QNX 2026 SDV Developer Report: Integration Consumes 35% of Effort

QNX 2026 SDV Developer Report: Integration Consumes 35% of Effort

Software-defined vehicles (SDVs) are reshaping automotive engineering, but software complexity continues to slow the industry’s transition from hardware-centric development to software-driven innovation. According to QNX’s 2026 Driving the Future: SDV Developer Report, automotive software developers spend an average of 35% of their engineering effort integrating foundational software rather than building differentiated vehicle features.

The report, released on October 22, 2026, surveyed 1,100 automotive software developers worldwide. Its findings highlight a growing disconnect between automakers’ ambitions for faster software delivery and the engineering resources consumed by integration, debugging, testing, and regulatory compliance.

The survey also identifies cybersecurity as an increasingly urgent concern, reveals more pragmatic expectations for artificial intelligence (AI), and highlights China’s distinct priorities around functional safety, security, scalability, and intelligent in-cabin experiences.

For automotive engineering teams, the central challenge is no longer simply adding software capabilities. It is establishing reusable, dependable software foundations that enable innovation without compromising safety, reliability, or delivery speed.

📊 Key Findings from the 2026 SDV Developer Report

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The survey identifies several interconnected challenges affecting automotive software development.

• 35% of engineering effort goes to foundational software integration: Developers spend a substantial portion of their time integrating software below the application layer instead of implementing differentiated features.
• 19% spend at least half their time on integration: For nearly one in five developers, foundational integration consumes 50% or more of their engineering effort.
• 76% favor application-layer innovation: Most respondents believe automakers should prioritize customer-facing software features over continued investment in software infrastructure construction.
• 75% identify a lack of reusable software foundations as a development bottleneck: Inconsistent foundations increase duplicated work across vehicle programs.
• 44% cite debugging and testing as their primary development challenge: This represents an eight-percentage-point increase from the previous year.
• 48% identify cybersecurity threats as a leading future concern: Meanwhile, 83% expect SDVs to become major targets for cyberattacks within the next three years.
• AI expectations are becoming more practical: Only 25% expect AI to have a transformative impact, down from 36%, while 62% expect it to play an important role in everyday workflows.

Together, these findings suggest that SDV development is entering a more demanding phase. As vehicle software becomes more sophisticated, engineering productivity increasingly depends on controlling system complexity and reusing validated components.

🧩 Software Integration Is Consuming Resources Meant for Innovation

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The SDV development model promises continuous feature updates, software-driven differentiation, and more flexible vehicle architectures. However, realizing those benefits requires automakers to integrate increasingly complex software stacks spanning operating systems, middleware, hardware abstraction, communications, diagnostics, and application logic.

The QNX survey suggests that foundational integration remains a major obstacle.

Developers spend an average of 35% of their effort on software integration below the automotive application layer. This work is necessary, but it does not necessarily translate directly into features that consumers can see or use. When teams repeatedly solve similar integration problems across vehicle programs, engineering capacity is diverted from product differentiation.

The problem is particularly pronounced when platforms, suppliers, tools, and architectural decisions vary between projects.

Why Reusable Software Foundations Matter

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A common software foundation can reduce duplicated integration work by providing validated components, standardized interfaces, and established development workflows.

Instead of rebuilding the same infrastructure for every vehicle model, teams can reuse tested software components and focus on vehicle-specific behavior and customer-facing capabilities.

The benefits can extend across several engineering activities:

• Integration: Standardized interfaces reduce incompatibilities between components.
• Testing: Reusable components and established test suites reduce duplicated validation work.
• Functional safety: Shared foundations can support consistent safety processes and evidence reuse where permitted.
• Maintenance: Common components simplify security updates and defect remediation across product lines.
• Scalability: Platform consistency makes it easier to support multiple vehicle configurations without duplicating the entire software stack.

Reuse does not eliminate the need for system-level integration or validation. Automotive software must still be tested against the specific hardware, safety goals, operational environment, and regulatory requirements of each vehicle program.

The objective is to reduce avoidable repetition while preserving the assurance activities required for safety-critical systems.

For additional background on embedded software and platform engineering, explore KAD8’s technical articles.

🚗 Application-Layer Innovation Is Becoming the Competitive Priority

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The survey found that 76% of respondents believe original equipment manufacturers (OEMs) should prioritize application-layer innovation over software infrastructure construction.

Although this remains a strong majority, the figure is down slightly from 80% in 2025. The change suggests that developers may be taking a more measured view of how quickly the industry can shift resources toward customer-facing innovation.

The application layer is increasingly important because it shapes the features that distinguish one vehicle brand from another. Infotainment, personalization, driver assistance, connected services, and intelligent cabin experiences can all influence customer perception.

