Identify and Prioritize Technical Debt

A founder called me in panic mode. His lead engineer just quit, citing “I can’t work in this codebase anymore.” Three weeks later, his senior frontend dev gave notice too. Same reason.

When I audited their code, I found functions with 1,200 lines, zero tests, and comments like “TODO: fix this hack later” from two years ago. A simple feature change required modifying 15 different files.

The worst part? They didn’t know which debt to fix first. They had a list of 200 issues and no idea where to start.

Here’s how to identify what technical debt you actually have and what to tackle first.

What Technical Debt Actually Is

Think of it like a financial loan. You borrow time now by shipping faster with shortcuts and pay interest later with slower development and more bugs.

Examples I see constantly:

• “We’ll add tests after we validate the feature” - spoiler: you won’t
• Copy-pasting that function “just this once” - now it’s in 12 places
• “Let’s hardcode this for now” - it’s still hardcoded 8 months later
• Using React 16 because upgrading “isn’t urgent” - now you’re 3 major versions behind
• “We don’t need documentation yet” - new developer takes 6 weeks to onboard

Not All Debt Is Bad

Here’s what most people miss: some technical debt is strategic.

Strategic debt: “We’re shipping this MVP fast to validate, we’ll refactor if it works”

• Deliberate choice
• Documented
• Has a payback plan
• Worth the tradeoff

Accidental debt: “Oops, I didn’t know there was a better way”

• Team learning and growing
• Happens to everyone
• Fix gradually as you learn

Reckless debt: “We don’t have time for proper code”

• No consideration of consequences
• Creates massive problems
• Destroys companies

The first one is fine. The last one kills startups.

One founder told me: “We took on strategic debt to ship fast. Then we never paid it back. Two years later, it became reckless debt.”

The Four Types of Technical Debt

Use this framework to categorize every piece of debt:

Reckless-Deliberate

“We don’t have time for design”

Characteristics:

• Knowingly cutting corners without plan
• No documentation of shortcuts taken
• No intention to fix later
• Highest risk

Action: Fix immediately or it will compound

Example: Storing passwords in plain text because “encryption takes too long to implement”

Reckless-Inadvertent

“What’s layering?”

Characteristics:

• Team doesn’t know better practices exist
• No awareness of the problems being created
• High risk if not addressed

Action: Schedule soon, invest in training

Example: Building a monolithic 50,000-line file because team doesn’t know about modular architecture

Prudent-Deliberate

“We must ship now, we’ll deal with consequences”

Characteristics:

• Conscious decision with business justification
• Documented and tracked
• Plan to address later
• Medium risk

Action: Planned paydown after validation

Example: Hardcoding config values to ship MVP faster, with ticket to make it configurable

Prudent-Inadvertent

“Now we know how we should have done it”

Characteristics:

• Learned better approach after shipping
• Team skill improved over time
• Lowest risk

Action: Address opportunistically when touching that code

Example: Early code uses callbacks, team now knows async/await is better

How to Find Your Technical Debt

You can’t fix what you don’t know about. Here’s how to inventory it:

1. Code Audit

Spend 2-3 hours reviewing your codebase for:

Code smells:

• Functions over 100 lines
• Files over 500 lines
• Duplicated code in 3+ places
• Deep nesting (4+ levels)
• Comments like “TODO”, “HACK”, “FIXME”
• Complex conditionals (5+ if statements)

Missing fundamentals:

• Zero tests or under 20% coverage
• No error handling
• No logging
• Hard-coded credentials or config
• No documentation

One startup had 347 TODOs in their code. They tracked zero of them. Most were over a year old.

2. Developer Survey

Ask your engineers:

• What part of the codebase do you dread working in?
• What takes way longer than it should?
• What breaks most often?
• What would you rewrite if you had time?
• What makes onboarding new developers hard?

The answers reveal where the pain is.

3. Metrics Analysis

Track these over time:

• Deployment frequency: Going down? Debt might be the cause
• Lead time for changes: Taking longer? Debt slowing you down
• Change failure rate: Increasing? Debt causing bugs
• Time to restore service: Growing? Debt makes debugging hard
• Bug count in production: Rising? Debt accumulating

One startup’s deployment frequency dropped from daily to weekly over 6 months. Technical debt was crushing velocity.

4. Incident Analysis

Review your last 10 production incidents:

• How many were caused by technical debt?
• Same areas keep breaking?
• Patterns in what goes wrong?

One company found 7 of their last 10 incidents traced to one poorly designed module. That became their top priority.

5. Onboarding Friction

Time how long it takes new engineers to:

• Set up local development environment
• Understand the architecture
• Make their first meaningful contribution
• Feel confident deploying to production

Good: 1-2 weeks for first contribution Warning sign: 4-6 weeks Crisis: 8+ weeks or engineers quit during onboarding

How to Prioritize What to Fix First

You can’t fix everything. Here’s how to prioritize:

Priority 1: Critical Debt Blocking New Features

If debt prevents you from shipping what customers need, fix it immediately.

Example: Your auth system is so fragile that adding OAuth takes 3 weeks instead of 3 days.

Action: Stop new features, fix the blocker, then resume

Priority 2: Debt Causing Frequent Bugs or Incidents

If the same area keeps breaking in production, prioritize fixing the root cause.

