Essential Tech Debt Metrics

Highlights: Tech Debt Metrics

  • 1. Code complexity
  • 2. Code duplication
  • 3. Code churn
  • 4. Code coverage
  • 5. Code smells
  • 6. Documentation coverage
  • 7. External dependencies
  • 8. Refactoring backlog
  • 9. Time-to-fix issues
  • 11. Defect density
  • 12. Open issues
  • 13. Architectural complexity
  • 14. API stability
  • 15. Technical debt ratio

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In today’s fast-paced technological landscape, organizations are constantly pushing the limits to deliver high-quality software at breakneck speeds, often leaving behind what is known as “technical debt.” As this invisible threat accumulates, it insidiously erodes the foundation of a project, hindering efficiency, and ultimately jeopardizing its success. To effectively manage and mitigate technical debt, it is crucial to understand and track specific metrics that highlight the areas requiring immediate attention.

In this insightful blog post, we will explore the concept of technical debt, its implications on software development, and delve into the vital tech debt metrics that can help teams make informed decisions, prioritize issues, and maintain the long-term health of their projects.

Tech Debt Metrics You Should Know

1. Code complexity

This metric measures the complexity of the codebase, considering factors like cyclomatic complexity, nested conditionals, and method length. High complexity makes the code harder to understand and maintain, increasing the likelihood of bugs and hindering new feature development.

2. Code duplication

This metric measures how much code is duplicated across the codebase. Duplicate code is harder to maintain, as it requires changes to be made in multiple places, increasing the risk of introducing bugs.

3. Code churn

Code churn measures the rate of change in the codebase over time. High churn indicates frequent changes and instability, often implying that the code is not well-written or poorly designed.

4. Code coverage

This metric measures the percentage of code exercised by automated tests. A low code coverage leads to vulnerabilities in the system, thereby adding to the technical debt.

5. Code smells

Code smells refer to patterns in the source code that may indicate suboptimal design or implementation. These can lead to maintainability issues, an increased likelihood of bugs, and slow development.

6. Documentation coverage

This metric evaluates the quality and completeness of the code documentation. Insufficient documentation makes it difficult for other developers to understand and maintain the code, increasing the potential for introducing unintentional bugs or duplicating effort.

7. External dependencies

This metric measures the number and nature of external libraries and systems the application depends on. The more dependencies, the higher the chances of encountering issues like security vulnerabilities, compatibility problems, and increased maintenance time.

8. Refactoring backlog

A refactoring backlog measures the number of known code improvements that are waiting to be addressed. This includes tasks like code optimization, simplification, and organization, which can reduce technical debt over time.

9. Time-to-fix issues

This metric gauges how long it takes for reported issues to be fixed, including bugs and feature requests. A high time-to-fix can indicate a higher technical debt and slower responsiveness to issues.

10. Mean time between failures (MTBF)

MTBF measures the average time between application failures. A low MTBF indicates a less reliable system and a higher level of technical debt.

11. Defect density

Defect density measures the number of defects relative to the size of the codebase (e.g., bugs per thousand lines of code). A high defect density can suggest the presence of technical debt and code quality issues.

12. Open issues

This metric counts the total number of open issues in a project, assessing the backlog of work to be done. The more open issues, the higher the technical debt and the more urgent the need for addressing these problems.

13. Architectural complexity

Architectural complexity measures the interdependencies and relationships between different components of a system, capturing the overall design and structure of the codebase. High architectural complexity can increase the difficulty of making changes and maintaining the system.

14. API stability

API stability measures how consistently and reliably the API behaves with each update. Frequent changes to an API can make it difficult for clients to maintain their integrations, adding to the technical debt.

15. Technical debt ratio

This metric compares the estimated cost of fixing technical debt items to the overall cost of developing the system, providing a relative measure of the severity of technical debt within a project.

Tech Debt Metrics Explained

Technical debt metrics are essential in understanding and managing the quality and robustness of a software system. They help identify key areas of improvement and monitor progress in addressing critical issues. Code complexity contributes to the difficulty in understanding and maintaining code, leading to increased bugs and slower feature development. Code duplication signifies harder maintenance and a higher risk of introducing bugs. Code churn reflects instability and possible design issues, while code coverage indicates the thoroughness of automated tests, with low coverage leading to vulnerabilities. Code smells suggest potential maintainability issues and increased likelihood of bugs.

Documentation coverage impacts developers’ ability to comprehend and maintain code. External dependencies can pose security, compatibility, and maintenance challenges. Refactoring backlog highlights pending code improvements that can reduce technical debt. Time-to-fix issues measures the responsiveness to issues, and a high value indicates a higher technical debt. Mean Time Between Failures (MTBF) reflects system reliability, while defect density suggests code quality issues. Open issues represent the amount of pending work, which contributes to technical debt. Architectural complexity increases the difficulty of making changes and maintaining the system.

API stability is crucial for maintaining integrations and reducing technical debt. Lastly, the technical debt ratio provides a relative measure of technical debt severity by comparing the cost of addressing debt items to the overall development cost.


In conclusion, measuring and managing tech debt is a critical aspect of ensuring the long-term success and sustainability of any software development project or organization. By utilizing tech debt metrics, teams can gain valuable insights into the overall health and performance of their codebases, identify potential problem areas that need attention, and work proactively to improve software quality. Incorporating these metrics into regular planning and review processes allows for more informed decision-making and resource allocation, reducing the negative impact of tech debt on the project’s overall success.

Ultimately, addressing tech debt effectively is about striking the right balance between innovation and maintenance, and the right metrics can be instrumental in achieving this balance. Make the most out of these tech debt metrics and take control of your software’s future.



What are Tech Debt Metrics?

Tech Debt Metrics are quantitative measurements used by development teams to estimate the cost of technical debt in a software project. These metrics ensure that technical issues are assessed and addressed, thereby keeping the project maintainable and efficient over time.

Why are Tech Debt Metrics important?

Tech Debt Metrics are important because they help to identify areas of potential improvement in a software project. By measuring the impact of technical debt, teams can prioritize tasks related to refactoring, coding standards, architecture, and design decisions, ultimately leading to higher-quality software with better maintainability.

What are some common Tech Debt Metrics a team might track?

Common Tech Debt Metrics include but are not limited to code smells, code complexity, code coverage, maintainability index, and documented code violations. Each of these metrics offers insight into various aspects of a project's health and helps in identifying areas with excessive technical debt.

How can Tech Debt Metrics be used to enhance a development team's productivity?

Tech Debt Metrics not only help in identifying problem areas but also aid in tracking improvements over time. By setting goals to reduce technical debt, teams can focus on refactoring and improving code quality, thereby enhancing overall productivity and software performance.

Can Tech Debt Metrics be applied to all types of software projects?

Yes, Tech Debt Metrics are applicable to a wide range of software projects regardless of their size, complexity, or development methodology. However, the specific metrics used and their implementation might vary from one project to another. It is essential to find the right balance between tracking technical debt and focusing on new feature development.

How we write our statistic reports:

We have not conducted any studies ourselves. Our article provides a summary of all the statistics and studies available at the time of writing. We are solely presenting a summary, not expressing our own opinion. We have collected all statistics within our internal database. In some cases, we use Artificial Intelligence for formulating the statistics. The articles are updated regularly.

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