What Is Technical Debt?

Technical debt is the implied cost of future rework caused by choosing a faster or easier solution now instead of a better approach that would take longer. Originally coined by Ward Cunningham in 1992, the metaphor draws a parallel to financial debt: taking shortcuts creates an obligation to "repay" later through refactoring, rewriting, or restructuring. Left unaddressed, technical debt accumulates interest in the form of slower development, more bugs, and increasingly brittle systems.

Technical debt is unavoidable in any software project that operates under real-world time constraints. The problem is not its existence but its invisibility. Teams that do not track or communicate their debt often discover its full weight only when a critical feature takes three times longer than expected or when a routine change triggers a cascade of failures.

A survey by Stepsize found that 52% of engineering teams have missed deadlines due to unaddressed technical debt, and the average developer spends one day per week working around or fixing debt-related issues. These costs rarely appear on product roadmaps, which makes them easy to ignore during planning and difficult to justify when requesting dedicated cleanup sprints.

Making technical debt visible is the first step toward managing it. When debt is tracked alongside feature work, product and engineering leaders can make informed trade-offs rather than discovering constraints only at the worst possible moment.

Technical debt takes many forms. Deliberate debt occurs when a team knowingly ships a quick solution with a plan to revisit it later. Accidental debt accumulates when requirements evolve and previously sound designs no longer fit. Bit rot introduces debt passively as dependencies age, standards shift, and the ecosystem around the code moves forward while the code itself stays still.

Managing technical debt starts with identification. Teams can tag debt items in their issue trackers, maintain a dedicated debt register, or annotate code with structured TODO comments that link to tickets. Some organizations assign a "debt score" to modules based on age, complexity, and incident history, helping prioritize which areas deserve attention first.

Repayment works best as a continuous practice rather than a periodic event. Teams that reserve 15 to 20 percent of each sprint for debt reduction maintain a sustainable pace. Those that defer all cleanup until a "tech debt sprint" often find the backlog has grown beyond what a single sprint can address.

For strategies on surfacing hidden debt, see Technical Debt Visibility.

Issue trackers like Jira and Linear can categorize debt items alongside feature work when teams establish consistent labeling conventions. Static analysis tools flag complexity hotspots and outdated dependencies. Glue helps engineering teams surface the context behind debt by connecting code patterns to the decisions, discussions, and pull requests that created them, making it easier to assess which debt matters most. For reduction strategies, Technical Debt Reduction offers a practical framework, and Technical Debt Tracking covers measurement approaches.

Is all technical debt bad?

No. Deliberate, well-tracked technical debt can be a rational trade-off, especially when speed to market is critical. The danger lies in untracked or invisible debt that accumulates silently. Debt that is acknowledged, documented, and scheduled for repayment is a strategic tool rather than a liability.

How do you convince stakeholders to invest in reducing technical debt?

Frame debt in terms of its business impact: slower feature delivery, increased bug rates, longer onboarding times, and higher incident frequency. Attaching concrete metrics, such as the percentage of sprint capacity consumed by debt-related work, makes the case more persuasive than abstract appeals to code quality.

What is the difference between technical debt and legacy code?

Legacy code is older code that remains in production, often without adequate tests or documentation. Technical debt is the cost of suboptimal decisions regardless of the code's age. New code can carry debt if it was written with known shortcuts. Legacy code frequently carries significant debt, but the two concepts are not synonymous.