Example: Your payment processing has 5 incidents in 3 months, all from the same messy 800-line function.

Action: Refactor that function before the next incident

Priority 3: Debt Slowing Development Significantly

If debt makes features take 3x longer than they should, address it.

Example: Adding a new API endpoint requires changing 12 files and 3 databases because of tight coupling.

Action: Decouple systems incrementally

Priority 4: Easy Wins

Low effort, high impact fixes. Great for building momentum.

Example:

• Upgrade dependency with security vulnerability - 30 minutes
• Add README to confusing repo - 1 hour
• Extract duplicated function into shared util - 2 hours

Action: Sprinkle these into sprints for morale

Priority 5: Everything Else

Nice to have but not urgent. Address opportunistically.

Example: Using callbacks instead of async/await. Works fine, just not ideal.

Action: Fix when you’re touching that code anyway

The Prioritization Matrix

Use this to score each piece of debt:

Impact (1-5):

• How much does this slow development?
• How often does this cause bugs?
• How many engineers does this affect?

Effort (1-5):

• How long to fix? (1 = hours, 5 = months)
• How risky is the change?
• How much code needs changing?

Priority Score = Impact / Effort

Example:

• Messy payment code: Impact 5, Effort 3, Score = 1.67 (high priority)
• Old dependency: Impact 2, Effort 1, Score = 2.0 (easy win)
• Ugly admin UI: Impact 1, Effort 4, Score = 0.25 (low priority)

Focus on scores above 1.0.

How to Communicate Debt to Non-Technical People

Your CEO or investors don’t care about “code smells” or “tight coupling.” Translate debt into business terms:

Translate to Business Impact

Instead of: “We have high cyclomatic complexity in our checkout flow”

Say: “Our checkout code is fragile. We’ve had 3 production incidents in 2 months. Each incident costs us $15K in lost sales and 20 hours of engineer time. Refactoring it would take 1 week and prevent future incidents.”

Show the Velocity Impact

Create a chart showing:

• Features shipped per month over last year
• Declining trend as debt accumulates
• Projection if debt isn’t addressed

One startup showed their CEO they went from shipping 12 features/month to 4 features/month over 18 months. That got buy-in for a debt reduction sprint.

Calculate the Dollar Cost

Quantify the cost:

• Engineer hours spent on debt-related bugs: $X
• Lost deals due to missing features: $Y
• Customer churn from reliability issues: $Z
• Recruiting challenges from bad reputation: hard to quantify but real

Total debt cost: $X + $Y + $Z per quarter

Compare to cost of fixing it: usually 10-20% of the total cost.

Use the Credit Card Analogy

“Technical debt is like credit card debt. We borrowed time to ship fast, now we’re paying high interest (slow development, bugs). If we don’t make payments, we go bankrupt (can’t ship anything, engineers quit). We need to dedicate 15% of our capacity to paying down the principal.”

Non-technical founders get this immediately.

The 2026 Technical Debt Landscape

New types of debt have emerged:

AI/ML Integration Debt

Poorly integrated AI features that are:

• Expensive to run at scale
• Slow and unpredictable
• Hard to improve or debug
• Tightly coupled to specific models

For strategies on AI integration, see AI Integration for Post-Seed Startups.

Dependency Hell

NPM packages, Python libraries, and APIs constantly updating and breaking compatibility. One startup had 47 outdated dependencies with known security vulnerabilities.

Security Debt

Rapidly changing security requirements creating legacy vulnerabilities. What was acceptable in 2024 fails compliance in 2026.

For security prioritization, see Security Best Practices by Stage.

Multi-Cloud Complexity

Technical debt from managing multiple cloud providers without proper orchestration. Great for redundancy, nightmare for maintenance.

Red Flags That Debt Is Out of Control

Watch for these warning signs:

• Simple features take weeks to implement
• Frequent production incidents
• Engineers refuse to work in certain parts of the codebase
• High engineer turnover citing code quality
• Failed deployments are common
• New hires take months to be productive
• Investors raising concerns in due diligence

If you see 3 or more of these, you have a debt crisis.

For due diligence preparation, see How to Pass Technical Due Diligence.

Create a Debt Register

Maintain a living document tracking all significant debt:

Debt register should include:

• Description of the debt
• Type (Reckless/Prudent, Deliberate/Inadvertent)
• Impact score (1-5)
• Effort to fix (1-5)
• Priority score (Impact/Effort)
• Business impact in dollars if known
• Date identified
• Owner (who’s responsible)
• Status (not started, in progress, resolved)

Update it monthly. Review it in planning meetings.

One startup’s debt register had 50 items. They fixed the top 10 over 6 months and velocity improved 40%.

The Bottom Line

You can’t fix technical debt if you don’t know what you have or what matters most.

The process:

1. Audit your codebase (code review, developer survey, metrics)
2. Categorize each debt (reckless vs prudent, deliberate vs inadvertent)
3. Prioritize by impact and effort
4. Translate to business terms for stakeholders
5. Track in a debt register
6. Fix systematically, not randomly

Most startups know they have debt but don’t know where it is or what to fix first. That’s why engineers quit and features slow to a crawl.

Identifying and prioritizing debt is the first step to managing it successfully.

Need help auditing your technical debt? Book a call to discuss a systematic approach to identifying and prioritizing what matters most.