Measuring technical debt: metrics and tools for a clean code base

The term "technical debt" is ubiquitous in software development.

It describes the implicit costs arising from the decision for a simple but limited solution instead of a better but more time-consuming approach.

As with financial debt, technical debt can also attract interest in the form of increased complexity, slow development and higher susceptibility to errors.

But how can you make and measure such abstracts as technical debt?

In this post we illuminate the most important metrics and tools that will help you evaluate the state of your code base and reduce proactive technical debt.

An important step to ensure long-term high code quality is to reduce the active technical debt .

What are technical debts and why should they be measured?

The term "technical debt" is ubiquitous in software development.

For Measuring technical debt: metrics and tools for a clean code base, Legacy Modernisation, Solution: Legacy Reduction sowie Software Maintenance help you align rollout, scope and budget before you commit.

Technical debts often arise under time pressure or incomplete information. You consciously or unconsciously take shortcuts to publish a feature faster.

This can be advantageous in the short term. However, leads to a code base which is difficult to maintain, expand and understand.

Measuring technical debt is the first step to make it visible and manageable.

It allows teams to make informed decisions about when and where refactoring is needed and helps to ensure the long-term health and sustainability of a software project.

metrics for quantifying technical debt

Short: There are a variety of metrics to quantify technical debt.

There are a variety of metrics to quantify technical debt. No single metric can draw the complete picture.

However, in combination they give a good overview of the code quality. Here are some of the key indicators:

metric	Description	Relevance for technical debt
Error rate	The ratio of errors to the size of the code base.	A high error rate indicates quality problems and hidden complexity.
Code Churn	The frequency with which code is changed or deleted.	High Churn can indicate unstable, badly conceived code.
Technical debt ratio (TDR)	The ratio of costs to problems to development costs.	A high TDR shows that a large proportion of the resources are used for refurbishment.
Code duplication	The percentage of double code.	Duplicated code increases maintenance and error susceptibility.
Zyklomatic complexity	A measure of the number of linear separate paths by the code.	High complexity makes it difficult to understand and test the code.
Test Cover	The share of the code covered by automated tests.	Low Te

References and further reading

The following separate references complement the topics in this article:

• Bitkom – German digital industry association.
• German Federal Office for Information Security (BSI).
• European Commission – Digital strategy.
• MDN Web Docs (Mozilla)
• W3C – World Wide Web Consortium.

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— Björn Groenewold, Managing Director, Groenewold IT Solutions

Frequently Asked Questions (FAQ)

What is this article about: “Measuring technical debt: metrics and tools for a clean code base”?

This post explores Measuring technical debt. Metrics and tools for a clean code base from the perspective of needs, typical pitfalls. And sensible next steps.

In short: The term "technical debt" is ubiquitous in software development.

Who benefits most from the content described here?

Useful for project leads and product owners in Software development who must choose between standard software, custom development, and integration.

How does this topic fit into an IT or digital strategy?

Technically and organizationally, alignment with experienced partners pays off — from requirements to operations; start with the [services overview](/en/services/software-development). For multi-system landscapes, [IT consulting and architecture](/en/services/it-consulting) helps align vendors and internal teams.

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