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A Practical Workflow for Contributing to a Large, Structured Codebase

This is the workflow I follow before I use AI agents to implement any feature or bug fix. 🧭 Requirements/Specification  ↓ Design/Architecture  ↓ AI Code Generation  ↓ Human Review  ↓ Build & Static Analysis  ↓ Testing & Validation …

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This is the workflow I follow before I use AI agents to implement any feature or bug fix. 🧭



Requirements/Specification

 ↓

Design/Architecture

 ↓

AI Code Generation

 ↓

Human Review

 ↓

Build & Static Analysis

 ↓

Testing & Validation

 ↓

Defect Resolution

 ↓

Security & Compliance Review

 ↓

Release

 ↓

Production Monitoring



vs



Claude Code

    ↓

Implements feature

    ↓

Codex QA Agent

    ↓

Runs application

    ↓

Tests happy path

    ↓

Tests edge cases

    ↓

Tests error handling

    ↓

Produces QA report



This will resolve the self-review bias, confirmation bias, or AI-to-AI bias.





1️⃣ Understand Before Writing Code



Before touching any code, I try to understand what I'm building and why. I usually start by reading:




  • specs/<module>/<TICKET>-<slug>.md

  • plan/<module>/<TICKET>-<slug>.md

  • status.md



Then I review the project conventions:




  • specs/CONVENTIONS.md

  • specs/conventions/core-porting.md



Finally, I read the existing implementation (entities, services, mappers, etc.) so my changes follow the existing architecture instead of introducing a new style.








💡 Pro-Tip

Good code fits into the codebase. Great code looks like it was always there.











2️⃣ Plan the Change



Once I understand the requirements, I identify which architectural layers are affected. I always respect the dependency order:




Schema / Entities / DAOs

Mappers / DTOs

Service Layer

Application Layer

Controllers


I don't jump ahead of dependencies.

If a change is complicated or ambiguous, I document the approach before writing code.

---

## 3️⃣ Write the Code

While implementing, I follow the repository's rules. Some examples:

| Rule | Detail |---|---|---|
| DTOs | Generated from `schema.yml` — never handwritten |
| Status values | Sourced only from the Core Porting specification |
| Traceability | Every ported behavior includes a source citation |

Citation formats I use:

- `← Source <path>`
- `← PS §...`
- `← BR-###`

Beyond repository rules, I also try to:

- ✅ Match existing naming conventions
- ✅ Keep comments minimal and meaningful
- ✅ Make small, focused changes instead of massive rewrites

---

## 4️⃣ Verify Everything

After implementation comes verification.

I run the relevant module tests:







bash

mvn -pl test





Locally I usually include `-am`, since Liquibase is disabled and schema changes need to be applied first.

Because this repository doesn't currently have independent QA, I also:

- Verify that all tests pass
- Run mutation tests if coverage is uncertain
- Exercise the runtime flow instead of relying only on successful compilation

If something fails, **I report it honestly** rather than hiding the failure. 🛠️

---

## 5️⃣ Finish Cleanly

Before considering the work complete, I:

- Reference the requirement or rule IDs implemented
- Update `status.md` only after owner approval
- Commit and push only when requested
- Create an ADR if I intentionally deviate from established conventions

If there's an exception, it should be **documented — not silently introduced**.

---

## 🔁 The Entire Workflow







plaintext

Read the specification



Read the conventions



Understand the existing code



Plan the implementation



Write minimal changes



Test and verify



Report results honestly









Following this process helps me write code that integrates naturally with the existing codebase, minimizes regressions, and makes future maintenance much easier.

---

*If you follow a similar workflow (or have tweaks that work better for your team), I'd love to hear about it in the comments!* 👇


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