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To reduce technical debt without stopping feature work, you need three things. First, a written list of the debt you actually carry. Second, a way to measure what it costs you each week. Third, a fixed slice of every sprint to pay it down, starting with the code you change most. Most startups do not need a rewrite. They need to stop paying interest on the few parts of the system that every change has to pass through.
This guide explains how to measure technical debt, the types that hurt startups most and the step-by-step way we pay it down. It is also how we structure a source code audit: find the debt, estimate what it costs, rank it and fix the expensive parts first.
What is technical debt?
Technical debt is the extra effort every future change costs you because of a shortcut taken earlier in the design or the code. The shortcut itself is the principal. The slower changes, the workarounds and the incidents it causes are the interest, and you keep paying that interest until you fix the shortcut.
Ward Cunningham introduced the metaphor in his 1992 experience report on the WyCash system. In his telling, shipping first-time code is like borrowing, and a little debt speeds you up as long as you pay it back promptly. Researchers at Dagstuhl Seminar 16162 later gave it a working definition. It covers design or implementation choices that are expedient in the short term but make future changes more costly or impossible. They mainly hurt maintainability and evolvability.
Two distinctions matter in practice. Debt is not the same as bugs: users see bugs, while debt stays invisible until someone tries to change the code. And debt can be deliberate or accidental. "We will support one currency at launch" is a decision you can record and revisit. A module that quietly grew until everything depends on it is an accident, and accidents are the debt that surprises you.
The types of technical debt that hurt startups most
Teams talk about technical debt as one thing, but the types of technical debt need different fixes. These are the ones we check first in a startup codebase, in roughly the order they cause damage.
- Test debt. No automated tests around the paths that make money: signup, checkout and billing. Every release becomes a manual check or a gamble, so releases slow down and bunch up.
- Architecture debt. One module, service or database table that everything depends on. A small change in one feature ripples into three others, and estimates stop meaning anything.
- Dependency and platform debt. Frameworks, runtimes and databases at or past end of life. The OWASP Top 10:2025 ranks software supply chain failures third, and that category includes software that is vulnerable, unsupported or out of date. Our software end-of-life tracker lists the dates that matter for common startup stacks.
- Delivery debt. Manual deploys, no staging environment, no rollback and no alerting. Only one person can release, and every release is stressful.
- Security debt. Secrets committed to the repository, endpoints that never check who is asking and admin tools left open. Broken access control is first on the same OWASP list. This debt is common in vibe coding apps, where the code was generated faster than anyone could review it.
- Knowledge debt. One person understands the billing code, nothing is written down and there are no runbooks. This debt comes due the week that person goes on vacation or leaves.
- Data debt. Schema shortcuts, two sources of truth for the same customer, and data changed by hand instead of through migrations.
Technical debt examples from startup codebases
These technical debt examples are hypothetical, but each one follows a common pattern.
- For example, a seed-stage marketplace might store prices as floating-point numbers to ship faster. A year later, refund reports are off by a few cents and finance reconciles them by hand every month.
- A team that built its first version with an AI coding tool might have every page query the database directly with no access checks. Adding team accounts now means touching every page.
- A SaaS app pinned to an old framework version might need a workaround for every new library. The upgrade gets bigger each month the team waits.
- A startup might deploy with a script that lives on one engineer's laptop. Nothing goes wrong until that laptop is unavailable on launch day.
The cost of technical debt
The cost of technical debt shows up as time first. A 2018 Stripe survey run with Harris Poll covered more than 1,000 developers and more than 1,000 C-level executives in five countries. The developers estimated that 13.5 hours of a 41.1-hour work week went to technical debt. That is about a third of engineering time spent on interest. If your team matches that average, a four-person team spends more than one full engineer's time standing still.
At the scale of an economy the numbers get large. The 2022 report from the Consortium for Information & Software Quality estimated accumulated technical debt in US software at about $1.52 trillion. The same report estimated the cost of poor software quality at $2.41 trillion or more.
