Feature prioritization methods

interface FeatureRequest {
  id: string;
  title: string;
  source: 'jira' | 'github' | 'linear' | 'customer';
  tags: string[];
  metadata: Record<string, unknown>;
}

interface ScoringFramework {
  name: string;
  calculate(request: FeatureRequest, weights: ScoringWeights): number;
}

interface ScoringWeights {
  reach: number;
  impact: number;
  confidence: number;
  effort: number;
  technicalDebtMultiplier?: number;
}

class JiraAdapter {
  async fetchUnprioritized(): Promise<FeatureRequest[]> {
    // Mock implementation: in production, use Jira REST API
    // Filter by label: "needs-prioritization"
    // Map custom fields to reach, impact, confidence, effort
    return [];
  }
}

class RICEFramework implements ScoringFramework {
  name = 'RICE';

  calculate(request: FeatureRequest, weights: ScoringWeights): number {
    const reach = Number(request.metadata.reach) || 0;
    const impact = Number(request.metadata.impact) || 0;
    const confidence = Number(request.metadata.confidence) || 0.5;
    const effort = Number(request.metadata.effort) || 1;

    const rawScore = (reach * 

impact * confidence) / effort; const debtAdjustment = weights.technicalDebtMultiplier ? (request.tags.includes('tech-debt') ? weights.technicalDebtMultiplier : 1) : 1;

return rawScore * debtAdjustment * weights.reach;