However, application-layer differentiation depends on the quality of the underlying platform. A sophisticated interface cannot compensate for unstable middleware, inconsistent hardware interfaces, unreliable communications, or inefficient software deployment processes.

What Would Free Developers to Build Differentiated Features?

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Respondents identified several measures that could help teams devote more time to customer-facing development.

These findings point to a practical engineering strategy: improve the entire software delivery environment rather than focusing exclusively on application development.

Better-integrated tools can reduce context switching and configuration errors. Reusable components can limit duplicated implementation work. Pre-integrated software solutions can reduce the initial effort required to assemble a functioning platform.

The effectiveness of these measures depends on architectural consistency, documentation quality, automated testing, version management, and the ability to maintain shared components across multiple vehicle programs.

🔧 Debugging, Testing, and Scalability Are Increasing Delivery Bottlenecks

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The survey identifies debugging and testing as the leading challenge facing automotive software teams. A total of 44% of global respondents selected this issue, up eight percentage points from 2025.

It now ranks ahead of integration complexity, cited by 39%, and excessively long development cycles, cited by 37%.

This shift matters because debugging and testing difficulties often reflect deeper architectural and process problems. Inconsistent environments, poorly defined interfaces, hardware-dependent behavior, and inadequate automated test coverage can all increase the time required to diagnose and reproduce defects.

China’s Development Challenges Are More Pronounced

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The Chinese results reveal additional pressure as the market moves from rapid feature innovation toward broader deployment across vehicle platforms and product lines.

• 52% identify debugging and testing as a major challenge, compared with 44% globally.
• 47% cite insufficient software scalability across vehicle models, platforms, or product lines.

These findings indicate that engineering teams need development processes capable of supporting larger software portfolios without proportionally increasing integration and validation effort.

For example, an automotive software platform may need to support multiple electronic control units, hardware revisions, vehicle configurations, and regional requirements. Without clear interfaces and automated compatibility testing, each new configuration can introduce additional validation work.

A scalable SDV engineering process therefore needs more than a shared codebase. It also requires reproducible build environments, automated regression testing, reliable software configuration management, and traceability between requirements, implementation, and verification results.

Developer Confidence in Productivity Is Declining

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The proportion of respondents rating their development productivity as excellent fell from 30% to 20%.

This decline reinforces the report’s broader message: better-equipped development environments do not automatically translate into higher engineering throughput.

When teams spend increasing amounts of time diagnosing failures and reconciling inconsistent software components, productivity can deteriorate even as tools and infrastructure improve.

Dong Yuanwen, Chief Representative of QNX Greater China, described this as a complexity-management problem. His assessment emphasizes that repeatedly solving integration, development, and functional-safety challenges prevents teams from investing those resources in features that drivers can directly experience.

The implication is that productivity improvements require architectural and organizational changes, not simply faster coding or additional development tools.

🛡️ Cybersecurity and Functional Safety Are Rising Priorities

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Cybersecurity has become one of the report’s most prominent concerns. Nearly half of respondents, 48%, identify cybersecurity threats as a leading future issue, up five percentage points from the previous year.

In addition, 47% believe software vulnerabilities represent the biggest risk to scaling SDV deployment, while 83% expect software-defined vehicles to become major targets for cyberattacks within the next three years.

Connected vehicles introduce multiple potential attack surfaces, including wireless interfaces, telematics systems, mobile applications, cloud services, vehicle networks, and over-the-air (OTA) update mechanisms.

As software becomes more interconnected, security weaknesses can affect not only individual features but also vehicle-wide functions and the integrity of software updates.

China’s Emphasis on Security Capabilities

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Chinese respondents expressed even greater concern about cybersecurity.

The Chinese cybersecurity figure increased by 15 percentage points from the previous year, according to the report.

The results also indicate that functional safety and cybersecurity are increasingly viewed as complementary engineering disciplines. Functional safety addresses risks arising from system malfunction, while cybersecurity addresses malicious activity and other security threats. In connected vehicles, the two can intersect: a security compromise may trigger unsafe system behavior, while safety mechanisms must remain effective under relevant fault and threat conditions.

Regulatory Compliance Must Be Built into Development

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The survey identifies cybersecurity standards, including UNECE WP.29 and ISO/SAE 21434, as significant compliance challenges. A total of 46% of respondents cite cybersecurity standards among the most difficult requirements to satisfy.

These frameworks and regulations require engineering organizations to address security throughout the vehicle lifecycle rather than treating it as a final pre-release checklist.

Practical measures include threat analysis, secure software development, vulnerability management, incident response planning, supplier coordination, and appropriate documentation of security-related decisions.

The report also states that more than 700 regulations and legislative proposals affecting vehicle technologies were added globally during 2025, and that one-third of respondents experienced development delays associated with regulatory changes.

