October 20, 2023

How Do You Measure Technical Debt?


Software development is a fast-paced world, where engineering considerations sometimes take a backseat to more immediate business needs.

The consequence is technical debt, a burden that accumulates over time and eats away at the effectiveness of your product and engineering teams. Too much tech debt can increase development cycles, introduce vulnerabilities, frustrate your customers and limit your ability to effectively grow the business.

However, as the old saying goes, you can't improve what you can't track. Successfully paying down tech debt requires an effective measurement strategy so stakeholders can understand the depth and nature of the issue.

In this blog, we'll walk through some of the more popular metrics used for measuring technical debt, offering a look at how to quantify what is often considered a qualitative issue.

Why is it important to measure tech debt?

Measuring technical debt is critical for a few reasons, each of which has a direct impact on both the software development lifecycle and the broader business strategy.

First, without a quantitative understanding of your technical debt, it's nearly impossible to prioritize what to fix and when. Metrics provide a way to triage the most pressing issues, allowing you to allocate resources effectively.

Second, unmeasured technical debt can escalate into a ticking time bomb that disrupts workflow and impedes innovation. As technical debt accumulates, it increases the complexity of the codebase, making it increasingly difficult to implement new features or make changes without introducing bugs. This can significantly slow down development cycles, undermining your ability to respond to market demands or shifts in business strategy.

Lastly, measuring technical debt has a human element: it directly impacts developer morale and productivity. Constantly working in a codebase that is laden with technical debt can be a demoralizing experience, leading to burnout and turnover. By quantifying the debt, you not only provide a roadmap for improvement but also show the development team that their challenges are recognized and that there is a plan to address them.

Need help understanding what's technical debt and what isn't? Check out our guide covering classic examples of technical debt.

Key metrics for assessing technical debt

Every business is different, and effectively tracking your tech debt is typically a matter of finding the combination of metrics that work best for you. Here are some of the most common.

New bugs vs. Closed bugs

This metric tracks the rate at which new bugs are being reported in comparison to the rate at which bugs are being resolved and closed. A widening gap between new and closed bugs often signals growing technical debt. When new bugs consistently outnumber closed ones, it indicates that the development team is falling behind in addressing issues. Conversely, if the team is closing bugs at a rate that matches or exceeds the creation of new ones, it usually suggests that the codebase is relatively stable and that the team is successfully managing its technical debt.

Understanding the "New Bugs vs. Closed Bugs" ratio can have direct implications for project planning and resource allocation. If you notice a widening gap, it might be time to pause and allocate more resources towards bug-fixing and refactoring, rather than pushing ahead with new features.

Code quality

Complex code can quickly become unwieldy code, especially if there is a limited number of engineers who understand how the code works. Code quality is an aggregation of metrics that track the quality and complexity of a codebase. The metrics most commonly included in code quality are:

  • Cyclomatic complexity - A measure of the number of independent paths through a program's source code. Higher values indicate more complex code, which can be harder to understand, test and maintain.
  • Class coupling - Indicates the degree to which classes in a codebase are interconnected. High class coupling suggests that classes are not modular and may be overly dependent on each other, making the code harder to modify or extend.
  • Lines of code - While easy to measure, this metric is often less informative on its own, as more lines don't necessarily mean worse code. However, unusually high or low counts can be flags for further investigation.
  • Depth of inheritance - Measures how deeply a class is embedded in an inheritance hierarchy. A deeper inheritance depth can increase complexity, as the class inherits attributes and behaviors from multiple ancestor classes, making the code harder to follow and maintain.

Code churn

Code churn measures the frequency of code changes by tracking the number of times a particular line of code has been deleted, replaced, or rewritten. High code churn could suggest potential problems with quick fixes or inadequate code quality, requiring further investigation and resolution.

Conversely, low code churn signifies that the existing code is stable and properly maintained.

Code coverage

Code coverage is a metric that measures the percentage of code that is executed when running a testing suite. The code coverage metric can be used to assess the efficiency of code, as unused lines may suggest inadequate coding. An appropriate target number for code coverage may be approximately 80%, ensuring thorough testing and minimizing the risk of undiscovered bugs or issues.

Maintaining a high code coverage helps organizations ensure their codebase undergoes thorough testing, which reduces the likelihood of technical debt. This contributes to a more stable and reliable software product, ultimately leading to improved user satisfaction and business outcomes.

Cycle time and lead time

Cycle time refers to the time it takes to complete a task or work item from the moment active work begins until it's finished. A prolonged cycle time often indicates inefficiencies in the development process, potentially exacerbated by high levels of technical debt in the code or infrastructure. For instance, if developers are constantly navigating through convoluted code or are bogged down by the complexities of a legacy system, cycle times will naturally extend, slowing down the entire development process.

Lead time, on the other hand, measures the time from when a new task is created until it's completed, encompassing both the waiting time and the active work time (Cycle time). It provides a broader view of how efficiently work flows through the development pipeline. Elevated lead times can be symptomatic of bottlenecks or other systemic issues, including technical debt that's impacting not just the coding but also testing, reviewing, and deploying stages of the workflow.

Both of these metrics serve as vital tools for diagnosing inefficiencies and guiding improvements. Consistently high cycle times and lead times are often signs that technical debt is accruing, warranting immediate attention to streamline the development process and mitigate further accumulation of debt.

Technical debt ratio (TDR)

The Technical Debt Ratio (TDR) is a metric that evaluates the future cost of technical debt by comparing remediation costs to development costs, thereby providing an indication of acceptable levels of technical debt. The TDR is calculated by taking the remediation cost, dividing it by the development cost, and then multiplying the result by 100.

In an ideal scenario, the TDR would be approximately 5%. This allows development teams to balance their efforts between addressing technical debt and working on new features.

How to turn your technical debt metrics into action

Metrics alone serve as indicators, but the real value emerges when you use them to drive improvements.

Start by prioritizing the issues that have the most immediate impact on your development efficiency and product quality. For example, if your new bugs vs. closed bugs metric shows a widening gap, allocate more resources to debugging and refactoring. Simultaneously, consult your code quality metrics to identify areas that require immediate attention, such as modules with high cyclomatic complexity or excessive class coupling. By focusing on the most problematic aspects first, you make the most out of your limited resources, whether it's developer time or computing power.

Once you've identified your priority issues, integrate the action plans into your development sprints. Make it a routine to allocate a certain percentage of each sprint to tackle technical debt. This way, you're not just firefighting; you're proactively maintaining your codebase. It's also crucial to continuously monitor your metrics to gauge the effectiveness of your actions. Are cycle times reducing? Is the gap between new and closed bugs narrowing? These changes will show whether your efforts are paying off, allowing for course corrections as needed.

Metrics provide the roadmap, but taking informed action turns that roadmap into a journey towards a more maintainable, efficient, and high-quality codebase.

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