} }

**4. Build the Ranking Pipeline**
Orchestrate ingestion, scoring, and output. The pipeline should be idempotent, support batch processing, and emit events for downstream systems.

```typescript
class PrioritizationEngine {
  private framework: ScoringFramework;
  private weights: ScoringWeights;

  constructor(framework: ScoringFramework, weights: ScoringWeights) {
    this.framework = framework;
    this.weights = weights;
  }

  async rank(features: FeatureRequest[]): Promise<FeatureRequest[]> {
    const scored = features.map(f => ({
      ...f,
      priorityScore: this.framework.calculate(f, this.weights)
    }));

    return scored.sort((a, b) => b.priorityScore - a.priorityScore);
  }

  async syncAndRank(adapter: JiraAdapter): Promise<void> {
    const raw = await adapter.fetchUnprioritized();
    const ranked = await this.rank(raw);
    
    // Emit to event bus or update issue tracker labels
    console.log(`Ranked ${ranked.length} features. Top: ${ranked[0]?.title}`);
  }
}

5. Integrate with CI/CD & Backlog Management The ranked output must drive engineering action. Automate label assignment (p0, p1), generate sprint drafts, and gate PR merges against priority tags. Use GitHub Actions or GitLab CI to enforce that only features above a threshold score enter the active development pipeline.

Architecture Decisions & Rationale

• Plugin-based scoring: Frameworks evolve. Hardcoding RICE locks teams into static math. Adapters allow WSJF, Kano, or custom ROI models to drop in without pipeline rewrites.
• Config-driven weights: Business priorities shift. Storing weights in environment variables or a config service enables real-time recalibration without code deployments.
• Technical debt multiplier: Pure RICE penalizes infrastructure work. A multiplier ensures debt reduction scores competitively, preventing architectural decay.
• Event-driven output: Decoupling ranking from issue tracker updates prevents API rate limits and enables multi-tool sync (Jira, Linear, GitHub Projects).
• Observability hooks: Log score distributions, framework switches, and adoption deltas. Prioritization is only as good as its feedback loop.

Pitfall Guide

1. Treating scoring as static Weights and frameworks degrade as product strategy shifts. A RICE configuration tuned for growth phase fails in retention phase. Best practice: quarterly weight reviews tied to OKR shifts, with automated alerts when score distributions flatten.

2. Ignoring technical debt in the algorithm Pure impact/reach scoring starves maintenance work. Engineering teams accumulate interest until deployments stall. Best practice: apply a configurable debt multiplier or reserve a fixed capacity bucket (e.g., 20%) for non-feature work.

3. Over-indexing on reach without feasibility validation High-reach features with unvalidated technical assumptions cause timeline blowouts. Best practice: gate scoring with a feasibility flag from engineering leads. Features without architecture approval receive a confidence penalty.

4. Tooling without process alignment Deploying a scoring engine without cross-functional buy-in creates shadow backlogs. Product, engineering, and design must agree on weight definitions. Best practice: document weight calibration sessions and tie them to quarterly planning.

5. Skipping validation loops Shipping ranked features without measuring adoption breaks the feedback cycle. Best practice: instrument every deployed feature with telemetry hooks. Feed 30-day adoption rates back into the confidence field for future scoring.

6. Not accounting for opportunity cost Prioritization matrices rarely model what gets delayed. Best practice: maintain a blocked-by graph. When a high-score feature depends on low-score infrastructure, the engine should surface the dependency chain, not just rank independently.

7. Decoupling prioritization from deployment metrics A ranked backlog means nothing if CI/CD pipelines ignore it. Best practice: enforce pipeline gates. PRs without a valid priority label or below-threshold score require explicit override justification.

Production Bundle

Action Checklist

• Define scoring schema: Map issue tracker fields to reach, impact, confidence, effort
• Select baseline framework: Start with RICE; plan WSJF or custom adapter for later
• Configure technical debt multiplier: Set initial value (1.2–1.5) based on architecture review
• Build ingestion adapter: Implement webhook or scheduled sync for your issue tracker
• Deploy ranking pipeline: Containerize engine; expose ranked output via API or event bus
• Integrate with CI/CD: Enforce priority labels; gate PR merges against threshold
• Instrument telemetry: Add adoption tracking to every shipped feature
• Schedule calibration: Quarterly weight review tied to OKR and adoption data

Decision Matrix

Scenario	Recommended Approach	Why	Cost Impact
Early-stage startup	Ad-Hoc/Consensus + Lightweight RICE	Speed over precision; rapid hypothesis testing	Low tooling cost; high context-switch cost
Scaling SaaS product	Weighted Scoring (RICE/WSJF)	Balanced throughput and predictability; reduces backlog debt	Medium engineering overhead; high ROI stabilization
Mature platform with telemetry	Telemetry-Driven Algorithmic	Data-calibrated scoring aligns delivery with user behavior	High initial integration cost; lowest rollback rate
Heavy infrastructure team	Debt-Aware Weighted Scoring	Prevents architectural decay; maintains deployment velocity	Medium scoring complexity; high stability gain
Regulated/compliance domain	Rule-Gated + Confidence Penalty	Ensures auditability; penalizes unvalidated assumptions	Low automation; high compliance safety

Configuration Template

{
  "scoring": {
    "framework": "RICE",
    "weights": {
      "reach": 1.0,
      "impact": 1.5,
      "confidence": 0.8,
      "effort": 1.0,
      "technicalDebtMultiplier": 1.3
    },
    "thresholds": {
      "p0": 85,
      "p1": 60,
      "p2": 35
    }
  },
  "ingestion": {
    "adapter": "jira",
    "filters": {
      "labels": ["needs-prioritization"],
      "status": ["Open", "Backlog"]
    },
    "syncInterval": "0 */6 * * *"
  },
  "output": {
    "target": "github-projects",
    "labels": {
      "p0": "priority/critical",
      "p1": "priority/high",
      "p2": "priority/medium"
    },
    "events": true
  }
}

Quick Start Guide

1. Install dependencies: npm i @codcompass/prioritization-core axios (or your preferred HTTP client for issue tracker APIs)
2. Configure weights: Copy the JSON template to config/prioritization.json. Adjust thresholds and debt multiplier to match your team's current capacity.
3. Run ingestion: Execute the sync script via cron or CI scheduler. Verify that unranked issues are normalized into FeatureRequest objects.
4. Deploy ranking pipeline: Start the engine container. Confirm ranked output emits events or updates issue tracker labels automatically.
5. Validate feedback loop: Ship a batch of p0/p1 features. After 30 days, pull adoption telemetry and recalibrate the confidence weights. Iterate quarterly.

Prioritization is not a meeting. It's a pipeline. When scoring is automated, calibrated against production data, and enforced through delivery gates, engineering teams stop guessing and start shipping measurable impact. Implement the engine, enforce the thresholds, and let telemetry dictate the backlog.