Technical debt: how to measure it and reduce it sustainably

Ward Cunningham, who coined the technical debt metaphor in 1992, originally meant a deliberate, conscious trade off: moving fast with imperfect code to validate an idea, then paying back that debt once the learning was in hand. The term has since broadened into a much looser usage, covering both chosen shortcuts and unintended mistakes alike: duplicated code, missing tests, outdated dependencies. Like financial debt, it generates interest. The longer it accumulates without being addressed, the more expensive each new feature becomes to build.

The main forms of technical debt

Type	Example
Code debt	Duplicated, poorly structured code, with no shared conventions
Test debt	Lack of automated tests, insufficient coverage on critical features
Architecture debt	Technology choices that no longer fit the product's current scale
Documentation debt	Undocumented knowledge, dependent on a handful of key people
Dependency debt	Outdated or unmaintained libraries, unpatched security flaws

How to measure your technical debt

Track the time spent on fixes and maintenance relative to new feature development.
Measure automated test coverage on the application's critical scope.
List outdated or unmaintained dependencies.
Ask teams which areas of the code they avoid touching for fear of regressions.

Best practices to reduce it sustainably

Allocate dedicated, recurring time to reducing debt, rather than waiting for a complete overhaul.
Prioritize the debt that actually slows down development, rather than aiming for perfect code everywhere.
Strengthen test coverage before refactoring, to make changes safer.
Document architecture decisions at the time they are made, not after the fact.

A concrete example

A technology SME notices that every new feature takes twice as long as expected. A diagnostic reveals that a central module, built quickly several years earlier, was never automatically tested and accounts for most of the fixes. Rather than rewriting it entirely, the team progressively adds tests before each change, which reduces the risk of regressions and, within a few months, restores speed across the whole team.

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Aiming for zero technical debt is, in fact, a questionable goal: some shortcuts remain rational when the code in question has a short lifespan or limited stakes. The real skill lies in distinguishing debt worth paying back from debt better left consciously in place, rather than chasing a uniform code cleanliness that is expensive to maintain everywhere.