Contents
SmartBear Study
Closing the AI Software Quality Gap
AI code generation is accelerating development, but at what cost? In our latest study, 70% of software experts report degraded application quality, and 60% experienced issues in the past year as development outpaces testing.
The challenge is no longer if AI will reshape development, but how to ensure application integrity when building 10x more apps at 10x speed.
Read the study to explore the top findings and learn how an application integrity foundation closes the software quality gap.
Survey demographics
SmartBear conducted a comprehensive survey of 273 software testing and quality decision-makers in January 2026. We captured a mix of technical leadership, QA directors, senior developers, and architects/engineers.
67% of respondents were director-level or above, with CTOs/CIOs, directors of QA/test, and senior software developers as the most common job titles.
500–4,999 employees
View full demographic breakdown
31% CTOs/CIOs, 26% QA directors, 22% Senior developers
What is your current job role?
83% are involved in testing, 80% in quality standards
Are you involved in, or accountable for, any of the following activities at your organization?
Software & SaaS = 79% of respondents
What industry does your company primarily operate in?
72% at companies with 500–4,999 employees
Approximately how many employees work at your organization?
67% director-level or above
What best describes your seniority level?
66% work at companies with $50M+ revenue
Approximately what is your organization's annual revenue?
Source: SmartBear Study, Closing the AI Software Quality Gap
What the data reveals
AI code generation has hit critical mass
93% have adopted AI coding tools; 40% now generate 41%+ of their code with AI — a figure expected to surge to 60% within 12 months.
Code quality is already suffering, and the anxiety is rising
70% are concerned quality is suffering now. 67% worry about further decline over the next 12 months. 60% have already experienced quality issues.
Testing cannot keep pace with AI-driven development
68% are concerned that faster AI development will create testing bottlenecks. 92% still test manually despite 87% having automation in place.
Concerns about testing persist
60% have already experienced quality issues from development outpacing testing. 64% are concerned applications aren't tested across all deployment environments.
Leadership doesn't fully grasp the risk
65% are concerned about under-investment in application-level testing. An equal 65% believe decision-makers don't recognize the AI testing risks.
Autonomous testing is the answer
92% say autonomous testing would at least moderately improve quality. 97% are increasing testing investment in 2026, with 86% increasing by 11% or more.
AI code generation has hit critical mass
The shift from traditional development to AI-assisted coding is here.
93% of respondents have already adopted AI coding tools, with 40% currently using AI to generate at least 41% of their code. 60% of respondents expect AI to generate more than 41% of their code in the next 12 months, up from the 40% who are already doing so today.
AI-generated code will jump from 40% to 60% at the 41%+ threshold
What percentage of your code is currently written or accelerated by AI tools? / What percentage do you expect in 12 months?
Source: SmartBear Study, Closing the AI Software Quality Gap
Code quality is already suffering, and the anxiety is rising
Software testing hasn't kept pace with AI code generation, resulting in acute pain points for software teams. 70% of respondents are concerned application quality is already suffering as AI accelerates development and produces more code and applications faster.
70% concerned about quality today – 67% worried about the next 12 months
How concerned are you that application quality is suffering / will suffer as AI produces more code and applications faster?
Source: SmartBear Study, Closing the AI Software Quality Gap
Testing can't keep pace with AI-driven development
The gap between AI-driven code creation and traditional application testing creates a bottleneck that the majority worry will only get worse. 68% of respondents are concerned faster AI development will create testing bottlenecks.
68% concerned faster AI development will create testing bottlenecks
How concerned are you that faster AI-driven code creation will create bottlenecks in testing and deployment?
Source: SmartBear Study, Closing the AI Software Quality Gap
Automation was supposed to be the answer, but it hasn't eliminated the manual burden. To date, 87% of teams have automated at least 21% of testing, yet 92% still test manually. Traditional automation can't adapt quickly enough to AI-generated code, leaving teams caught in the gap between development velocity and quality validation.
Automation hasn't replaced manual testing: 87% have automated at least 21% of testing, yet 92% still test manually
What percentage of your application testing is partially or fully automated?
Source: SmartBear Study, Closing the AI Software Quality Gap
Concerns about testing persist
As AI accelerates development, 60% of organizations experienced quality issues in the past year because development moved faster than testing could validate. The visibility gap across development, staging, and production environments transforms speed into risk — where teams are deploying with minimal visibility.
60% have already experienced quality issues from development outpacing testing
Has your organization experienced application quality issues attributed to development moving faster than testing can keep up?
Source: SmartBear Study, Closing the AI Software Quality Gap
64% concerned applications aren't tested across all deployment environments
How concerned are you that your applications are being tested across all the environments where they'll run?
Source: SmartBear Study, Closing the AI Software Quality Gap
Leadership doesn't fully grasp the risk
65% of technical leaders report that their organizations are under-investing in application-level testing compared to code-level testing.
An equal 65% believe decision-makers at their organization fundamentally misunderstand the AI testing risks. They're accelerating AI-driven development without recognizing that testing capabilities must scale accordingly. This disconnect at the leadership level creates a critical quality crisis – testing teams are left scrambling without the budget, tools, or headcount needed to validate software at the speed of AI-assisted development.
65% concerned about under-investment in application-level testing
How concerned are you that your organization is investing enough in application-level testing vs. code-level testing?
Source: SmartBear Study, Closing the AI Software Quality Gap
65% say decision-makers don't see AI testing risks
How concerned are you that your organization's decision-makers recognize the risks of faster AI-driven development without matching improvements in testing?
Source: SmartBear Study, Closing the AI Software Quality Gap
Autonomous testing is the answer
Organizations are responding to the crisis with investment in autonomous testing. Nearly all respondents (97%) plan to increase their testing investments in 2026.
Nearly all organizations (97%) planning to increase testing investment in 2026
How much do you expect your organization to increase application testing investment in 2026?
Source: SmartBear Study, Closing the AI Software Quality Gap
92% expect autonomous testing to at least moderately improve application quality, with 67% expecting significant or dramatic improvements. Unlike traditional automation, truly autonomous testing can adapt to AI-generated code, identify issues intelligently, and keep pace with accelerated development cycles.
92% say autonomous testing would improve application quality
How much would fully autonomous application testing improve your application quality?
Source: SmartBear Study, Closing the AI Software Quality Gap
Set a new standard with application integrity and BearQ™
As applications expand from human-coded to AI-augmented to AI-generated, today we examine how the volume and velocity of code outpaces teams’ ability to validate it. Autonomous software testing mitigates this escalating risk trajectory, eclipsing cumbersome, manual code-based testing to uphold application quality.
This is where application integrity becomes essential. Application integrity is a fundamental shift from asking "does your code work?" to ensuring your application experience matches the intended outcome. Application integrity provides continuous, measurable assurance that your software just works as intended – with governance to operate at AI speed and scale.
Learn how SmartBear is pioneering application integrity with BearQ, the agentic QA system with always-on teammates that help you uphold the highest application integrity standards.
Methodology: SmartBear conducted a comprehensive survey of 273 software testing and quality decision-makers in January 2026. We captured a mix of technical leadership, QA directors, senior developers, and architects/engineers. 67% of respondents were director-level or above. 83% are involved in testing and 80% in quality standards and governance. 72% work at companies with 500–4,999 employees. 79% of survey respondents work at software and SaaS companies.