Tech Debt Statistics for 2026

A release can look on schedule until a small change reaches the module nobody wants to touch. Then the extra review, regression work, and stabilization time show up all at once. If you are looking for tech debt statistics you can cite in 2026, there is no single credible global percentage for engineering time spent on debt. The useful evidence is a mix of code-analysis benchmarks, remediation models, analyst estimates, and practitioner surveys—and those measures should not be treated as interchangeable.

Key tech debt statistics for 2026

• SIG estimates technical debt accounts for 21%–40% of total IT spending in its 2026 enterprise-software benchmark. This is SIG’s benchmark framing of IT spend, not a measured share of developer time or a universal industry rate. SIG’s State of Software 2026 says its benchmark covers more than 400 billion lines of code across more than 30,000 systems.

• In that same 2026 SIG benchmark, 86% of code falls below SIG’s recommended maintainability rating, while 71% has a low degree of security controls. These are SIG code-quality classifications, not technical-debt percentages.

• SIG reports that 1.9% of enterprise production code is AI-generated in its 2026 benchmark and that AI-generated code has roughly twice the security-risk violations of human-written code. This is a benchmark observation, not controlled evidence that AI causes the difference.

• SIG’s 2026 modeled scenario says reducing code-level technical debt can save €870,000 in developer time per system per year. It is a modeled capacity saving, not observed cash savings for every system.

• CAST’s September 24, 2025 research release estimates that companies and governments would need 61 billion software-development workdays to remediate debt accumulated over four decades. CAST based the model on more than 10 billion lines of code across 17 countries representing 51% of world GDP.

• CAST classifies 45% of code in its analysis as fragile, 32% as suffering from bloat, and 31% as too rigid. These code-analysis categories are not additive and do not make up a single technical-debt rate. CAST’s 2025 report is the underlying source.

• Sonar’s July 2023 cost model estimated $306,000 a year, or 5,500 developer hours a year, to remediate technical debt in a one-million-line project. The model drew on more than 200 real-world projects over 12 months, totaling about 11 million lines of code; it is historical modeled context, not a 2026 industry average.

• HFS’s May 2025 research estimates $1.5 trillion–$2 trillion in accumulated technical debt among Global 2000 enterprises, based on responses from 608 IT and business leaders. It is an analyst estimate, not a disclosed balance-sheet total.

• A family of practitioner surveys reported 653 valid responses from industry practitioners in six countries: 47% had practical experience identifying or managing technical debt, 22% had theoretical knowledge only, and 31% had never heard of it. The InsighTD research paper is older survey evidence, not a current global benchmark.

• Google Cloud’s 2025 DORA report announcement says its research includes survey responses from nearly 5,000 technology professionals. It found a positive relationship between AI adoption and throughput or product performance, but a negative relationship with delivery stability; DORA reports relationships, not a direct technical-debt measure.

• Sonar’s January 2026 developer survey of more than 1,100 professional developers found that respondents said AI accounted for 42% of committed code at the time of the survey. They expected 65% by 2027, which is a forecast from respondents rather than an observed result.

• Sonar’s February 2026 survey reports that 88% of software developers experienced at least one negative AI impact on technical debt, while 93% reported at least one positive impact. These are developer-reported experiences, not causal estimates.

Get the Mobile Testing Playbook Used by 800+ QA Teams

Discover 50+ battle-tested strategies to catch critical bugs before production and ship 5-star apps faster.

Email addressGet Instant Access

100% Free. No spam. Unsubscribe anytime.

The numbers at a glance

The table is a reference map, not a total. A modeled workday, an IT-spend share, a code classification, and a survey response answer different questions. Keep the publisher, population, period, and evidence type attached whenever you reuse a figure.

What technical debt costs

The most defensible way to discuss technical-debt cost is to start with the unit you need for a decision.

For an enterprise-budget conversation, SIG’s 21%–40% of total IT spending framing describes a share of spending within its benchmark. For portfolio-scale remediation capacity, CAST’s 61 billion workdays is a modeled global burden built from code analysis, not calendar time booked by employers. For a project-level discussion, Sonar’s $306,000 and 5,500 developer hours per year for a one-million-line project is a model based on its 2023 project sample. HFS’s $1.5 trillion–$2 trillion estimate is broader still: an analyst estimate of accumulated debt in Global 2000 enterprises.

Those figures point to a material problem, but they cannot be added together. They use different populations, assumptions, and units. Your business case becomes more credible when you choose the measure that fits the decision rather than converting each one into a false common total.

Using software-quality cost statistics can help teams choose defensible metrics without collapsing unlike technical-debt measures into one number.

How common are structural technical-debt problems?

Code-analysis benchmarks describe structure, not the amount of time your developers will spend fixing it. SIG’s maintainability and security-control classifications identify areas that may deserve investigation under SIG’s rules. CAST’s fragile, bloat, and rigidity labels identify different kinds of structural weakness in CAST’s analyzed code. Neither publisher’s classification should be presented as a census of all software.

The InsighTD surveys add practitioner context. Among the 653 valid responses, deadline and time pressure was the most-cited cause of technical debt. The paper reported design debt at 21.99%, test debt at 19.94%, code debt at 14.66%, architecture debt at 10.7%, and documentation debt at 9.09% by occurrence. Those percentages describe the survey’s categorization of reported debt types, not their cost or severity.

