How to Measure Technical Debt: 7 Key Metrics for CTOs

Most companies acknowledge they have technical debt. Very few actually measure it. But that gap is expensive, and closing it starts with understanding what technical debt really is, why it deserves a place on the executive agenda, and how to put a number to it that leadership can act on.

At Dreamix, we have been building and modernising enterprise software systems for 20 years. In that time, we have seen tech debt treated as a fact of life more often than it is treated as a strategic concern. The companies that measure technical debt, can start to manage it more efficiently. On the contrary, the ones that do not, pay for it, eventually, and usually at the worst possible moment.

This article covers what technical debt is, why tracking it matters across the C-suite, how it blocks modernisation and AI adoption, and the seven metrics that give technology leaders a credible and actionable view of their software portfolio's health.

What Is technical debt?

Technical debt refers to the cost that accumulates when software development decisions prioritise speed over long-term quality. The term was coined in 1992 by software engineer Ward Cunningham, who used the analogy of financial debt deliberately: taking a shortcut now is like borrowing money. The work gets done faster, but the interest compounds over time.

In practical terms, technical debt shows up as code that is harder to maintain than it should be, architectures that resist change, testing gaps that increase the risk of every release, outdated frameworks that limit integration, and documentation so incomplete that onboarding a new engineer takes weeks. Together, and left unmanaged, they slow everything down.

There are three main types of technical debt that organisations typically encounter:

Intentional debt is a deliberate trade-off. A team chooses to launch a product quickly, accepting that certain parts of the codebase will need refactoring later. When there is a plan to address it, this can be a sound strategic decision.

Unintentional debt builds up without anyone actively choosing it. Evolving requirements, inconsistent coding practices, staff turnover, and the gradual ageing of systems all contribute. This is the kind that tends to surprise leadership teams, surfacing as ballooning maintenance costs or system fragility at the exact moment a major initiative is underway.

Environmental debt accumulates as your technology stack ages relative to the market. Frameworks and programming languages go out of support (e.g. COBOL). Infrastructure approaches that were standard five years ago become incompatible with modern cloud-native architectures. The codebase itself may not have changed, but the world around it has.

Keeping track of it matters for a straightforward reason: you cannot prioritise what you cannot measure, and you cannot fund the remediation of something that has no defined cost. Regular measurement turns technical debt from an abstract complaint into a quantifiable risk with a clear return on investment for addressing it.

What every C-Level executive should understand about technical debt

According to McKinsey, technical debt accounts for approximately 40% of the average company's IT balance sheet, and around 30% of CIOs report that more than a fifth of their technology budget, money nominally allocated to new product development, is silently redirected toward resolving debt-related issues. That is a significant misdirection of capital with no visibility in the budget conversation.

Technical debt affects every senior leader differently, depending on what they are responsible for. The most effective organisations are those where measurement and accountability span across the C-suite rather than being confined to engineering.

For CTOs, the concern is architectural health and engineering velocity. A codebase carrying heavy debt gets harder to change with every new feature added to it. Complexity accumulates, dependencies become unpredictable, and the risk of any single change causing downstream failures rises steadily. CTOs who track technical debt metrics can make architecture decisions on evidence, identify where modernisation investment will have the greatest impact, and maintain a software portfolio that keeps pace with the business strategy.

For CIOs, the stakes are strategic. As IDC has noted, systems that were once fit for purpose now increasingly inhibit agility, scalability, and trust in data-driven decision-making. The CIO's challenge is translating the technical reality of debt into a business case that secures investment and executive alignment. IDC also predicts that 40% of CIOs in 2025 are actively driving enterprise initiatives to remediate technical debt specifically to remain competitive as AI adoption accelerates. Without a measurement framework, none of those initiatives have a defensible starting point.

