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arXiv cs.AI· Thomas Bartz-Beielstein·· 3 天前AI 评分31

删除代码而不丢失知识:面向 Python 库的再生式软件

Deleting Code Without Losing Knowledge: Regenerative Software for Python Libraries

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开源包 spotregen 实现了 Fowler 提出的七项再生式软件原语,让程序可被删除重建而不丢失知识:知识以机器可读记录存于受追踪目录,每个单元据此生成包含架构、意图、决策、公共契约、评估与消费者但无实现的 regeneration bundle。

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Abstract:In the age of "Software 3.0", when a complete program can be rewritten in minutes by an agent, the traditional approach of relying on code as the sole source of truth becomes questionable. Fowler advocated for a set of seven primitives, which lay the framework for regenerative software, so that programs can be deleted and rebuilt without losing their knowledge. The knowledge lives in machine-readable records in a tracked directory, from which a *regeneration bundle* is built for each unit. The regeneration bundle is a single file with the module's architecture, intent, decisions, public contract, evaluations, and consumers, but no implementation, plus the one dependency module it may import. The open-source package spotregen implements these primitives and provides a graphical user interface. This report presents a first application of Fowler's primitives to the safety-critical forecasting library spotforecast2-safe. During the first experiments, the Claude Code session that orchestrated the study consumed more than 43 million tokens, almost all of them its own history re-sent with every request. This motivated the switch to an architecture that is built on local models. Deterministic checks and the maintainer judged the results. The report contains executable examples for every primitive. Each example runs spotregen against the repository or against a throw-away copy of it.
Comments: 53 pages
Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
MSC classes: 68-04
ACM classes: D.2.0
Cite as: arXiv:2610.17197 [cs.SE]
  (or arXiv:2610.17197v1 [cs.SE] for this version)
  https://doi.org/10.48550/arXiv.2610.17197

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Thomas Bartz-Beielstein [view email]
[v1] Mon, 12 Oct 2026 17:57:40 UTC (4,695 KB)

来源:arXiv cs.AI · arxiv.org