Tech Debt Tracker

The agent identifies, scores, prioritizes, and tracks technical debt across codebases using AST parsing, cost-of-delay analysis, and trend dashboards.

Workflow

1. Scan codebase -- Run the Debt Scanner against the target repository. It uses AST parsing and pattern matching to detect debt signals across all six categories (code, architecture, test, documentation, dependency, infrastructure).
2. Score each item -- Apply the Severity Scoring Framework. Rate each item on velocity impact, quality impact, productivity impact, and business impact (1-10 each). Estimate effort (XS-XL) and risk level.
3. Calculate interest rate -- For each item, compute Interest Rate = Impact Score x Frequency of Encounter per sprint. Calculate Cost of Delay = Interest Rate x Sprints Until Fix x Team Size Multiplier.
4. Prioritize -- Plot items on the Cost-of-Delay vs Effort matrix. Assign priority: Immediate (high cost, low effort), Planned (high cost, high effort), Opportunistic (low cost, low effort), Backlog (low cost, high effort).
5. Allocate sprint capacity -- Apply the Debt-to-Feature Ratio based on current team velocity. Reserve the recommended percentage for debt work.
6. Generate reports -- Produce the Executive Dashboard (health score, trend, top risks, investment recommendation) and the Engineering Dashboard (daily new/resolved, interest rate by component, hotspots).
7. Track trends -- Compare current scan against previous baselines. Alert if debt accumulation rate exceeds paydown rate for two consecutive sprints.

Debt Classification

Category	Key Indicators	Detection Method
Code	Functions > 50 lines, nesting > 4 levels, cyclomatic complexity > 10, duplicate blocks > 3	AST parsing, complexity metrics
Architecture	Circular dependencies, tight coupling, missing abstraction layers, monolithic components	Dependency analysis, coupling metrics
Test	Coverage < 80% on critical paths, flaky tests, test suite > 10 min	Coverage reports, failure pattern analysis
Documentation	Missing API docs, outdated READMEs, no ADRs, stale comments	Coverage analysis, freshness checking
Dependency	Known CVEs, deprecated APIs, unused packages, version conflicts	Vulnerability scanning, usage analysis
Infrastructure	Manual deploys, missing monitoring, env inconsistencies, no DR plan	Audit checklists, config drift detection

Severity Scoring Framework

Rate each dimension 1-10:

Dimension	1-2	5-6	9-10
Velocity Impact	Negligible	Affects some features	Blocks new development
Quality Impact	No defect increase	Moderate defect increase	Critical reliability problems
Productivity Impact	No team impact	Regular complaints	Causing developer turnover
Business Impact	No customer impact	Moderate performance hit	Revenue-impacting issues

Effort sizing: XS (1-4 hrs), S (1-2 days), M (3-5 days), L (1-2 weeks), XL (3+ weeks)

Interest Rate and Cost of Delay

Interest Rate = Impact Score x Frequency of Encounter (per sprint)
Cost of Delay = Interest Rate x Sprints Until Fix x Team Size Multiplier

Example:
  Legacy auth module with poor error handling
  Impact: 7  |  Frequency: 15 encounters/sprint  |  Team: 8 devs
  Planned fix: sprint 4 (3 sprints away)

  Interest Rate = 7 x 15 = 105 points/sprint
  Cost of Delay = 105 x 3 x 1.2 = 378 total cost points

Prioritization Matrix

Quadrant	Cost of Delay	Effort	Action
Immediate (quick wins)	High	Low	Do first
Planned (major initiatives)	High	High	Schedule dedicated sprints
Opportunistic	Low	Low	Fix when touching related code
Backlog	Low	High	Reconsider quarterly

WSJF Alternative

WSJF = (Business Value + Time Criticality + Risk Reduction) / Effort

Each component scored 1-10. Highest WSJF items are prioritized first.