For CFOs, the question is always one of cost and ROI. Technical debt has a principal and an interest component, much like financial debt. The principal is the cost of all the modernisation work that has been deferred. The interest is the ongoing complexity tax paid on every project, the additional time, resource, and risk that accumulates on top of development costs. McKinsey's research found that companies pay an additional 10 to 20 percent on top of every project's cost just to manage existing technical debt. When a CFO sees that figure represented clearly, the conversation about budget allocation changes.

For COOs, the issue is operational continuity and reliability. Legacy systems carrying significant debt tend to be fragile. They resist integration with modern tools, create bottlenecks in workflows, and introduce delays in the delivery of internal capabilities. Technical debt in operational systems can also extend the time it takes to onboard new processes or respond to changing market conditions, which directly affects the business's ability to operate efficiently.

For Heads of Security and CISOs, technical debt is one of the most underappreciated security risks in the enterprise. Systems built under older security assumptions accumulate vulnerabilities that modern frameworks address by default. Outdated dependencies, patching debt, and architectural patterns that predate current threat models all create exposure. As organisations in regulated sectors like fintech, healthcare, and aviation know well, a security gap in a legacy system carries compliance consequences that go far beyond the cost of the fix itself.

The common thread across all of these perspectives is that each role is affected by technical debt in ways that are measurable and manageable, but only if the organisation has a framework for doing so.

How technical debt blocks modernisation and AI adoption

Technical debt and modernisation are directly at odds. Every organisation pursuing a modernisation programme, whether that is migrating to the cloud, moving from monolithic to microservices architecture, or adopting AI-powered capabilities, will find that the speed and cost of that programme is directly shaped by the amount of debt it carries going in.

McKinsey's analysis of 220 companies across seven industries found that organisations in the bottom fifth for technical debt (also referred as the “the digital dark matter”) performance are 40% more likely to have incomplete or cancelled modernisation programmes than those in the top fifth. The reason is straightforward: modernisation that lands on top of a debt-laden system costs significantly more, takes significantly longer, and delivers results far below what was scoped.

The AI dimension adds particular urgency to this. AI-powered capabilities, whether that is intelligent process automation, predictive analytics, or agentic AI development that embeds features in products, depend on clean data pipelines, scalable infrastructure, and modern integration patterns. And all three of these can be compromised by technical debt.

At Dreamix, we see this play out with partners across aviation, fintech, transportation, and healthcare. The organisations that invest in understanding and measuring their technical debt before committing to a modernisation or AI programme consistently deliver better outcomes. Those that treat modernisation as an opportunity to bypass the debt question tend to find it waiting for them midway through.

7 technical debt metrics every CTO should track

With the business context in place, here are the seven metrics that give CTOs, CIOs, and their leadership peers a concrete and actionable picture of technical debt across a software portfolio.

1. Technical Debt Ratio (TDR)

The Technical Debt Ratio is one of the most widely used formal metrics for quantifying technical debt at the codebase level. It compares the estimated cost of fixing all known issues against the cost of rebuilding the system from scratch.

Formula:

TDR = (Remediation Cost / Replacement Cost) × 100%

Example: A team estimating $200,000 in remediation costs against a $1,000,000 system replacement cost carries a TDR of 20%.

Industry benchmarks suggest that a TDR below 5% reflects a healthy, high-velocity codebase. Many organisations running legacy enterprise systems operate at 10% or higher, often without knowing it.

For CTOs, TDR is the closest equivalent to a credit rating for a software portfolio. It shows how much of the system's potential value is already spoken for by past decisions, and it gives the CFO a financial frame for what remediation investment would actually cost relative to the alternative.

2. Maintenance-to-Innovation Ratio

This metric captures the proportion of engineering time spent keeping existing systems running versus building new capabilities. It is one of the clearest indicators of whether technical debt is actively constraining the organisation's ability to grow.

Formula:

Maintenance ratio = (Engineering hours on maintenance / Total engineering hours) × 100%

A ratio above 50% means teams are spending more time managing what already exists than building what comes next. Industry guidance points to keeping this below 40% as a healthy benchmark. McKinsey documented cases where organisations that addressed their technical debt systematically brought their maintenance share of engineering time down from 75% to 25%, freeing up significant capacity for product development.