For a startup, the bill arrives in specific places. Features take far longer than they should, and incidents repeat in the same part of the system. New engineers need weeks before their first safe change. Investors find the problems during tech due diligence and factor them into the round.
How to measure technical debt
You cannot manage debt you only feel. To measure technical debt, combine three kinds of signals: how fast and safely you deliver, what the code itself shows and what the team tells you. Then write the results into one register that product and engineering both read.
Delivery signals
DORA is the research program behind the State of DevOps reports. It now tracks five software delivery performance metrics: change lead time, deployment frequency, failed deployment recovery time, change fail rate and deployment rework rate. You do not need a dashboard product to start. Pull the numbers from your repository and deploy history once a month. Look for the parts of the system where lead time and change fail rate climb together. That is where the interest is being paid.
Code signals
Static analysis gives you a rough price. SonarQube's documentation defines technical debt as the sum of the estimated effort to fix maintainability issues. Its technical debt ratio divides that effort by the estimated cost of writing the code, which it sets at 30 minutes per line by default. By default, a ratio of 5% or less earns an A rating, and 50% or more earns an E. Treat the figure as a trend line, not a verdict, because the tool cannot see architecture problems or missing tests on critical paths.
Add three checks the tools miss. Find hotspots: files that change often and are hard to read, which your git history will show you. Measure test coverage on the paths that make money, not across the whole codebase. And list every dependency version against its end-of-life date, along with any known vulnerabilities.
Team signals
Ask how long a new engineer takes to ship a first safe change and how much of each sprint goes to unplanned work. Ask which parts of the code people avoid. "Nobody touches the billing module" is a measurement. Write it down.
The debt register
Put everything into one list with the same columns, so items can be compared and ranked. Here is an example register for a hypothetical seed-stage SaaS team.
| Item | Interest (what it costs now) | Principal (effort to fix) | Risk if ignored | Decision |
|---|---|---|---|---|
| No tests on checkout | Every release needs a manual checkout check | Days | A broken checkout goes unnoticed | Fix this sprint |
| Deploy script on one laptop | Only one person can release | Days | Releases stop when that person is away | Fix this sprint |
| Framework at end of life | Library upgrades blocked, security patches ending | Weeks, in stages | Unpatched vulnerabilities | Plan for next quarter |
| Prices stored as floats | Finance fixes refund reports by hand | Days, plus a data migration | Wrong invoices | Fix with the next billing change |
| Old admin reports | Slow to load, rarely used | Weeks | Low | Leave for now |
How to reduce technical debt without stopping feature work
The way to reduce technical debt is to pay the most expensive interest first, in small steps that ship alongside features. A big-bang rewrite stops delivery, restarts the debt clock and puts all the risk into one release. This is the order we work in.
- Write the register in one sitting. Engineers list the debt they hit in the last month, and whoever owns the roadmap adds what slowed recent launches. Give every item an interest, a principal and a risk.
- Rank by interest and change frequency. Ugly code that nobody changes can wait. Debt in code you change every week goes to the top, because you pay its interest every week.
- Build the safety net first. Add tests around the money paths, a CI pipeline that runs them, one-command deploys with rollback and error tracking. Every later fix becomes cheaper and safer.
- Reserve a fixed budget. We suggest starting with a set share of every sprint, for example one day in ten per engineer. Add a rule that each change leaves the code it touches a little cleaner. Fund large items, such as a framework upgrade, as their own small projects.
- Refactor in small, reversible steps. Change the code behind tests and ship each step on its own. For a component that needs replacing, use the Strangler Fig pattern. Put a facade in front of the old part and move one route or feature at a time to the new one. Retire the old code once nothing calls it.
- Keep dependencies moving. Small, regular upgrades are cheap, and rare big ones are not. Dependabot version updates open pull requests automatically once you add a dependabot.yml file to the repository.
- Re-measure every month. Report progress in terms the business feels: lead time in the checkout area, releases per week, incidents in billing. "We refactored the payments module" means little to a board. "Checkout changes now ship the same day" means a lot.
