How to Measure Tech Debt Metrics: A Comprehensive Guide

In the race to deliver top-tier software products under tight deadlines, an effective strategy on how to measure technical debt has become critical to software development.

According to the Consortium for Information & Software Quality (CISQ), the cost of technical debt in the United States surpassed $2.41 trillion in 2022, up from $1.31 trillion in 2010. Technical debt can cause so much frustration and burnout to development teams. And the only way to overcome it is to manage it proactively.

In this article, we will explore essential metrics for identifying and measuring technical debt, examine its primary types, and outline best practices for proactive management. Let’s dive in!

 

Metrics for Measuring Technical Debt

To measure and improve technical debt, you first need to identify it. Here’s how to identify technical debt:

How to identify technical debt?

Managing technical debt begins with creating a “technical debt list”. This list includes various technical debt “items,” each representing an unfinished task that could lead to future problems if not addressed.

Examples of technical debt items include:

• Modules in need of refactoring
• Pending tests
• Required inspections or reviews
• Documentation updates (including comments)
• Architectural compliance issues
• Known latent defects that need resolution

Each item on this list should include a description of where the debt item is in the system and why that task needs to be done and estimates of the principal and the interest:

• Principal: The effort required to complete the unfinished task.
• Interest: Consists of two components:
• Interest probability: The chance that leaving the debt unresolved will increase the cost of future work.
• Interest amount: The extra work needed if the debt is not addressed.

For instance, the interest probability for a testing debt item is the likelihood that latent defects, which could have been caught during testing, will cause issues later. The interest amount is the additional effort required to fix these defects in the future compared to if they had been identified during initial testing.

The technical debt list should be regularly reviewed and updated after each release, with items added and removed as necessary. Different types of debt are added at various stages of the release process:

• Testing debt items: Added when tests or reviews are skipped for a particular release or module.
• Defect debt items: Added when a defect is found but not fixed in the current release.
• Documentation debt items: Added when software modifications are not reflected in the corresponding documentation.
• Design debt items: Generated when code deficiencies, such as poor practices or architectural violations, are identified.

Technical debt: How to measure?

Initially, technical debt items are given broad estimates (high, medium, or low) for principal, interest probability, and interest amount.

These rough estimates are sufficient for tracking and initial decision-making. More precise estimates are made later during detailed planning when more information is available.

For accurate technical debt measurement, specific procedures are followed for each metric, depending on the type of technical debt item and the historical data available.

A complete technical debt evaluation will look like the following table:

A comprehensive technical debt evaluation involves detailed estimates for principal, interest amount, and interest probability. This quantification allows the software development team to make analytical comparisons and create data-driven plans.

A 2018 study found that only 7.2% of organizations systematically track technical debt, and only 26% use tools for it. However, those who actively manage and reduce technical debt achieve at least 50% faster service delivery times and other benefits. Therefore, measuring tech debt and selecting the most useful metrics based on your business’s needs and objectives is crucial.

After defining the estimation of all 3 metrics principal, interest amount, and interest probability, more detailed metrics will be calculated.

Some metrics we suggest to monitor are defect ratio, debt index, lead time, code coverage, tech debt ratio, change failure rate, and the number of failed Continuous Delivery (CD) and Continuous Integration (CI).

The table below provides a better understanding of all metrics and how to calculate each one:

Metric	What it is?	Why need this?	How to calculate?
Defect ratio	The defect ratio metric compares new bugs to closed bugs during a specific period	A low ratio indicates effective issue resolution. A high ratio suggests quality or development process issues, as new bugs outpace fixes	Defect Ratio = Number of New Bugs / Number of Closed Bugs
Lead time	Lead time measures the time between task commitment and its release	Long lead times can lead to obsolescence, increased costs, and reduced customer satisfaction. Identifying and automating bottlenecks can improve lead times	The metric measured in units of time (hours, days, weeks) from task commitment to release
Debt index	This metric is the ratio of total tech debt to the system’s scale (e.g., codebase size). It provides a relative measure of technical debt per unit of code size or complexity	A lower debt index indicates better debt management relative to codebase complexity	The debt index is calculated by dividing the total tech debt by some measure of the system’s scale, such as the size or complexity of the codebase.   Debt Index = Total Technical Debt / Codebase Size (e.g., lines of code, function points)   The specific formula for calculating the debt index may vary depending on the methodology or tool used
Code coverage	The metric that assesses the percentage of code tested by automated tests	Higher coverage indicates more of the code has been tested, reducing the likelihood of bugs. A target of around 70% coverage is often considered good.	Code Coverage = (Number of Executed Code Lines / Total Code Lines) * 100   It is calculated by dividing the executed code lines or blocks during testing by the total code lines or blocks, expressed as a percentage of test coverage
Technical debt ratio (TDR)	This ratio measures the effort required to address technical debt vs. developing new features	A lower TDR indicates a better balance between addressing debt and developing new features. High TDR suggests a significant portion of effort goes to fixing debt	TDR = (Remediation Cost / Development Cost) * 100
Change failure rate (CFR)	This metric assesses the quality of change management processes	High CFR indicates frequent disruptions or failures due to changes. Low CFR suggests effective change management and higher success rates for changes	CFR = (Number of Failed Changes / Total Number of Changes) * 100
Number of failed CD and CI events	This number counts instances where automated Continuous Delivery (CD) and Continuous  Integration (CI) processes fail	The high number of failures points to issues with code quality, testing, or deployment infrastructure. Indicates areas needing improvement for reliable delivery.	Count of failed CD and CI events during the software development lifecycle