For CTOs presenting to a board or to a CEO, this ratio makes the opportunity cost of technical debt tangible. Every percentage point above the healthy threshold represents engineering capacity that is not available for product development, feature delivery, or competitive positioning.

3. Mean Time to Change (MTTC)

Mean Time to Change measures how long it takes to implement a standard change across a system or set of systems. It is a direct proxy for how much architectural complexity has accumulated.

Formula:

MTTC = Total time required for all standard changes / Number of changes

When this number increases quarter over quarter, it is a reliable early signal that architectural debt is building. Changes that once took a sprint now take several. Work that was predictable becomes uncertain. In most cases, the cause is not team capacity but codebase complexity.

For CTOs and CIOs managing multiple products or systems, tracking MTTC across the portfolio also reveals where debt is most heavily concentrated, which is the right input for sequencing modernisation efforts and making the argument for prioritised investment.

4. Deployment Frequency

Deployment frequency tracks how often code changes can be safely pushed to production. In a well-managed codebase, teams deploy frequently and with confidence. As debt accumulates, the risk of any single change affecting something unexpected rises, and teams compensate by deploying less often and testing more extensively for longer.

Declining deployment frequency is often the first leadership-visible symptom of worsening technical debt. If your engineering teams were deploying weekly and are now deploying monthly with the same headcount, the underlying system has become more fragile.

For COOs and business leaders, this metric maps directly onto GTM timelines. Slower deployment means slower feature releases, longer feedback cycles with customers, and a reduced ability to respond to competitive pressure in a timely way.

5. Defect Escape Rate (DER)

Defect Escape Rate measures the percentage of bugs that reach production before being identified. Higher rates are a strong indicator of degrading code quality, insufficient testing coverage, or both.

Formula:

DER = (Defects found in production / Total defects found) × 100%

Example: If 5 bugs are discovered after a release and 95 were caught during testing, the total defect count is 100 and the DER is 5%.

As technical debt accumulates, engineering teams spend more cognitive effort managing system complexity and less time on thorough testing, which pushes DER upward. For CTOs and Heads of Security in regulated industries, including fintech, healthcare, and aviation, a rising DER carries compliance and risk implications well beyond the cost of fixing the bugs themselves.

For more in-depth information about testing as an outsourced service, I encourage you to read our expert blog post by Polina Ezekieva, Head of QA Practice at Dreamix: 

Button: Outsourced Software Testing: Complete Executive Guide 2026

6. Code Complexity Score

Code complexity is engineering-facing, but its business implications are direct and significant. Two measures are commonly tracked together:

Cyclomatic complexity counts the number of independent execution paths through a piece of code. Higher numbers mean more potential locations for bugs and more testing required to cover them adequately.

Cognitive complexity measures how difficult the code is for a developer to understand. This affects onboarding time, maintenance speed, and the likelihood that a developer introducing a change in one area will inadvertently affect something in another.

Tools including SonarQube, CodeScene, and CAST Imaging calculate these scores automatically and continuously. They are most valuable when integrated into development pipelines, so teams have visibility of debt accumulation in real time rather than discovering it during a programme review or an audit.

For CTOs, rising complexity scores are also worth examining alongside hiring and onboarding costs. The harder a codebase is to understand, the longer it takes to bring new engineers up to speed, which compounds the cost of any staff turnover.

7. McKinsey's Tech Debt Score (TDS)

McKinsey developed the Tech Debt Score as a structured way for organisations to quantify technical debt and benchmark their position against industry peers. Based on an analysis of 220 companies across five geographies and seven sectors, TDS provides a composite view of an organisation's debt severity and the likely impact on business performance.

