What Is Technical Debt and How Do You Pay It Down?

Every software team has heard some version of this: “We can’t add that feature yet. We need to clean up the code first.” To the business, it can sound like an excuse. To developers, it’s a real and growing cost. That cost has a name: technical debt.

This guide explains what technical debt is, where it comes from, the main types, and a practical way to decide which debt to pay down and when, without freezing feature work.

What technical debt means

The term comes from Ward Cunningham. In his 1992 OOPSLA experience report on the WyCash portfolio system, he compared shipping first-time code to going into debt. A little debt can speed development, as long as it’s paid back promptly with a rewrite. The danger is debt that never gets repaid, because every hour spent working around imperfect code is like paying interest.

Researchers later formalized the idea. A 2016 Dagstuhl seminar on managing technical debt, organized by researchers including Ipek Ozkaya of Carnegie Mellon’s Software Engineering Institute, agreed on a working definition: design or implementation choices that are expedient in the short term but make future changes more costly or impossible. The same group described 2 parts:

• Principal: what it would cost now to build a better solution.
• Interest: the extra cost you keep paying while the debt exists, through slower work, extra defects and lower quality.

That framing is the key to managing debt. You don’t need to fix all of it. You need to stop paying high interest.

Key takeaway: Technical debt isn’t automatically bad. It’s a loan. Taken on purpose and repaid quickly, it can help you ship. Ignored, the interest compounds until simple changes take weeks.

How teams end up in debt

Debt usually comes from reasonable decisions under pressure, not carelessness:

• Deadlines. A launch date forces a quick fix that was meant to be temporary.
• Learning. The team now understands the problem better than when they first built it, so the original design no longer fits.
• Changing requirements. The product grew in a direction the architecture never planned for.
• Aging dependencies. Frameworks, libraries and operating systems move on, and your versions fall out of support.
• Missing practices. No automated tests, no code review, no documentation.
• Staff turnover. Knowledge leaves, and nobody dares touch the parts only 1 person understood.

Some debt is deliberate (“we’ll ship this and refactor next quarter”). Some is accidental (“we didn’t know there was a better way”). Both are normal. The problem is debt nobody tracks.

The main types of technical debt

Dependency and security debt deserve special attention because their interest isn’t only slower work. It’s risk. The US Government Accountability Office’s July 2025 report on federal legacy systems found that 8 of the 11 most critical systems it reviewed used outdated languages, 4 had unsupported hardware or software, and 7 were running with known cybersecurity vulnerabilities. That’s what technical debt looks like after decades without paydown.

Signs your debt is getting expensive

You don’t need a dashboard to notice high interest. Watch for:

• Estimates for “simple” changes keep growing.
• The same areas of the code cause bugs again and again.
• Releases need long manual testing or happen rarely because they’re risky.
• New hires take months to become productive.
• Developers avoid certain files or modules entirely.
• Security scans flag the same outdated packages every month.
• Upgrades get postponed because “everything will break.”

If several of these apply, debt is already slowing your business, even if no one has labeled it.

How to measure technical debt

Perfect measurement isn’t possible, but useful measurement is. Combine a few signals:

1. Change hotspots. Files changed most often that also generate the most bugs are where interest is highest. Your version control history shows this.
2. Delivery metrics. Track lead time for changes, deployment frequency, change failure rate and time to restore service. Rising lead times and failure rates often point to debt.
3. Static analysis. Code quality tools flag complexity, duplication and outdated dependencies. Treat their scores as hints, not verdicts.
4. Dependency age and support status. List frameworks and runtimes that are past or near end of support.
5. Developer input. Ask engineers to name the 3 parts of the system that slow them down most. Their answers are often the most accurate data you’ll get.

How to prioritize paydown alongside features

This is where most teams struggle. The answer is to treat debt like any other backlog item: visible, sized and prioritized against business value.

Step 1: build a debt register

Create a simple list, in your issue tracker, of known debt items. For each, note what it is, where it lives and what it’s costing.

Step 2: score each item

Use 3 quick ratings from 1 to 5:

• Interest: How often does this slow us down or cause defects, and how badly?
• Risk: Could this cause a security incident, outage, compliance failure or a forced emergency upgrade?
• Principal: How much effort would the fix take?

Step 3: sort into 4 buckets

Step 4: reserve steady capacity

Big “cleanup quarters” rarely survive contact with the roadmap. A steadier approach is to reserve a fixed slice of every sprint or cycle for debt work, and agree on that slice with product leadership in advance. Pair it with the “leave it better than you found it” rule: when developers touch code for a feature, they tidy that area as part of the work.

Step 5: tie debt to features

The easiest debt to fund is debt blocking something the business wants. If a new checkout flow requires untangling the payment module, the refactor is part of the feature estimate, not a separate request.

How to prevent new debt

Paying down old debt is pointless if you add new debt just as fast. Practices that help:

• Automated tests for critical paths, so changes are safe.
• Code review with agreed standards.
• Continuous integration that runs tests and checks on every change. See what CI/CD is for the basics.
• Dependency update routines, such as automated update pull requests reviewed weekly.
• Definition of done that includes tests and docs, not only working code.
• Architecture decision records, short notes that explain why a design was chosen.
• Security built into the pipeline, covered in our guide to DevSecOps.

Good internal platforms also reduce debt by giving teams paved, well-maintained paths. Our article on platform engineering and developer experience explains how.

Can AI help pay down technical debt?

AI coding assistants can speed up some debt work: explaining unfamiliar code, writing missing tests, suggesting refactors and drafting upgrade changes. They can also create new debt if teams accept generated code they don’t fully understand. Treat AI output like a junior developer’s pull request: review it, test it and keep humans accountable for design decisions. Our guide on how AI coding assistants are changing software teams covers the trade-offs.

Where to start this month

1. Hold a 1-hour session with your developers to list the top 10 debt items.
2. Score them for interest, risk and effort using the steps above.
3. Pick 2 quick wins and finish them in the next sprint.
4. Agree on a standing capacity slice for debt with product leadership.
5. Add debt to planning. Review the register every month, the same way you review the feature backlog.

If you’re budgeting a new build or rebuild, factor maintenance into the total cost from day 1. Our guide to custom software development cost explains why, and the MVP development guide shows how to take on debt intentionally when speed matters most.

Conclusion

Technical debt is a normal part of building software, and some of it is a smart trade. The trouble starts when nobody tracks it and the interest quietly eats your team’s time. Make debt visible, score it by interest and risk, fix the expensive items first and protect a steady slice of capacity for paydown. Do that, and “we need to clean up first” becomes a planned investment instead of a recurring surprise.

Sources

• Ward Cunningham, The WyCash Portfolio Management System (OOPSLA 1992) — origin of the technical debt metaphor
• Dagstuhl Seminar 16162: Managing Technical Debt in Software Engineering — 2016 working definition, principal and interest
• Carnegie Mellon SEI: Managing Technical Debt in Software Engineering — research summary of the seminar
• US GAO, GAO-25-107795: Agencies Need to Plan for Modernizing Critical Decades-Old Legacy Systems — findings on outdated languages, unsupported software and vulnerabilities