juicer -- Squeeze SSOT Out of Existing Code

juicer – Squeeze SSOT Out of Existing Code Image: AI generated

“We already have 200 endpoints”

Someone reads the yongol post and asks: “Looks good, but our project already has tens of thousands of lines. Do we need to write SSOT from scratch?”

Starting from scratch would be nice, but it is impossible in practice. No team can stop a running service and write specs from zero. You cannot tear up the foundation of a store that is open for business.

That is why juicer was built. It squeezes SSOT out of existing code.

Put the fruit in whole, juice comes out

juicer statically analyzes web framework source code to auto-extract declarative specs.

juicer scan --openapi ./my-project

This single line produces an OpenAPI 3.0 spec from existing code. What used to take days of manual work finishes in seconds.

Zero runtime overhead. No instrumentation needed. Pure static analysis. The same direction has been validated academically – Huang et al. (2024) presented Respector at ICSE, a technique for automatically generating OpenAPI specs from source code via static analysis alone.

Three frameworks

Framework	Language	Extraction
Gin	Go	Routes, bindings, responses, middleware
NestJS	TypeScript	Decorators, DTOs, guards, interceptors
FastAPI	Python	Routes, Pydantic models, dependencies

Go, TypeScript, Python – the three most widely used backend frameworks covered.

What it extracts

Layer	Output
Routes	HTTP method, path, handler location, middleware
Request	Body binding type + struct fields, query/form/path params, file uploads
Response	Status codes, body type + struct fields, `json`/`validate` tags
OpenAPI	paths, parameters, requestBody, responses, components/schemas
DDL	Per-table CREATE TABLE snapshot from migration history
SQL	Repository method skeletons – CRUD type, table, params, return

Decisions scattered across source code are condensed into declarative specs.

DDL consolidation

When 50 migration files have piled up, it is hard to tell what the current schema looks like. You have to mentally replay the accumulated ALTER TABLEs in order.

juicer ddl ./migrations -o ./schema

Reads the entire migration history and outputs one clean CREATE TABLE snapshot per table. 50 files become as many .sql files as there are tables.

sqlc query scaffolding

Extracts SQL query skeletons from code written with the repository pattern.

juicer sql next --repo ./repository --queries ./db/query

next – ratchet workflow. Shows the next repository method without a query, write the skeleton, move on. When all are filled, it stops.

juicer sql status

Shows current progress. Not the agent declaring “I’m done,” but the machine counting the remaining methods.

Entry into the yongol pipeline

juicer’s output is yongol’s input.

Existing code
  ↓ juicer scan --openapi
  ↓ juicer ddl
  ↓ juicer sql next
OpenAPI + DDL + sqlc queries
  ↓ yongol validate
  ↓ yongol generate
SSOT-based codebase

juicer reverse-extracts SSOT from existing code; yongol validates consistency and generates code. The transition from brownfield to greenfield doesn’t happen all at once but incrementally. Perry and Wolf (1992) defined architecture erosion and drift – violating the spec is erosion; becoming insensitive to the spec is drift. juicer is the tool that reverse-extracts specs from code where drift has already occurred, realigning them.

Start by feeding juicer’s OpenAPI and DDL to yongol. Run validate to expose cross-layer inconsistencies. Fix them one by one. Add SSaC. Add Rego. Add Hurl. At every step, yongol guarantees consistency.

Not foundation work but seismic retrofitting. Martin Fowler’s (2004) Strangler Fig pattern – don’t replace legacy all at once; wrap it incrementally. Reinforce the building without closing the store.

Get started

npx skills add park-jun-woo/juicer

juicer scan --openapi ./my-project

Existing code becomes juice.

Code: github.com/park-jun-woo/juicer

References

Huang, R., Motwani, M., Martinez, I., & Orso, A. (2024). Generating REST API Specifications through Static Analysis. Proceedings of the IEEE/ACM 46th International Conference on Software Engineering (ICSE 2024). ACM
Lercher, A., Macho, C., Bauer, C., & Pinzger, M. (2024). Generating Accurate OpenAPI Descriptions from Java Source Code. arXiv:2410.23873. arXiv
Chauhan, S., Rasheed, Z., et al. (2026). OpenAI for OpenAPI: Automated generation of REST API specification via LLMs. Journal of Systems and Software. arXiv
Perry, D. E. & Wolf, A. L. (1992). Foundations for the Study of Software Architecture. ACM SIGSOFT Software Engineering Notes, 17(4), 40-52. ACM
De Silva, L. & Balasubramaniam, D. (2012). Controlling software architecture erosion: A survey. Journal of Systems and Software, 85(1), 132-151. ScienceDirect
Fowler, M. (2004). StranglerFigApplication. martinfowler.com/bliki. Link
Fritzsch, J., Bogner, J., Wagner, S., & Zimmermann, A. (2019). Microservices Migration in Industry: Intentions, Strategies, and Challenges. 2019 IEEE ICSME, pp. 481-490. arXiv
Curino, C. A., Moon, H. J., & Zaniolo, C. (2008). Graceful database schema evolution: the PRISM workbench. Proceedings of the VLDB Endowment, 1(1), 761-772. VLDB
Nghiem, D., Guo, H., & Foster, J. S. (2023). A Qualitative Study of REST API Design and Specification Practices. 2023 IEEE VL/HCC, pp. 148-156. IEEE