# Design Document: Forecast Burndown Chart

## Overview

This feature adds a per-metric forecast burndown chart to the CCP Metrics page. It combines historical compliance data (from `compliance_snapshots`) with forward-looking remediation projections (derived from `compliance_items` resolution dates) into a single stacked bar + line chart. A metric selector allows users to switch between individual compliance metrics (e.g., 2.3.5, 2.3.6, 5.2.5).

The design separates concerns into:
1. A **pure helper function** (`computeMetricForecastBurndown`) for testable forecast computation
2. Two **API endpoints** (forecast-burndown data + metrics-list) added to the existing VCL multi-vertical router
3. A **React chart component** using recharts `ComposedChart` with `Bar` + `Line` + `ReferenceLine`

Key design decisions:
- **No caching**: The API queries `compliance_items` directly on each request so edits on the AEO Compliance page are immediately reflected.
- **Pure computation**: The forecast logic is a stateless helper function that accepts pre-fetched data, making it testable in isolation without database mocks.
- **Existing patterns**: Routes use the `createVCLMultiVerticalRouter` factory pattern; the chart component follows the same inline-style + recharts patterns as `AggregatedBurndownChart` and `TrendChart`.

## Architecture

```mermaid
flowchart TD
    subgraph Frontend
        MS[MetricSelector] -->|selected metric_id| FBC[ForecastBurndownChart]
        FBC -->|fetch| API1[GET /metric/:metricId/forecast-burndown]
        MS -->|fetch on mount| API2[GET /metrics-list]
    end

    subgraph Backend
        API1 --> RH1[Route Handler]
        API2 --> RH2[Route Handler]
        RH1 -->|query| DB[(PostgreSQL)]
        RH1 -->|compute| HF[computeMetricForecastBurndown]
        RH2 -->|query| DB
    end

    subgraph Database
        DB --> CI[compliance_items]
        DB --> CS[compliance_snapshots]
    end
```

The data flow:
1. On page load, `MetricSelector` fetches `/api/compliance/vcl-multi/metrics-list` to populate the dropdown.
2. When a metric is selected, `ForecastBurndownChart` fetches `/api/compliance/vcl-multi/metric/:metricId/forecast-burndown`.
3. The route handler queries `compliance_items` for active devices and `compliance_snapshots` for historical data, then passes both to `computeMetricForecastBurndown`.
4. The helper returns structured data that the frontend renders as a `ComposedChart`.

## Components and Interfaces

### Backend: Helper Function

**File:** `backend/helpers/vclHelpers.js`

```javascript
/**
 * Computes per-metric forecast burndown from device records and historical snapshots.
 *
 * @param {Array<{hostname: string, resolution_date: string|null}>} currentDevices
 *   Active non-compliant devices for the metric
 * @param {number} totalAssets
 *   Total device count in scope for this metric (from snapshot or summary)
 * @param {Array<{month: string, total_assets: number, non_compliant: number, compliance_pct: number}>} historicalSnapshots
 *   Pre-computed historical data points (up to 4 months)
 * @returns {{
 *   historical: Array<{month: string, total_assets: number, non_compliant: number, compliance_pct: number}>,
 *   forecast: Array<{month: string, total_assets: number, non_compliant: number, compliance_pct: number}>,
 *   current_snapshot: {total_assets: number, non_compliant: number, compliant: number, compliance_pct: number, blockers: number, with_dates: number}
 * }}
 */
function computeMetricForecastBurndown(currentDevices, totalAssets, historicalSnapshots) { ... }
```

### Backend: API Endpoints

**File:** `backend/routes/vclMultiVertical.js` (added to existing router)

| Method | Path | Auth | Description |
|--------|------|------|-------------|
| GET | `/api/compliance/vcl-multi/metric/:metricId/forecast-burndown` | `requireAuth()` | Returns combined historical + forecast data for a metric |
| GET | `/api/compliance/vcl-multi/metrics-list` | `requireAuth()` | Returns list of metrics with active non-compliant device counts |

**Forecast Burndown Response Shape:**
```json
{
  "metric_id": "2.3.5",
  "historical": [
    { "month": "2025-01", "total_assets": 500, "non_compliant": 45, "compliance_pct": 91.0 },
    { "month": "2025-02", "total_assets": 510, "non_compliant": 38, "compliance_pct": 92.5 }
  ],
  "forecast": [
    { "month": "2025-04", "total_assets": 520, "non_compliant": 25, "compliance_pct": 95.2 },
    { "month": "2025-05", "total_assets": 520, "non_compliant": 15, "compliance_pct": 97.1 }
  ],
  "current_snapshot": {
    "total_assets": 520,
    "non_compliant": 30,
    "compliant": 490,
    "compliance_pct": 94.2,
    "blockers": 8,
    "with_dates": 22
  }
}
```

**Metrics List Response Shape:**
```json
[
  { "metric_id": "2.3.5", "device_count": 30 },
  { "metric_id": "2.3.6", "device_count": 12 },
  { "metric_id": "5.2.5", "device_count": 45 }
]
```

### Frontend: Components

**File:** `frontend/src/components/pages/CCPMetricsPage.js` (added to existing file)

1. **`MetricSelector`** — Dropdown component that fetches and displays available metrics. Triggers forecast data fetch on selection change.

