By Glue Team
Technical debt starts invisible and dies unmeasured.
A system is built. It works. A decision is made for speed: we'll optimize later. "Later" never comes. The debt compounds silently. Years later, you realize velocity is 50% lower than it should be.
This doesn't have to happen. The solution is systematic tracking through the full lifecycle.
Stage 1: Discovery
Debt starts when it's identified, not when it exists.
Discovery happens through:
- Code review. "This code is hard to understand. It needs refactoring."
- Production incidents. "This system went down because of cascading failures. Architecture needs work."
- Engineering retrospectives. "It took 3x longer to implement Feature X than Feature Y. Why? Architectural coupling."
- Performance analysis. "This endpoint is slow. Data model is inefficient."
None of these are automatic. They require engineering discipline.
Best practice: have a monthly retrospective. Ask: "What slowed us down this month? What systems are getting worse?" Document it.
Stage 2: Documentation
Once debt is identified, document it.
What to document:
- What is the debt? (Be specific. Not "code quality is bad." But "auth service has 40% test coverage, making refactoring risky.")
- Where is it? (Which system? Which files?)
- How much time does it cost? (5 hours per month? 20%?)
- What's the risk? (Is this blocking features? Causing instability? Slowing everyone?)
- How old is it? (New debt is often acceptable. 3-year-old debt is not.)
Document in a system everyone can see. Not a hidden wiki. Not a Slack conversation. A central debt register.
Stage 3: Prioritization
You have debt. You have limited capacity. What do you fix first?
Prioritization framework:
High urgency + High cost = Fix first
- Blocking critical features? High urgency.
- Costing 20% of team time? High cost.
- Fix it now. Block time. Refactor.
High urgency + Low cost = Quick win
- Blocking critical features? High urgency.
- Costing 2% of team time? Low cost.
- Fix it in a sprint or two.
Low urgency + High cost = Plan quarterly
- Not blocking features? Low urgency.
- Costing 15% of team time? High cost.
- Allocate a sprint to it next quarter.
Low urgency + Low cost = Ignore or defer
- Not blocking features? Low urgency.
- Costing 1% of team time? Low cost.
- Not worth the effort. Ignore.
Stage 4: Planning and Estimation
Once you've decided to fix debt, plan it properly.
Estimate:
- How long will refactoring take?
- How many engineers?
- What's the risk? (Can we do this incrementally or is it a big bang?)
- What's the payoff? (Will this free up 10% of capacity? 20%?)
Make the investment case explicit. Tell your product team. "We're spending 2 weeks to reduce architecture debt. Payoff: 1 FTE freed up. We'll ship more features long-term."
Stage 5: Execution
This is where most teams fail. They plan refactoring but never execute.
Execution best practices:
Protect the time. Don't let other work creep in.
Go incremental. Don't do a big rewrite. Extract one subsystem, ship it, move to next.
Write tests first. Before you refactor, add test coverage. Now you can safely refactor.
Measure as you go. Track progress. "Week 1: Extracted payment module. Week 2: Refactored auth layer." Make progress visible.
Ship early. Deploy the refactored piece. Don't wait until everything is done.
Stage 6: Verification
After refactoring, verify it worked.
Measure:
- Cycle time: Did it decrease? (Lower is better)
- Velocity: Did it increase? (More points completed per sprint)
- Bug rate: Did it decrease?
- Time spent on debt in that system: Did it drop?
If you don't see improvement, you refactored the wrong thing. Learn and adjust.
If you see improvement, quantify it. "Architecture refactoring freed up 1 FTE (40 hours per month). That translates to $60K annually."
Stage 7: Prevention
Once you've fixed debt, prevent new debt from accumulating.
- Code review standards. Is code review catching debt early?
- Architectural decisions. When engineers propose new systems, do they think about architectural debt?
- Monitoring. Are you tracking debt continuously so new debt doesn't hide for years?
Continuous Cycle
Debt tracking isn't a one-time activity.
Month 1: Discover new debt through retrospectives and code review. Month 2: Document and prioritize. Quarter 1: Execute on top 3 debt items. End of Q1: Verify improvements. Quarter 2: Plan next round of refactoring based on learnings.
Repeat quarterly.
After 2-3 quarters, you've eliminated the worst debt. After a year, your team's velocity has increased 30-40%. The investment pays for itself.
With Glue
With Glue, you can automate discovery. Glue scans your codebase and identifies:
- Complex hotspots (code quality debt)
- Knowledge silos (knowledge debt)
- Architectural constraints (architecture debt)
- Testing gaps (testing debt)
Instead of discovering debt through retrospectives (slow), you see it automatically. Then you follow the same lifecycle: document, prioritize, plan, execute, verify.
Frequently Asked Questions
How often should we go through this cycle? Monthly discovery. Quarterly planning and execution. Annually verify ROI.
What if refactoring doesn't improve metrics? You refactored the wrong thing. Learn and move to different debt.
How do we prevent debt from accumulating again? Continuous monitoring. Use tools to alert you when complexity grows or test coverage drops. Address issues when they're small.