How much of your 2027 engineering budget will go to maintaining code your team has already shipped?
For most companies the answer is a large and growing share. Teams are shipping more code, and much of it is written by AI. In Stack Overflow’s 2026 Developer Survey of more than 30,000 developers, 80% of daily AI users spend at least an hour a day using AI, and 67% of respondents use it mainly to write code in areas they already know.
More code means more tech debt unless the team changes how it works. This guide covers how to reduce technical debt in six steps: measure it, prioritize it, budget for refactoring, review code at the speed it arrives, modernize legacy systems in increments, and automate the checks that keep new debt out.
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Step 1: Start by measuring the technical debt you already have
Ward Cunningham, later a co-author of the Agile Manifesto, introduced the technical debt metaphor in 1992 to describe the future cost of shipping code that reflects an incomplete understanding of the problem. Today, the term also covers other shortcuts and design decisions that make software harder to maintain or change.
The main types of technical debt are:
- Code debt: Duplicated logic, high code complexity, inadequate testing.
- Architectural debt: Modules that depend on everything else.
- Documentation debt: Decisions nobody wrote down.
- Infrastructure debt: Outdated runtimes and skipped technology upgrades.
Much of it is unintentional technical debt, created by shortcuts nobody noticed.
Technical debt management starts with code quality numbers you can track every quarter: test coverage, duplicate blocks, complexity scores, dependency age, and time in review. Log each item in Jira or your existing tracker with a rough monthly cost, and treat the list as backlog, not a wiki page nobody opens.
Step 2: Prioritize debt by business cost, not by annoyance
Not all tech debt deserves attention. A module nobody has touched in three years may cost you very little. One your team changes every sprint costs you on every change, so fix it first.
Score each item on two things: how often the code changes and how expensive each change is. Martin Fowler’s technical debt quadrant helps because it separates deliberate debt, chosen for a reason, from inadvertent debt, which nobody chose.
A written rationale helps teams understand why deliberate debt was accepted, but it doesn’t make that debt harmless. Prioritize items based on their ongoing maintenance cost, risk and impact on planned work.
Managing technical debt this way gives product managers a number to weigh against feature work.
Step 3: Budget refactoring into every sprint
Refactoring is the daily work of reducing technical debt, and the first work cut when a deadline arrives. A June 2026 review of 104 academic and industry sources found that LLM-assisted development amplifies existing code, design, and documentation debt. Its authors named fast-integration debt, generated code merged for speed before anyone weighs the maintenance cost, as the pattern that sets off the others.
Teams that refactor consistently reserve capacity for it. A common practice in Scrum teams is to hold back 15% to 20% of each sprint for debt work and treat it like any other commitment in sprint planning.
Whichever model you choose, write it down and report on it. A refactoring budget nobody tracks gets spent on bug fixes within two sprints.
Step 4: Review code at the speed it arrives
Regular code reviews are where tech debt gets caught or waved through, and AI has given reviewers more to catch. Stack Overflow’s 2025 Developer Survey of more than 49,000 developers found that 66% cite almost-right AI solutions as their top frustration, and 45% say debugging AI-generated code takes longer than writing it. In the 2026 survey, 48% trust AI output only when the task is low risk and they can easily verify it.
Unreviewed AI code becomes debt the day it merges. Keep pull requests small enough to read in one sitting. Let a linter enforce coding standards so reviewers can focus on design and code quality. Pair programming suits AI-heavy changes because two people end up understanding code neither typed.
Margaret-Anne Storey, a professor at the University of Victoria, recommends teams establish cognitive debt mitigation strategies, such as that no AI-generated change ships until one person can fully explain it. Record why each change was made, not only what changed, which is the cheapest way to reduce documentation debt.
Step 5: Modernize legacy systems in increments
Legacy code carries the oldest and most expensive debt in many codebases, and the urge to rewrite it is usually wrong. A full rewrite of a monolithic application can stall feature delivery for a year and bring back bugs the old system fixed.
Replace it in pieces instead. Put the monolith behind a stable interface, pull out one module at a time, and route traffic to it once its automated tests pass. The facade and adapter design patterns exist for exactly this.
Step 6: Automate the checks that keep new debt out
The cheapest technical debt to manage is debt you prevent from entering the codebase. That means automated testing and static analysis on every change, run in your CI/CD pipelines before review.
Security shows why. An April 2026 study ran 500 security-critical prompts through seven widely used models and found that AI output contained at least one vulnerability in 55.8% of 3,500 code samples they tested. Explicit security instructions in the prompt lowered that rate by only four points. Code that compiles is not code that is safe.
Set a test coverage floor and fail the build when a change drops below it. Add mutation testing to catch unit tests that assert nothing. Continuous integration only reduces debt if the build is allowed to fail.
How Modularis helps teams manage technical debt
Most technical debt comes from building fast without a process that protects code quality. AI code generation on its own makes software development faster, not better.
Modularis 19, a modular AI code generation platform for commercial products, is built around that problem. Deterministic, model-driven generation produces the structural foundation, roughly 80% of the code, and LLMs help engineers with the customer-facing 20%. The result is a stable, serviceable codebase rather than another layer of unreviewed output on an old one.
For products that already carry heavy debt, the Tech360 Assessment maps what exists and what it will cost to fix, and Modularis architects transfer their knowledge to your engineers at no extra cost, so your team stays self-sufficient.
Over 26 years, Modularis has launched 79 products with zero failures, supported more than 500,000 IoT devices, and created over $285 million in exit value.
Book a demo to see how Modularis 19 fits your roadmap.