# Com / Optage

## Overview

The `com.optage` area appears to be a documentation and extraction subsystem centered on turning Optage source conventions into structured metadata. Based on the child module evidence, this package is less about runtime business logic and more about analyzing source files, identifying annotations and headers, and packaging the results into simple model objects that other tooling can consume.

At a high level, the system seems to do two things:
- **Parse** Optage-flavored source content and detect documentation signals such as headers, business labels, test indicators, and change markers.
- **Model** those findings as lightweight records so they can be logged, rendered, compared, or passed into downstream reporting.

The available evidence suggests that the parser and model modules are complementary halves of the same pipeline: the parser discovers and classifies source metadata, while the model module provides the data containers used to hold that output.

## Sub-module Guide

### `com.optage.model`

The `model` module contains the data structures that represent extracted documentation and classification results. It includes types such as:
- `BusinessLabel` for field-level business annotations
- `ChangeMarker` for versioned source change annotations
- `FileHeader` and nested `RevisionEntry` for file-level metadata and revision history
- `TestInfo` for test-class classification results
- `OptageCombinedTarget` as a sample target that combines several extraction patterns

This module does not appear to implement extraction logic itself. Instead, it acts as the shared vocabulary for anything that wants to describe Optage source metadata in a structured way.

### `com.optage.parser`

The `parser` module is not available in the child wiki evidence here, but its role can be inferred from the model module and the parent package scope. It appears to be the consumer-facing counterpart to `model`: it likely reads source files, recognizes Optage documentation conventions, and emits instances of the model classes.

In other words, the parser probably produces the objects, while the model module defines their shape. The two modules are therefore tightly coupled by data flow even if the current evidence does not show direct imports or resolved dependencies.

### Relationship between the sub-modules

The relationship is best understood as a pipeline:
1. `parser` inspects source text and identifies relevant metadata.
2. It creates `model` objects to normalize those findings.
3. Downstream code reads those objects to build documentation, reports, or validation output.

`OptageCombinedTarget` in the model module looks especially important as a synthetic fixture. It appears to exist so parser logic can be tested against a single file containing multiple kinds of extractable signals.

## Key Patterns and Architecture

### Data extraction followed by structured modeling

The dominant pattern is a two-stage flow:
- **Detection stage** in the parser: scan source text for conventions such as file headers, revision histories, Japanese business labels, and versioned change comments.
- **Representation stage** in the model layer: store each detected item as a simple object with getters and diagnostic `toString()` output.

This separation keeps parsing concerns away from documentation representation concerns. That is helpful when the same extracted data needs to be rendered in different ways or reused by multiple tools.

### Record-style objects

The model classes are intentionally small and mostly constructor/getter based. They appear to function like record-style DTOs rather than rich domain entities. This makes them easy to serialize, log, and pass through intermediate analysis steps.

### Mixed-signal sample input

`OptageCombinedTarget` appears to be a deliberately mixed example that combines field labels and change markers in one file. That suggests the architecture expects real-world source files to contain multiple overlapping documentation conventions, and the parser must be able to distinguish them without confusing one type of metadata for another.

```mermaid
flowchart TD
Parser["parser"] --> HeaderScan["File header detection"]
Parser --> LabelScan["Business label detection"]
Parser --> MarkerScan["Change marker detection"]
Parser --> TestScan["Test classification"]
HeaderScan --> FileHeader["FileHeader"]
FileHeader --> RevisionEntry["RevisionEntry"]
LabelScan --> BusinessLabel["BusinessLabel"]
MarkerScan --> ChangeMarker["ChangeMarker"]
TestScan --> TestInfo["TestInfo"]
Parser --> SampleTarget["OptageCombinedTarget"]
SampleTarget --> LabelScan
SampleTarget --> MarkerScan
```

## Dependencies and Integration

The indexed evidence does not show resolved package dependencies, source imports, or external framework bindings for `com.optage`. That said, the child documentation strongly implies a few integration points:

- The parser likely depends on the model types to return structured results.
- `FileHeader` depends on `List<RevisionEntry>` to carry revision history.
- `TestInfo` depends on `List<String>` to preserve detection signals.
- `OptageCombinedTarget` appears to integrate multiple extraction scenarios into one sample source file.

From a system perspective, this package seems to sit near the boundary between raw source code and generated documentation. It probably connects upstream to source scanning and downstream to wiki/report generation, even though those neighboring packages are not visible in the current evidence.

## Notes for Developers

- Treat the `model` classes as shared contracts. If you change their shape, any parser or renderer built on them may need to change too.
- The design appears intentionally conservative: simple data holders, no obvious business logic, and `toString()` support for diagnostics.
- The presence of Japanese label and purpose fields suggests this area preserves source-language meaning rather than translating it away.
- `OptageCombinedTarget` looks like a fixture, not a production domain object. Use it as a parser-validation sample unless broader code evidence says otherwise.
- Because no direct parser source was available in the child docs, some relationships are inferred. The documentation should therefore be read as an architectural summary rather than a verified implementation map.
- If you extend this area, keep the distinction clear between extracted metadata, classification evidence, and sample inputs. That separation seems central to how the package is organized.