Technical debt management that keeps working
Paying debt down once is a project. Technical debt management is the habit that stops it piling up again, and it comes down to a few rules.
- Record deliberate debt when you take it on. When the team takes a shortcut on purpose, log it in the register with an owner and a date to revisit. Deliberate debt with a due date is a tool. Debt nobody wrote down is the kind that grows quietly.
- Make tests part of the definition of done. New code on a money path ships with tests, or it does not ship.
- Review the register at planning. Product and engineering decide together, because product owns the roadmap that the interest slows down.
- Give it an owner. A senior engineer or CTO keeps the register honest. If you do not have one, software development team augmentation can add that senior capacity without a full-time hire.
- Write the budget down. Google's SRE organization has an advertised goal of keeping operational work, or toil, below 50% of each SRE's time. Debt work benefits from the same idea: an explicit budget that rises when the measurements get worse and falls when they improve.
When an outside review helps
A register written by the team that built the system has blind spots, because people stop seeing what they live with every day. An outside review earns its cost at a few predictable moments.
- Before fundraising or an acquisition, when investors will run their own review anyway.
- After building quickly with AI coding tools, before real customer data goes in.
- Before hiring your first engineers, so they inherit a map of the system instead of a maze.
- When incidents keep landing in the same area and nobody can say why.
At BrainStormers, Thanos Gkanas, our Head of Engineering, leads this work. The output is a ranked fix list that covers security, architecture, tests and delivery risk, with each item sized in effort so you can plan it into sprints. The point is not a grade. It is knowing which few fixes buy back the most time.
Frequently asked questions
What is technical debt?
Technical debt is the extra effort every future change costs because of an earlier shortcut in the design or the code. The shortcut is the principal, and the slower changes, workarounds and incidents it causes are the interest. Ward Cunningham coined the metaphor in 1992. Like a loan, it is useful when taken on deliberately and repaid on time.
What are the main types of technical debt?
The main types are code debt, architecture debt, test debt, dependency and platform debt, delivery or infrastructure debt, security debt, knowledge or documentation debt and data debt. In startups, missing tests on the money paths, a tangled core module and outdated dependencies usually do the most damage. Every change and release has to pass through them.
Which signs show technical debt is growing?
Small features start taking longer, change lead time and change fail rate rise in the same parts of the code, and releases bunch up because each one feels risky. Other signs are incidents that repeat in one area, new engineers needing weeks for a first safe change and code that the team openly avoids touching.
What does technical debt cost a startup?
Mostly engineering time. In Stripe's 2018 developer survey, developers estimated that 13.5 hours of a 41.1-hour week went to technical debt, about a third of their time. For a startup, that time comes straight out of the roadmap. Debt also causes incidents, slows onboarding and weakens your position when investors find it during due diligence.
Is all technical debt bad?
No. Deliberate debt that is recorded, small and short-lived is a reasonable trade, especially in an MVP, where learning fast matters more than perfect code. Debt becomes a problem when nobody wrote it down, when it sits in code you change every week, or when it touches security and customer data.
How much time should a team spend paying down technical debt?
There is no universal number, so start with a fixed, written budget, for example one day in ten per engineer, and adjust it from your measurements. Raise it when lead time and change fail rate climb in the same area, and lower it when they improve. Fund large items such as framework upgrades as separate projects.
Sources and update history
- Ward Cunningham, The WyCash Portfolio Management System (OOPSLA 1992 experience report)
- Dagstuhl Seminar 16162: Managing Technical Debt in Software Engineering (2016)
- Stripe, The Developer Coefficient (September 2018)
- CISQ, The Cost of Poor Software Quality in the US: A 2022 Report
- DORA, software delivery performance metrics
- SonarQube Server documentation, Metrics definition
- OWASP Top 10:2025
- Microsoft Azure Architecture Center, Strangler Fig pattern
- GitHub Docs, Dependabot version updates
- Google SRE book, Chapter 5: Eliminating Toil
Update history
- First published.