That distinction matters when you set priorities. A high occurrence rate can help you decide what to inspect, but it cannot tell you which issue has the greatest release or customer impact. Pair structural findings with your own incident, rework, and change-failure records before assigning remediation capacity.

Does technical debt affect delivery stability?

The practitioner evidence supports a plausible delivery concern without supplying a universal conversion rate. The InsighTD research identifies delivery delay, low maintainability, and rework as prominent reported effects of technical debt. Those are reported experiences, not a large-scale estimate of how many hours of delay every debt item causes.

DORA supplies adjacent delivery evidence. Its 2025 research found AI adoption positively related to throughput and product performance, while being negatively related to delivery stability. You should not recast that relationship as proof that AI-generated code creates technical debt, or that technical debt alone caused instability. It does, however, support measuring speed and stability separately when you change a development workflow.

If AI-assisted code is part of your delivery system, your AI testing statistics should inform the testing discussion, but they do not replace repository-level measures of maintainability or rework. Use release evidence to decide whether a faster authoring workflow is preserving stability.

AI and technical debt in 2026

The current AI evidence has three different forms: benchmark observation, developer self-report, and delivery-system correlation.

SIG’s benchmark observation is conservative in scope: 1.9% of enterprise production code was AI-generated. Its finding of roughly twice the security-risk violations in AI-generated code is a reason to review the benchmark’s definitions and controls, not a universal multiplier to paste into a risk register.

Sonar’s January 2026 survey shows a different measure: respondents said AI made up 42% of their committed code, and expected it to reach 65% by 2027. The same survey found 96% did not fully trust AI-generated code, and 48% said they always verified it before committing. These figures describe what surveyed developers reported, including an expectation about the future.

Sonar’s February 2026 survey goes directly to perceived debt impact: 53% of respondents attributed a negative impact to AI producing code that looked correct but was unreliable, and 40% said AI increased debt by generating unnecessary or duplicative code. The reported positive effects alongside the negative ones are a useful warning against a one-sided conclusion.

For QA leaders, the practical implication is to verify behavior as well as code appearance. The broader state of QA automation can help you frame that operating question, while this report’s numbers show why AI adoption and reliable delivery should be tracked as separate outcomes.

What the public data cannot tell you

The limits in the evidence are as decision-useful as the headline figures.

1. No cited source provides one comparable 2026 percentage of engineering time spent specifically on technical debt. SIG reports an IT-spend estimate; CAST models remediation workdays; Sonar models project-level hours; and surveys capture perceptions. None is a clean substitute for the others.

2. No cited source establishes a large-scale, direct technical-debt-to-MTTR or incident-frequency estimate. Public research contains signals about rework and delivery stability, but not a universal conversion from debt to outages or recovery time.

3. No cited source measures under controlled conditions how much AI-assisted development creates versus resolves technical debt. The available evidence consists of code-analysis observations, reported experiences, and relationships between variables—not a controlled causal experiment.

These gaps are a reason to build an internal baseline. Track the measures you can consistently define: remediation time for selected categories, escaped defects associated with changed components, release rollback or recovery outcomes, and the verification rate for AI-assisted changes. Do not claim that your internal measures are industry benchmarks unless their population and method support that comparison.

No first-party Quash data covers technical-debt statistics. That means there is no Quash benchmark to add to these public figures without guessing.

How to use tech debt statistics in planning

Use an IT-spend share or accumulated-burden estimate to open a portfolio conversation, not to forecast a precise local budget. The purpose is to show that debt competes with feature investment for capacity.

Use code-analysis classifications to choose where to inspect more closely. SIG’s maintainability and security-control indicators, or CAST’s structural categories, can support a triage hypothesis. Your own change history should determine whether that hypothesis earns funding.

Use modeled project cost for a scenario, with the assumptions visible. Sonar’s one-million-line model may be relevant to a similarly sized system, but it is not evidence that every project has the same cost structure.

Use AI surveys and delivery findings to design controls rather than to declare a verdict on AI. A reasonable control might require review and behavior-level verification for AI-assisted changes in high-risk flows. If those flows run through mobile apps, your mobile QA automation approach should account for the device and platform combinations that matter to your release.

Methodology and source notes

This report prioritizes sources that own the underlying benchmark, survey, or model: SIG, CAST, Sonar, HFS, Google Cloud’s DORA program, and the InsighTD research authors. It separates benchmark estimates, code-analysis classifications, modeled remediation burden, analyst estimates, and survey responses because they are not equivalent evidence.

The figures are recent where current tooling and AI adoption are concerned. Sonar’s 2023 cost model remains because it documents its project sample and method, but it is labeled as historical modeled context rather than a current market rate. CAST’s report is branded 2025 and its related release is dated September 24, 2025; its page copyright does not change the report’s publication period.

The useful conclusion is not that every organization carries the same amount of technical debt. It is that public tech debt statistics can help you choose a question—budget pressure, structural quality, remediation capacity, or workflow control—if you keep each number in its original context. Your next decision should be to measure the equivalent question in your own systems, using a definition your engineering and finance stakeholders can both inspect.