By Arjun Mehta
Technical debt is invisible until you measure it.
You feel it. Code is slower to change. Bugs multiply. Engineers complain. But you can't point to it. You can't show the business impact. You can't make the investment case.
Without visibility, debt is abstract. With visibility, it's quantifiable. And quantifiable problems get fixed.
Why Visibility Matters
When you can see debt:
- You can prioritize. Fix the high-cost debt first.
- You can measure impact. "This debt is costing us 40 hours per month."
- You can make the business case. "Invest $100K in refactoring, free up $300K in capacity annually."
- You can track progress. "We reduced debt from 30% to 15% of team time."
- You can prevent. Once you see a debt pattern, you can prevent it from accumulating.
Without visibility, you're flying blind.
What To Make Visible
1. Code Complexity
Which systems are hard to understand? Which are simple?
Metrics:
- Cyclomatic complexity (how many paths through the code)
- Lines per function
- Dependency depth
High complexity = high risk. Engineers are afraid to change it. Bugs hide.
Visualize: "Auth service has 3 modules with high complexity. Payment service has 1."
Focus refactoring on high-complexity systems.
2. Test Coverage
Which systems have good tests? Which are brittle?
Metrics:
- Line coverage
- Branch coverage
- Critical path coverage
Low coverage = high risk. Changes break things. Refactoring is unsafe.
Visualize: "Notification service: 90% coverage. Payment service: 40%."
Prioritize testing in low-coverage critical systems.
3. Knowledge Distribution
Which systems have single-person knowledge? Which are distributed?
Metrics:
- Commits per person per system
- Code review participation
- On-call responsibilities
Single-person systems = knowledge risk. If that person leaves, the system fails.
Visualize: "Alice owns payment system (8 of 10 commits). Bob owns auth (split 6-4 with Carol)."
Prioritize knowledge distribution in high-risk systems.
4. Dependency Health
Which dependencies are outdated? Which are stale?
Metrics:
- Dependency age (last update)
- Security issues
- Usage (is it still needed?)
Old dependencies = risk. Security vulnerabilities. Incompatibilities. Maintenance burden.
Visualize: "Using Rails 5.0 (2 years old, 3 security issues). Spring Framework: latest version."
Prioritize updating security-critical dependencies.
5. Architectural Constraints
Which systems are tightly coupled? Which are modular?
Metrics:
- Cross-service dependencies
- Shared data coupling
- Circular dependencies
Tight coupling = velocity risk. Changes require coordination across teams. Slow iteration.
Visualize: "Payment service depends on Auth, Notifications, User service. Auth is independent."
Prioritize extracting tightly-coupled subsystems.
6. Debt Impact on Velocity
How much is debt costing?
Metrics:
- Time spent on rework (estimated or tracked)
- Time spent on knowledge transfer
- Time spent on firefighting
Impact: "We estimate 25% of team time is spent managing technical debt."
Impact in dollars: "That's $750K per year for a 30-person team."
This is the business case for fixing debt.
How To Build Visibility
Option 1: Manual Tracking
You create a spreadsheet. You update it monthly.
Pros: You understand every line. No magic.
Cons: Takes time. Gets stale. Requires discipline.
Option 2: Automated Tools
You use a tool that analyzes your codebase.
Pros: Automatic. Current. Finds things you'd miss.
Cons: Requires integration. Can have false positives.
Option 3: Hybrid
You use a tool for metrics. You interpret them and make decisions.
This is best. Tools give you data. You add context. You make decisions.
The Debt Visibility Dashboard
Once you have data, put it in a dashboard everyone sees.
Monthly view:
- Code complexity hotspots (top 10)
- Test coverage trends (improving or declining?)
- Knowledge silos (single-person systems)
- Dependency vulnerabilities
- Estimated debt cost (% of team time)
Quarterly view:
- Debt reduction progress
- Velocity trend (is refactoring freeing capacity?)
- Knowledge distribution (is knowledge becoming distributed?)
That dashboard becomes your debt management tool.
Why Glue Fits Here
Glue automatically analyzes your codebase and extracts the visibility you need:
- Code complexity and hotspots
- Knowledge distribution
- Architectural dependencies
- Systems at risk
Instead of building visibility manually, Glue provides it. You focus on decision-making and execution.
Common Mistakes
Mistake 1: Only measuring code metrics. Complexity and coverage are useful. But they don't measure business impact. Measure: how much time is this costing?
Mistake 2: Not making it visible. You track debt but it's in a hidden spreadsheet. Leadership doesn't see it. They don't understand why refactoring matters. Make it visible.
Mistake 3: Measuring but not acting. You have a beautiful debt dashboard. You don't do anything about it. Measure to act. Track to prioritize. Prioritize to fix.
The Payoff
When you have visibility:
- High-cost debt is obvious. You fix it first.
- Progress is measurable. You see improvements.
- Business impact is quantifiable. You can make investment cases.
- Prevention is possible. You catch debt early instead of years later.
Visibility is the foundation of debt management.
Frequently Asked Questions
How often should we update visibility metrics? Monthly at minimum. Weekly is better. Automated tools can provide continuous visibility.
What if our metrics look bad? That's okay. Now you know. Many teams are surprised by how much debt they have. Once they see it, they can fix it.
Can we improve visibility without expensive tools? Yes. Start with manual analysis. Track complexity, coverage, knowledge distribution in a spreadsheet. As you scale, add tooling.