Sprint Allocation (Debt-to-Feature Ratio)

Team Velocity	Debt %	Feature %	Strategy
< 70% of capacity	60%	40%	Remove major blockers
70-85% of capacity	30%	70%	Balanced maintenance
> 85% of capacity	15%	85%	Opportunistic only

Sprint planning rule: Reserve 20% of sprint capacity for debt. Prioritize items with the highest interest rates. Add "debt tax" to feature estimates when working in high-debt areas.

Debt Item Data Structure

{
  "id": "DEBT-2024-001",
  "title": "Legacy user authentication module",
  "category": "code",
  "subcategory": "error_handling",
  "location": "src/auth/legacy_auth.py:45-120",
  "description": "Authentication error handling uses generic exceptions",
  "impact": { "velocity": 7, "quality": 8, "productivity": 6, "business": 5 },
  "effort": { "size": "M", "risk": "medium", "skill_required": "mid" },
  "interest_rate": 105,
  "cost_of_delay": 378,
  "priority": "high",
  "status": "identified",
  "tags": ["security", "user-experience", "maintainability"]
}

Status lifecycle: Identified > Analyzed > Prioritized > Planned > In Progress > Review > Done | Won't Fix

Refactoring Strategies

Strategy	When to Use	How It Works
Strangler Fig	Large monoliths, high-risk migrations	Build new around old; gradually redirect traffic; remove old
Branch by Abstraction	Need old + new running in parallel	Create interface; implement both behind it; switch via config
Feature Toggles	Gradual rollout of refactored components	Add toggle at decision points; test both paths; remove old
Parallel Run	Critical business logic changes	Run both implementations; compare outputs; build confidence

Executive Dashboard

TECH DEBT HEALTH
  Overall Score: [0-100]  |  Trend: [improving/declining]
  Cost of Delayed Fixes: [X development days]
  High-Risk Items: [count]

MONTHLY REPORT:
  1. Executive Summary (3 bullet points)
  2. Health Score Trend (6-month view)
  3. Top 3 Risk Items (business impact focus)
  4. Investment Recommendation (resource allocation)
  5. Success Stories (debt resolved last month)

Engineering Dashboard

DAILY:
  New items identified  |  Items resolved  |  Interest rate by component

SPRINT REVIEW:
  Debt points completed vs planned  |  Velocity impact
  Newly discovered debt  |  Team code quality sentiment

Example: Scanning a Python Microservice

# Run debt scanner
python scripts/debt_scanner.py --repo ./payment-service --output debt_inventory.json

# Output summary:
#   Total items found: 47
#   Critical: 3  |  High: 8  |  Medium: 21  |  Low: 15
#
#   Top 3 by cost-of-delay:
#     1. DEBT-001: payment_processor.py - nested exception handling (CoD: 420)
#     2. DEBT-002: db/migrations/ - 12 unapplied migrations (CoD: 315)
#     3. DEBT-003: tests/ - 62% coverage on payment flow (CoD: 280)

# Prioritize items
python scripts/debt_prioritizer.py --inventory debt_inventory.json --sprint-capacity 40

# Generate executive report
python scripts/debt_dashboard.py --inventory debt_inventory.json --baseline previous_scan.json

Quarterly Planning

1. Identify 1-2 major debt themes per quarter
2. Allocate dedicated sprints for large-scale refactoring
3. Plan debt work around major feature releases
4. Track: debt interest rate reduction, velocity improvements, defect rate reduction, code review cycle time

Scripts

Debt Scanner (debt_scanner.py)

Scans codebase using AST parsing and pattern matching. Detects all six debt categories. Outputs structured JSON inventory.

Debt Prioritizer (debt_prioritizer.py)

Analyses debt inventory using cost-of-delay and WSJF frameworks. Outputs prioritized backlog with sprint allocation recommendations.

Debt Dashboard (debt_dashboard.py)

Generates trend reports comparing current scan against baselines. Produces executive and engineering dashboard views.

References

See REFERENCE.md for the complete Technical Debt Quadrant (Fowler), detailed detection heuristics per category, and implementation roadmap phases.