 

By consistently monitoring these metrics, organizations can gain a comprehensive understanding of their tech debt, identify areas needing improvement, and implement strategies to enhance software quality and efficiency.

 

What Is Technical Debt?

Technical debt is a widely recognized term in the software development industry, acknowledged as a critical issue affecting organizations.

In a recent McKinsey survey, CIOs reported that 10 to 20% of the technology budget dedicated to new products is diverted to resolving issues related to tech debt.

More troubling still, CIOs estimated that tech debt amounts to 20 to 40% of the value of their entire technology estate before depreciation. This translates into hundreds of millions of dollars of unpaid debt for larger organizations.

And things are not improving: 60% of the CIOs we surveyed felt their organization’s tech debt had risen perceptibly over the past three years.

Although Ward Cunningham introduced the tech debt metaphor two decades ago, it has gained substantial attention from researchers only in recent years. Various definitions of technical debt have emerged in academic and industry literature:

• According to Gartner, tech debt refers to work owed to an IT system when teams prioritize short-term gains over long-term quality, impacting system performance, scalability, or resilience. This includes taking shortcuts or delaying system maintenance, ultimately leading to software instability and increased support costs.
• Ward Cunningham coined the term “technical debt” in 1992, likening it to financial debt in the securities market. He explained that shipping initial code is like taking on debt: while it can expedite development, it must be promptly repaid with a rewrite to avoid accumulating interest. This interest accrues with every moment spent on imperfect code, potentially stalling entire engineering efforts, especially in complex, object-oriented implementations. The concept has since evolved from Cunningham’s original notion of “not-quite-right code” to encompass any delayed internal software development tasks that risk causing future issues if left unresolved, emphasizing short-term gains over long-term software health.
• In an academic research, tech debt is broadly defined as immature or incomplete artifacts within the software lifecycle. Carolyn and Yuepu describe it as artifacts that persist within a system, increasing long-term costs and diminishing software quality.
• Similarly, “A systematic mapping study on tech debt and its management” study highlights tech debt as compromises made during development that offer short-term benefits but may undermine the software’s long-term health.
• McKinsey categorizes tech debt into two components: the principal and interest. The principal involves deferred maintenance, necessary upgrades, and bespoke software modifications that exceed original vendor support. Interest represents ongoing complexities and productivity losses resulting from initial compromises, such as nonstandard data harmonization and workaround solutions.

Based on the definitions provided, here’s a synthesized definition of technical debt:

“Technical debt is the cumulative consequence of expedient software development decisions that prioritize short-term benefits over long-term sustainability. 

It encompasses deferred maintenance, incomplete artifacts, and compromises made during development, all of which pose risks to system performance, scalability, and resilience. 

Left unaddressed, tech debt leads to increased software instability, higher support costs, and diminished overall software quality. Effective management is essential to mitigate these risks and ensure the long-term health and efficiency of IT systems.”

 

Main Types of Tech Debt

We often equate technical debt solely with bad code, but there is more to it. There are various types of tech debt.

In 2007, Steve McConnell proposed that tech debt can be categorized into two main types: intentional and unintentional. Intentional debt is deliberately taken on as a strategic decision, whereas unintentional debt results from poor practices and is not part of a deliberate strategy.