For global OEMs and suppliers, the challenge is to incorporate evolving requirements into reusable engineering processes while accounting for jurisdiction-specific obligations.

Common software foundations can help standardize parts of this work, but regulatory compliance still requires evidence appropriate to the product, applicable rules, and intended operating environment.

🤖 AI Is Moving from Transformative Expectations to Practical Engineering Support

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The report shows a shift in how automotive developers evaluate artificial intelligence.

The proportion expecting AI to have a transformative impact on development fell from 36% to 25%. At the same time, the share expecting AI to play an important role in daily workflows rose from 55% to 62%.

Overall, 87% of respondents still expect AI to play an important role in software development.

This does not necessarily indicate declining interest. Instead, it suggests that developers are increasingly evaluating AI through practical outcomes rather than broad predictions about transforming the entire development lifecycle.

Where AI Can Improve Automotive Software Workflows

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Potential applications include:

• Generating and reviewing routine code.
• Assisting with test-case creation and test-data preparation.
• Summarizing logs and helping identify likely failure causes.
• Improving technical documentation and requirements traceability.
• Supporting code analysis and vulnerability triage.
• Helping engineers navigate large codebases and configuration histories.

In automotive development, these applications must be evaluated against requirements for correctness, reproducibility, traceability, intellectual property protection, and safety assurance.

AI-generated code or test cases cannot replace engineering review, systematic verification, or required safety evidence. Its value depends on whether it reduces engineering effort without introducing unacceptable defects or additional verification burdens.

The report’s findings suggest that AI is increasingly viewed as another tool in the engineering workflow, with its value measured by its ability to improve productivity and reduce repetitive work.

China Places Greater Emphasis on Intelligent Cabin Experiences

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Chinese developers also expressed a distinct view of AI’s role in vehicle differentiation.

According to the survey, 26% of Chinese respondents believe AI will have its greatest impact on in-cabin experiences and personalization over the next three years, compared with 16% globally.

This difference reflects the importance of intelligent user experiences as a potential competitive differentiator in China’s automotive market.

Possible applications include personalized infotainment, conversational interfaces, context-aware assistance, and adaptive cabin settings. However, the practical value of these capabilities depends on reliable data access, low-latency processing, privacy protections, and integration with the vehicle’s underlying software architecture.

The same foundational challenges identified elsewhere in the report apply here: intelligent features can be developed and maintained more efficiently when the platform provides stable interfaces, consistent deployment mechanisms, and dependable security controls.

📈 What the Findings Mean for the Future of SDV Engineering

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The 2026 QNX survey portrays an industry moving beyond early enthusiasm for software-defined vehicles and into the harder work of delivering complex software systems at scale.

The findings point to four priorities for automotive engineering organizations.

1. Standardize Foundations Without Eliminating Flexibility

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Reusable software platforms can reduce duplicated work, but they must support product differentiation and accommodate vehicle-specific requirements.

The goal is to establish stable interfaces and validated components while allowing application teams to innovate independently where appropriate.

2. Treat Testing and Verification as Core Engineering Infrastructure

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The growing prominence of debugging and testing suggests that organizations need stronger automated validation, reproducible environments, and better diagnostic capabilities.

Investing in these foundations can improve defect detection and reduce the cost of maintaining multiple vehicle configurations.

3. Integrate Security and Safety Throughout the Lifecycle

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Security and functional safety cannot be postponed until final validation. Both need to inform architecture, development practices, supplier integration, verification, and maintenance.

Reusable processes can improve consistency, but evidence and validation must remain appropriate to each system’s risks and requirements.

4. Measure AI by Engineering Outcomes

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AI adoption should be evaluated through measurable improvements in development time, defect detection, test coverage, and maintenance efficiency.

For safety-critical systems, productivity gains must be balanced against the additional effort needed to validate generated outputs and maintain traceability.

🔎 Conclusion: SDV Innovation Depends on Reducing Software Complexity

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QNX’s 2026 Driving the Future: SDV Developer Report highlights a fundamental challenge facing the automotive industry: software complexity is consuming engineering resources that could otherwise be used to build differentiated vehicle experiences.

The finding that developers spend an average of 35% of their effort on foundational software integration illustrates the scale of the problem. Meanwhile, rising debugging and testing demands, expanding cybersecurity concerns, regulatory complexity, and declining confidence in productivity reinforce the need for more sustainable engineering practices.

The report also shows that AI remains important to developers, but expectations are becoming more grounded in practical workflow improvements rather than promises of wholesale transformation.

For automakers and suppliers, the competitive advantage will increasingly depend on their ability to combine reusable software foundations with reliable validation, secure architectures, and efficient application development.

The next phase of SDV development will not be defined solely by how many features a vehicle can support. It will also depend on how efficiently, securely, and consistently engineering teams can deliver those features across an expanding range of vehicle platforms.