Effective Practices for Measuring and Managing Technical Debt

Technical debt is a concept familiar to many software developers: the idea that suboptimal solutions or shortcuts taken during development can result in challenges down the road. This metaphor likens it to financial debt, where the “interest” is paid over time through the cost of maintaining or updating substandard code. Managing this debt is crucial for maintaining long-term productivity and system health. Here, we’ll explore techniques and practices to effectively define, measure, and manage technical debt.

Defining Technical Debt

Technical debt refers to the accumulation of issues in a codebase or development process that hinder productivity and require rework. It can take many forms, but engineers frequently point to a few common problems:

  • Incomplete or missing documentation that makes it difficult to understand how systems work or how to use APIs.
  • Poor test coverage or unreliable tests that make changes risky and lead to frequent rollbacks.
  • Quick-and-dirty code written under tight deadlines, often intended as temporary but never improved.
  • Outdated code that hasn’t kept up with evolving standards or technologies, making it harder to maintain or integrate.
  • Legacy migrations that were started but never completed, leaving teams to maintain redundant systems or workflows.

While not all technical debt immediately impacts productivity, these kinds of issues often compound over time, slowing down development and increasing the cost of future changes.

Engineers typically face a handful of these issues at a time, and it is key to identify which categories are most disruptive to productivity.

Measuring Technical Debt

While technical debt can be difficult to quantify precisely, there are practical indicators that suggest where it may be accumulating. Engineers often “know it when they see it”—a codebase that feels fragile, frustrating, or overly complex usually has some form of debt lurking beneath the surface.

Observable signs of technical debt include:

  • A high number of TODO or FIXME comments scattered throughout the code.
  • Frequent bugs or regressions after changes, especially when tied to fragile areas with little or outdated test coverage.
  • Long onboarding times for new team members due to unclear code structure or missing documentation.
  • High churn in specific parts of the codebase, where repeated edits suggest instability or poor design.
  • Dependencies on outdated libraries or frameworks that are difficult to update without significant effort.

Engineering teams can also monitor technical debt through patterns in development activity. For example, if seemingly simple changes require significant time or coordination across teams, it may indicate underlying complexity or architectural misalignment. Build times, deployment delays, and the frequency of hotfixes can also reflect the hidden costs of debt.

Though some forms of debt are subtle or deeply embedded, experienced developers usually have a strong intuition for where it exists. By paying attention to these signals—and validating them with code reviews, historical trends, and team feedback—organizations can identify and prioritize the most impactful areas for improvement.

Managing Technical Debt

The key to managing technical debt is adopting a systematic approach that integrates it into the development process. Teams should balance immediate delivery pressures with the long-term health of their systems. Effective management practices involve:

  1. Deliberate Debt Incurment: Teams should make conscious decisions about when it’s appropriate to take on technical debt, understanding that some level of debt can accelerate delivery.
  2. Tracking and Paying Down Debt: Teams must continuously track technical debt and allocate resources to pay it down. Regularly assessing the severity and impact of different debt categories helps prioritize remediation efforts.
  3. Proactive Debt Management: Teams should avoid letting debt accumulate by embedding technical debt management practices into the workflow. This includes maintaining documentation, improving testing, and refactoring code as necessary.
  4. Frameworks and Maturity Models: To institutionalize technical debt management, teams can use frameworks and maturity models. These tools help assess the current state of debt management practices and guide teams toward better strategies. The levels range from reactive, where teams occasionally address debt, to structural, where teams embed debt management within the overall development process.

By using these practices, teams can better assess when taking on technical debt is a calculated risk and when it’s time to pay it off. The goal isn’t to eliminate all technical debt—after all, it can sometimes be a deliberate choice that helps meet business goals more quickly—but to minimize its negative impact on productivity.