McConnell further categorizes technical debt based on time frames:

1. Nondebt: This includes a feature backlog, deferred features, and cut features. Not all incomplete work constitutes debt, as these do not incur interest payments.
2. Debt:

• Unintentional debt: Incurred deliberately for specific reasons
• Intentional debt: Debt incurred intentionally
• Short-term debt: Typically incurred reactively, for tactical reasons
• Focused short-term debt: Individual, identifiable shortcuts (like a car loan)
• Unfocused short-term debt: Numerous small shortcuts (like credit card debt)
• Long-term debt: Generally proactive for strategic reasons

 

The paper Towards an Ontology of Terms on Technical Debt cites 13 distinct types of tech debt, including:

• Architecture debt
• Build debt
• Code debt
• Defect debt
• Design debt
• Documentation debt
• Infrastructure debt
• People debt
• Process debt
• Requirement debt
• Service debt
• Test automation debt
• Test debt.

However, this article will only choose the 4 main types of tech debt to analyze.

 

Requirements debt

Requirements debt arises when prioritizing features that do not add value to the product. Often, this results from inadequate requirements elicitation or accepting unnecessary customer requests without proper analysis.

There are two main types of requirements for debt:

• Poor requirements engineering: Inadequate stakeholder engagement leading to low-quality requirements.
• Ignorance of requirements: Misunderstanding actual customer needs, resulting in poorly implemented features.

 

Design and architecture debt

Design and architecture debt stems from compromises and suboptimal decisions affecting a software system’s overall structure and integrity.

Key characteristics of design and architecture debt include:

• Nonlocal concerns: Issues like security or performance impacting multiple system parts.
• Entropy: Gradual degradation of design integrity over time.
• Cyclic dependencies: Complex interdependencies among system components.

There are several causes of design debt:

• Improper separation of concerns: Combining multiple responsibilities within a single class.
• Clone and own: Copying and modifying code without understanding dependencies.
• Tangled dependencies: Lack of architectural planning leading to a disorganized structure.
• Unplanned evolution: Adding features without considering the system-wide impact.

 

Implementation debt

Implementation debt refers to tech debt within the source code due to shortcuts and suboptimal coding practices.

Common causes include:

• Prototypes becoming products: Unrefined prototype code becoming permanent.
• Outdated technology choices: Using old technologies that no longer meet current needs.

Implementation debt manifests in various forms, such as poor coding style, inefficient or deprecated code, and code duplication. 

Maintaining good code is like sustaining good health through regular exercise and a balanced diet; it requires consistent adherence to best practices.

Neglecting this can lead to the buildup of implementation debt, ultimately affecting the software’s quality and maintainability.

 

Testing debt

Testing debt accumulates from deficiencies in the software testing processes, leading to increased bugs and issues during the software delivery period.

Causes of testing debt:

• Inadequate testing: Key functionalities lacking proper tests.
• Testing the wrong things: Misplaced testing priorities.
• Needless testing: Wasting resources on known correct parts.
• Unreliable tests: Tests that do not consistently yield accurate results.
• Focusing on happy paths: Neglecting error cases in testing.
• Mismatch with specifications: Tests not align with system requirements.

 

Deployment debt

Deployment debt relates to all the shortcuts, errors, or mistakes that occur during the deployment and operation of a software system.

Deployment debt arises when software is deployed in an ad hoc, often labor-intensive manner, making it challenging to deploy quickly and efficiently with minimal human involvement. This can create chaos since manual operations are prone to errors.

For example, in 2012, Knight Capital experienced a $400 million loss due to a manual deployment error, ultimately leading to the company’s bankruptcy.

 

Documentation debt

Documentation debt refers to shortcuts taken in documenting a software system, such as outdated design specifications and missing code comments.

Documentation debt occurs when proper documentation practices are neglected, leading to incomplete, outdated, or missing documentation.

This is different from documenting tech debt, which involves practices for capturing and describing existing technical debt.

 

How Does Technical Debt Impact Business?

Tech debt might affect businesses in both negative and positive ways.

The consequences of tech debt

The business impact of technical debt can be significant, affecting various aspects of operations and growth:

• Increased costs: Technical debt results in higher costs for businesses due to increased time and resources needed to fix issues stemming from development shortcuts or underinvestments. This applies to both operational systems and product development.
• Lower quality of service: Tech debt can significantly impact service quality by increasing the likelihood of software malfunctions and errors. This leads to challenges in maintaining and updating systems, which can result in unhappy customers, higher churn rates, and a diminished reputation.
• Reputation damage: Reduced service quality from tech debt can harm the reputation of SaaS businesses. Service disruptions or other issues caused by technical debt may lead to negative reviews and a loss of customer trust.
• Difficulty in scaling: Accumulated technical debt can hinder the scalability of SaaS businesses. As software complexity grows, maintaining and updating tools becomes more challenging, potentially limiting operational expansion.
• Reduced innovation: Technical debt limits the ability of SaaS businesses to innovate by diverting developer resources towards fixing backlog issues rather than developing new features. This results in missed deadlines, stagnant roadmaps, and decreased morale among developers.
• Longer time to market: Tech debt prolongs the time needed to launch products to market, diminishing a company’s competitive edge. Developers spend considerable time addressing technical debt, slowing down the development process and delaying product releases.