2. **`ForecastBurndownChart`** — Renders a `ComposedChart` with:
   - Blue `Bar` for `total_assets` (left Y-axis)
   - Orange `Bar` for `non_compliant` (left Y-axis, stacked appearance)
   - Green `Line` for `compliance_pct` (right Y-axis, 0–100%)
   - `ReferenceLine` as vertical divider between historical and forecast sections
   - Forecast data points rendered at 50% opacity

## Data Models

### Database Tables (existing, no schema changes required)

**`compliance_items`** (relevant columns):
| Column | Type | Description |
|--------|------|-------------|
| hostname | TEXT | Device hostname |
| metric_id | TEXT | Compliance metric identifier (e.g., "2.3.5") |
| status | TEXT | `'active'` or `'resolved'` |
| resolution_date | DATE | Planned remediation date (nullable) |
| vertical | TEXT | Organizational vertical (e.g., "NTS_AEO") |
| team | TEXT | Team assignment |

**`compliance_snapshots`** (relevant columns):
| Column | Type | Description |
|--------|------|-------------|
| snapshot_month | TEXT | Month in YYYY-MM format |
| vertical | TEXT | Organizational vertical |
| total_devices | INTEGER | Total devices in scope |
| compliant | INTEGER | Compliant device count |
| non_compliant | INTEGER | Non-compliant device count |
| compliance_pct | NUMERIC(5,2) | Compliance percentage |

### Internal Data Structures

**Chart data point (shared between historical and forecast):**
```javascript
{
  month: "2025-04",        // YYYY-MM
  total_assets: 520,       // Total devices in scope
  non_compliant: 25,       // Non-compliant count
  compliance_pct: 95.2,    // Percentage (0-100, 1 decimal)
  isForecast: true         // Frontend flag for opacity styling
}
```

**Current snapshot:**
```javascript
{
  total_assets: 520,
  non_compliant: 30,
  compliant: 490,
  compliance_pct: 94.2,
  blockers: 8,             // Devices with no resolution_date
  with_dates: 22           // Devices with a resolution_date
}
```

### Query Patterns

**Metrics list query:**
```sql
SELECT metric_id, COUNT(DISTINCT hostname) AS device_count
FROM compliance_items
WHERE status = 'active' AND vertical IS NOT NULL
GROUP BY metric_id
ORDER BY metric_id ASC
```

**Active devices for a metric:**
```sql
SELECT hostname, resolution_date, vertical
FROM compliance_items
WHERE metric_id = $1 AND status = 'active' AND vertical IS NOT NULL
```

**Historical snapshots for a vertical (3 months prior):**
```sql
SELECT snapshot_month AS month, total_devices AS total_assets,
       non_compliant, compliance_pct::numeric AS compliance_pct
FROM compliance_snapshots
WHERE vertical = $1 AND snapshot_month >= $2 AND snapshot_month < $3
ORDER BY snapshot_month ASC
```


## Correctness Properties

*A property is a characteristic or behavior that should hold true across all valid executions of a system — essentially, a formal statement about what the system should do. Properties serve as the bridge between human-readable specifications and machine-verifiable correctness guarantees.*

The `computeMetricForecastBurndown` helper function is a pure function with clear input/output behavior and universal properties that hold across a wide input space. This makes it an ideal candidate for property-based testing.

### Property 1: Forecast structure invariant

*For any* valid `currentDevices` array and non-negative `totalAssets` integer, the `computeMetricForecastBurndown` function SHALL return an object with `historical`, `forecast`, and `current_snapshot` fields, where every element in the `forecast` array contains `month` (YYYY-MM string), `total_assets` (equal to the input `totalAssets` or the current snapshot's total), `non_compliant` (non-negative integer), and `compliance_pct` (number between 0 and 100), and all `total_assets` values in the forecast array are identical.

**Validates: Requirements 1.4, 3.1, 3.6**

### Property 2: Blocker and with_dates partition invariant

*For any* valid `currentDevices` array and non-negative `totalAssets` integer, the `computeMetricForecastBurndown` function SHALL produce a `current_snapshot` where `blockers + with_dates = non_compliant`.

**Validates: Requirements 3.2**

### Property 3: Compliance percentage formula correctness

*For any* valid inputs where `totalAssets > 0`, the `computeMetricForecastBurndown` function SHALL compute `compliance_pct` for each forecast and current_snapshot data point as `ROUND((total_assets - non_compliant) / total_assets * 100, 1)`. When `totalAssets` is 0, all `compliance_pct` values SHALL be 0.

