所提出的确定性优先架构能够保留可审计的结构基线，量化观察到的主键和关系缺口，并防止模型生成的假设被静默提升为基于源头的架构事实。
英文摘要
Legacy database migrations often begin with incomplete or outdated documentation, leaving physical data definition language (DDL) as the principal evidence of data architecture. However, DDL does not fully encode conceptual intent, and model-generated completions can be plausible without being correct. This study proposes and evaluates a provenance-aware, deterministic-first pipeline for reconstructing logical and conceptual data specifications from Oracle-oriented DDL while explicitly separating observed facts, deterministic derivations, and large language model (LLM) suggestions. The pipeline performs DDL investigation, parsing, consolidation, primary-key backfilling, type normalization, and declared relationship-graph construction before optional LLM-assisted enrichment. It preserves source provenance in the deterministic catalog and declared relationship graph, and records inferred primary-key and foreign-key candidates in a separate reviewable overlay. We evaluated the implementation on 249 artifactized schema samples comprising 1,225 SQL files. The pipeline completed 244 samples (97.99%); 52 completed samples contained no extractable DDL. Across completed samples, the deterministic path reconstructed 208 tables and recovered 278 declared foreign-key records; 168 of the reconstructed tables lacked an explicitly parsed primary key before backfilling. LLM enrichment generated 100 foreign-key candidates in 36 samples, but the parent-table admissibility rate was only 17.9% for logical-specification candidates and 17.5% for conceptual-specification candidates. These findings show that the proposed deterministic-first architecture can preserve an auditable structural baseline, quantify observed primary-key and relationship gaps, and prevent model-generated hypotheses from being silently promoted to source-grounded architectural facts.
发表机构
- EPAM Systems
机构由 AI 辅助整理，请以论文原文为准。