Companies scoring well on TDS tend to show revenue growth around 20% higher than those in the bottom fifth. Those with the worst scores are significantly more likely to see modernisation programmes cancelled or run substantially over budget. For CTOs and CIOs building the investment case for modernisation, TDS provides a data-backed starting point that translates the engineering reality of debt into commercial language that resonates with CFOs and CEOs.

How Dreamix approaches technical debt proactively

At Dreamix, we have been building and modernising enterprise software systems since 2006. In 20 years of working with partners across fintech, aviation, transportation, healthcare, and pharma, we have seen what well-managed technical debt looks like and what poorly managed debt costs when it eventually surfaces.

Our approach to technical debt is proactive by design, because reactive management is always more expensive than preventive governance.

We assess the technical health of your existing systems before making any recommendations, because the right sequence of decisions depends on understanding where debt is actually concentrated and what your business priorities are.

We do not arrive with a standard modernisation template. Every engagement starts with your architecture, your context, and your goals.

Our teams proactively challenge the brief. We ask about the goal behind the request and flag where a short-term decision today is likely to create a larger cost further down the line. That is what contributing business value, rather than just writing code, actually looks like.

Most of our partnerships run for more than three years, and a number have continued for over a decade. That depth of relationship is what makes proactive debt management possible.

We invest heavily in the technical depth of our engineers through personalised growth plans and nearshore models that retain specialists on your domain over time. Our 95% employee retention rate means the people who know your system continue working on it. Technical debt compounds with team turnover. Institutional knowledge about why decisions were made is one of the most valuable assets in managing it.

We include debt tracking in project governance from day one, build quality gates into CI/CD pipelines, and conduct regular architectural reviews to catch complexity before it becomes structural.

We translate debt metrics into language that works for your leadership team, not just your engineering team.

Our architectural review service provides a structured assessment of your system's technical health: where debt is concentrated, what it is costing you in delivery capacity, and what a realistic remediation roadmap looks like. For organisations at the start of a modernisation or AI adoption journey, this is typically where the most important decisions get made.

When measurement reveals a deeper problem

In some cases, putting a proper measurement framework in place surfaces something more significant than accumulated code issues. If your technical debt metrics are deteriorating consistently despite investment, or if the root causes point to fundamental architecture decisions rather than maintenance gaps, that is usually a signal that targeted modernisation is needed.

Incremental cleanup has its place, but it cannot substitute for structural change when the underlying architecture is what is limiting the system's ability to support the business. The organisations that try to patch their way through this level of debt typically find themselves running expensive programmes that deliver limited results, because they addressed the symptoms rather than the cause.

This is also where the value of a development partner with genuine domain expertise and long-term thinking becomes clear. At Dreamix, we have worked through this challenge with partners across multiple industries, and our experience is that the organisations that make the most progress are those that approach modernisation as a business initiative from the outset, with executive alignment, clear measurement, and a development partner who is accountable to outcomes rather than just deliverables.

For more on the strategic frameworks that sit alongside these metrics, Dreamix's guide on how to manage technical debt without sacrificing innovation covers the end-to-end approach in detail.

Inevitably, if the picture that emerges from measurement points to a deeper architectural problem, or raises questions about your current development partner's ability to address it, our guide on what to consider when changing your software development vendor covers what to look for in that decision.

Wrapping up

Technical debt is a strategic issue. The organisations that treat it as one, by measuring it consistently, translating it into business terms, building it into governance, and working with development partners who proactively manage it, are the ones that keep engineering capacity available for growth.

The seven metrics here give CTOs, CIOs, and their leadership peers a concrete starting point: a way to quantify what was previously abstract, prioritise what was previously ignored, and build the investment case for the decisions that allow organisations to innovate rather than maintain.

With 20 years of enterprise software development experience across fintech, aviation, transportation, and healthcare, Dreamix is well placed to help. Whether you are looking to assess your current portfolio, plan a modernisation programme, or build technical debt management into the way your teams work day to day, we are ready to get into the detail with you.

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