**Validates: Requirements 3.3, 3.10**

### Property 4: Forecast non_compliant monotonicity

*For any* valid `currentDevices` array and non-negative `totalAssets` integer, the `forecast` array produced by `computeMetricForecastBurndown` SHALL have monotonically non-increasing `non_compliant` values (each month's `non_compliant` is less than or equal to the previous month's `non_compliant`).

**Validates: Requirements 3.4**

### Property 5: Per-month non_compliant computation correctness

*For any* valid `currentDevices` array and non-negative `totalAssets` integer, for each month in the `forecast` array, the `non_compliant` value SHALL equal the count of devices whose `resolution_date` is after that month (not yet remediated) plus the count of devices with no `resolution_date` (blockers).

**Validates: Requirements 3.8**

### Property 6: Forecast horizon bound

*For any* valid `currentDevices` array and non-negative `totalAssets` integer, the `forecast` array produced by `computeMetricForecastBurndown` SHALL contain at most 12 elements, and SHALL terminate either when all devices with resolution dates are projected to be remediated or at the 12-month maximum, whichever comes first.

**Validates: Requirements 1.9, 3.9**

### Property 7: Past-due resolution dates treated as current month

*For any* device in `currentDevices` whose `resolution_date` is in a month that has already passed relative to the current date, the `computeMetricForecastBurndown` function SHALL treat that device as projected to be remediated in the current month (i.e., it is excluded from `non_compliant` in the first forecast month if that month is after the current month, but remains in `non_compliant` for the current month's snapshot).

**Validates: Requirements 6.5**

## Error Handling

### Backend API Errors

| Scenario | HTTP Status | Response Body | Behavior |
|----------|-------------|---------------|----------|
| Unauthenticated request | 401 | `{ "error": "Unauthorized" }` | `requireAuth()` middleware rejects |
| Invalid/unknown metricId | 200 | `{ metric_id, historical: [], forecast: [], current_snapshot: {zeros} }` | Graceful empty response |
| Database query failure | 500 | `{ "error": "Failed to compute forecast burndown" }` | Catch block logs error, returns 500 |
| Database failure on metrics-list | 500 | `{ "error": "Failed to fetch metrics list" }` | Catch block logs error, returns 500 |

### Frontend Error States

| Scenario | UI Behavior |
|----------|-------------|
| Metrics list fetch fails | Inline error with AlertCircle icon, red border, error message text |
| Forecast data fetch fails | Inline error in chart area with AlertCircle icon and error description |
| Empty metrics list | "No metrics with active non-compliant devices" message |
| Empty forecast + empty historical | "No data available for this metric" message in chart area |
| Race condition (rapid metric switching) | Discard stale responses; only render data from the most recent selection |

### Helper Function Edge Cases

| Input Condition | Behavior |
|-----------------|----------|
| `currentDevices` is empty | Return empty `forecast`, zeroed `current_snapshot` (except `total_assets`) |
| `totalAssets` is 0 | All `compliance_pct` values are 0 (avoid division by zero) |
| `totalAssets` < `currentDevices.length` | Use `currentDevices.length` as `non_compliant` without clamping |
| All devices are blockers (no resolution dates) | Return empty `forecast` array (no month-by-month projection possible) |
| All resolution dates are in the past | Treat as remediated in current month; forecast shows only blockers remaining |

## Testing Strategy

### Property-Based Tests (via fast-check)

The `computeMetricForecastBurndown` helper function is a pure function with well-defined invariants, making it ideal for property-based testing. Each property test runs a minimum of 100 iterations with randomly generated inputs.

**Library:** `fast-check` (already available in the project's test dependencies based on existing `.property.test.js` files)

**Test file:** `backend/__tests__/forecast-burndown-chart.property.test.js`

**Generator strategy:**
- `currentDevices`: Array of `{ hostname: arbitraryString, resolution_date: oneOf(null, arbitraryFutureDate, arbitraryPastDate) }`
- `totalAssets`: Non-negative integer (including 0 and values less than device count)
- `historicalSnapshots`: Array of 0–4 elements with valid month strings and non-negative counts

Each test is tagged with: `Feature: forecast-burndown-chart, Property {N}: {title}`

Properties to implement:
1. Forecast structure invariant
2. Blocker/with_dates partition invariant
3. Compliance percentage formula correctness
4. Forecast non_compliant monotonicity
5. Per-month non_compliant computation correctness
6. Forecast horizon bound
7. Past-due resolution dates treated as current month

### Unit Tests (example-based)

**Test file:** `backend/__tests__/forecast-burndown-chart.test.js`

- Specific examples with known inputs and expected outputs
- Edge cases: empty devices, zero totalAssets, all blockers, all past-due dates
- Integration with route handlers (mocked database)

### Integration Tests

- Verify API endpoints return correct response shapes with seeded database data
- Verify authentication middleware is applied
- Verify correct SQL filtering (vertical scoping, active-only devices)
- Verify historical data derivation from `compliance_snapshots`

### Frontend Tests

- Component rendering with mock data (snapshot tests)
- Loading and error states
- Metric selector interaction (selection triggers fetch)
- Race condition handling (rapid metric switching discards stale responses)