 

The benefits of technical debt

Technical debt can be strategically leveraged in software development plan. When incurred deliberately for strategic purposes, the underlying rationale is that today’s development costs are perceived as higher than future costs. Several reasons can justify this approach:

• Time to market: When speed to market is crucial, spending an additional $1 on development today might prevent a $10 revenue loss. Even if the future development cost increases to $5, incurring the $1 debt now is a wise business decision.
• Preservation of startup capital: A startup operates with a finite amount of seed funding, making every dollar critical. Delaying an expense for a year or two allows the startup to cover that expense with more substantial funds later rather than using valuable startup capital immediately.
• Delaying development expense: When a system is retired, all its technical debt is effectively eliminated. After a system is decommissioned, there’s no practical difference between a meticulously crafted solution and a quick, less refined one. Unlike financial debt, technical debt is absolved upon the system’s retirement. Therefore, as a system nears the end of its service life, it becomes harder to justify investing in anything other than the most expedient solutions.

 

Manage Tech Debt With LTS Group

The most effective way to handle tech debt is through proactive management. This involves making intentional and well-justified decisions to take on debt, implementing a sound strategy for measuring and tracking it, and planning for its future repayment.

In order to manage technical debt, tech leaders can follow these steps:

• Step 1 – Analyze the development process and codebase: This includes conducting a thorough review of the current development processes and examining the codebáe to identify bottlenecks and areas of concern.
• Step 2 – Create a tech debt map: In this step, tech leaders document the indtìied technical debt in a visual map as in the picture below. This map includes all areas of the system affected by tech debt.
• Step 3 – Review and identify causes: With the Tech Debt Map in hand, the team should proceed to review each identified piece of technical debt to uncover its underlying causes.
• Step 4 – Assess the impact of inaction: Evaluating the potential consequences of neglecting each piece of technical debt is essential to understand if it might worsen over time.

At LTS Group, drawing on our 8 years of experience in software development solutions and over 300 projects, we’ve developed effective strategies for handling tech debt:

1. Transparency and alignment: Begin by openly discussing development challenges and the need for speed. Summarize these discussions in a way that all stakeholders, from engineers to management, share a common understanding, aided by visualizations.
2. Ownership and responsibility: Ensure clear ownership of all system components. Address parts of the system that lack ownership to prevent neglect and accumulation of technical debt.
3. Impact-based prioritization: Prioritize tech debt items based on their impact, as illustrated in our Tech Debt Map, to focus efforts effectively.
4. Empowerment and integration: Empower teams to tackle problems and address technical debt as part of regular product development. Strive for a balanced approach between reducing technical debt and adding new functionality, dedicating 10–20% of development time to this effort.
5. Refactoring as you go: For smaller technical debts, refactor code during routine updates to maintain code quality (“Good Boy Scout” principle).
6. Focused efforts for medium debt: Allocate specific times, like “Tech Debt Fridays,” or prioritize tech debt as a feature to dedicate focused attention to medium-sized issues.
7. Dedicated time for significant debt: Schedule fixed periods, such as allocating two days to a story or forming a dedicated task force, to address larger technical debt items over weeks or months.
8. Utilize metrics for insight: Utilize metrics such as code issue counts from tools like SonarQube and lead times to inform decisions on addressing technical debt effectively.

Tracking technical debt can be facilitated through various tools without introducing unnecessary complexity or cost:

1. Project management tools: Utilize tools like Jira, Asana, GitHub issues, or Azure DevOps Board for tracking technical debt within project management workflows.
2. Automated code analysis tools: Implement automated code analysis tools such as SonarQube to detect and manage code issues, security vulnerabilities, and other technical debt indicators.
3. Manual tracking methods: Employ manual tracking methods such as Excel or customized approaches tailored to specific needs, ensuring accessibility and ease of updates.

 

How to take the good out of a tech debt?

As discussed earlier, technical debt can be strategically managed in software development. But how can this be done? Here are some recommendations from LTS Group’s experts:

Enhance transparency of technical debt

One issue with technical debt is that project teams often incur it intentionally, yet it remains invisible and untracked.

Here are two methods to improve transparency in tracking technical debt:

• Maintain a dedicated debt list within the defect tracking system: Every time debt is accrued, log the necessary tasks to resolve it, along with estimated effort and timelines. Track the debt backlog closely, flagging any unresolved items older than 90 days as critical.
• Integrate the debt list into the Scrum product backlog: Treat each debt as a Scrum “story,” estimating the effort and scheduling for each debt similarly to other Scrum tasks.

Strengthen the ability to pay off tech debt

Teams vary in their ability to repay technical debt, often reflected in their “technical debt credit rating.”

The key to effectively managing technical debt is minimizing unintentional debt arising from subpar work quality. The less debt accrued through such means, the more strategic debt a team can safely handle.

In some projects, experts advise monitoring debt vs. team velocity. If servicing technical debt impacts a team’s velocity negatively, prioritize reducing debt until velocity stabilizes. Alternatively, track rework to gauge the cumulative debt accumulation.

 

Communicate about tech debt 

The use of technical debt terminology facilitates communication with non-technical stakeholders, historically challenged by opacity.

Transitioning from technical jargon to financial terms provides a clearer framework for discussions:

• Use the organization’s maintenance budget as an indicator of technical debt servicing: Distinguish between maintenance that sustains system operation versus enhancing system capabilities—only the former qualifies as technical debt.
• Frame debt discussions in financial metrics rather than technical features: For instance, “40% of our current R&D budget supports past releases” or “We allocate $2.3 million annually to service technical debt.”
• Ensure the nature of debt aligns with strategic goals: Not all debts are equal; some stem from prudent business decisions, while others result from inadequate technical practices or miscommunication regarding intended debts.
• Approach debt discussions as ongoing dialogues: It may take several conversations for stakeholders to fully grasp the nuances of technical debt.

 

Making informed decisions with techn debt

When confronted with the choice to incur tech debt, teams typically weigh two paths: the “costly but superior” approach versus the “quick and makeshift” route. Thoroughly estimating both options informs the optimal decision at that juncture.

Besides, these are the three issues that the development team should also take into account:

• How much it will cost to backfill the good path after you’ve already gone down the quick path?
• The interest payment on the tech debt: If you choose the quick path now, how much does that slow down other work until you’re able to retrofit the good path?
• Is there a path that is quicker than the good path and that won’t affect the rest of the system?

By adopting these strategies and leveraging appropriate tools, LTS Group ensures efficient management and reduction of technical debt across its projects.

 

Frequently Asked Questions About Technical Debt

How to measure technical debt in Agile?

Unlike traditional waterfall approaches that aim for final product delivery, Agile emphasizes continuous evolution and adaptability of systems. Over time, technical debt in Agile software development increases the cost of maintaining and evolving software systems, potentially affecting reliability, scalability, and overall productivity. By integrating technical debt management into Agile practices—through backlog integration, collaborative efforts, sprint planning, ongoing management, and adaptation to external factors—you can effectively measure and mitigate technical debt while maintaining the agility and responsiveness required for continuous software improvement.

 

What is technical debt?

Technical debt refers to the accrued cost resulting from expedient development choices that prioritize short-term benefits over long-term sustainability. It encompasses various compromises in code quality, architecture, documentation, and testing practices that accumulate during software development.

 

How do we measure tech debt?

Measuring technical debt involves conducting a comprehensive evaluation that includes detailed estimates for principal, interest amount, and interest probability. Key metrics recommended for monitoring include defect ratio, debt index, lead time, code coverage, tech debt ratio, change failure rate, and metrics related to Continuous Delivery (CD) and Continuous Integration (CI) failures. These metrics collectively provide insights into the extent and impact of technical debt, enabling proactive management strategies to enhance software quality and maintainability.

 

What is a good technical debt ratio?

The Technical Debt Ratio (TDR) assesses the relationship between the effort needed to resolve existing technical debt and the effort required for new feature development in a software project. This ratio is computed using the formula: (Remediation Cost ÷ Development Cost) × 100 = TDR. A favorable technical debt ratio is typically characterized by a balance between strategic debt incurred for short-term gains and the team’s ability to manage and repay this debt over time.

 

Final Thoughts About Technical Debt

Neglecting technical debt can result in disruptions in business operations, drain budgets, diminish productivity, lower work quality, compromise security, and more. Prioritizing the measurement and improvement of technical debt metrics is crucial for mitigating these risks.

This article has provided a comprehensive guide to identifying key metrics for measuring technical debt and implementing effective strategies to combat it.

If your software development team is seeking a skilled external vendor with extensive experience in managing diverse software projects and technical debt, LTS Group is ready to help achieve your goals. Contact us today to discuss your business requirements.

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