6 QA Testing Trends Australia Must Watch For in 2026

The latest QA testing trends in Australia focus on real-time quality validation, increased automation, and the use of AI to enhance test workflows. Australia is also making steady progress in this area, with the software testing services market expected to grow by 12.3% CAGR between 2024 and 2029, adding about USD 1.7 billion.

Australian organisations, especially in regulated sectors like fintech, health‑tech, and government, are accelerating their shift from linear QA to continuous testing, observability, and integrated release pipelines. 

At the same time, challenges such as a shortage of talent for modern test automation, complex hybrid/edge architectures, and stricter local regulations persist. 

This blog will explore 6 key QA testing trends set to happen in Australia in 2026, and offer actionable steps you can take now to stay ahead.

Key Takeaways

• Shift-left and shift-right testing: Integrate both approaches for continuous production validation and earlier-stage feedback loops.
• Use AI in testing: Use AI to highlight high-risk modules, generate test data, and optimise regression suites, improving test reliability.
• Low-code tools for non-technical teams: Empower business analysts and product owners to contribute to testing, accelerating feedback and improving agility.
• Cloud-native and automated testing: Adopt cloud-based test environments and automated performance testing to ensure scalability and faster releases.
• Align QA metrics to business outcomes: Focus on customer satisfaction, uptime, and regulatory compliance instead of just defect counts.

Why QA Testing Needs a Reset in Australia Now

Despite the rapid drift towards cloud‑native architectures, microservices, mobile platforms, and IoT endpoints, many Australian organisations continue to rely on legacy QA models designed for monolithic systems. 

These outdated approaches focus on big‑bang end‑of‑cycle testing rather than continuous validation, resulting in a slower release cadence, lower coverage, and a higher failure risk.

What’s Driving the Change in Australia

The shift towards continuous delivery and more complex tech stacks is forcing Australian businesses to rethink traditional QA practices. 

Legacy models, which test only at the end of the cycle, can no longer keep up with the pace of change. These factors are driving the need for faster, more agile, and continuous QA processes.

Key drivers of change in Australia include:

• Faster Release Cycles: CI/CD practices push for faster, more frequent releases, requiring continuous testing at every stage.
• Security and Regulatory Demands: Industries with stringent compliance requirements demand more rigorous, real-time testing to meet security standards.
• Proliferation of Devices: The rise of mobile, IoT, and edge devices requires QA to ensure consistent performance across diverse environments.
• Hybrid and Remote Architectures: The shift to cloud and hybrid infrastructures creates additional testing challenges across both on-premises and cloud-based systems.

Why This Matters for You

If your QA process is still slow and manual, you risk:

• Defects in Production: Delayed testing can let defects reach customers, damaging your brand and user trust.
• Slower Time-to-Market: Traditional QA methods lengthen release cycles, giving competitors an edge.
• Higher Fixing Costs: Issues caught late in the process are more costly to fix.
• Compliance Gaps: Outdated QA fails to meet the growing regulatory and security demands, exposing your business to risks.

Also Read: Manufacturing Automation: What’s Changing for Enterprises in 2025

Now that we’ve outlined the issues with traditional QA models and the forces driving change in Australia, let’s dive into the five key QA testing trends that you cannot afford to ignore for 2026. 

Trend 1: Testing Moves from Pre‑Release to Continuous Production Validation

In 2026, QA will no longer be limited to pre-release testing. The focus has shifted to continuous production validation, where teams monitor live traffic, gather real-time feedback, and assess real-user behaviour. This Shift-Right approach works alongside the traditional Shift-Left model, creating a hybrid strategy for continuous validation throughout the development lifecycle.

For Australian businesses, particularly SaaS providers, mobile apps, and cloud workloads, this means real-time testing is essential to reduce outages, enhance user experience (UX), and support smoother continuous delivery. 

Tools and Techniques

• Production Observability: Monitoring live production environments for anomalies.
• Synthetic Transactions: Automating tests that mimic real-user interactions.
• Chaos Engineering: Simulating failures to test system resilience.
• Feedback Loops and Analytics: Using user feedback and data insights to iterate quickly.

Business Benefit

• Faster Issue Detection: Catch problems early in production, not after a customer report.
• Fewer Critical Failures: Real-time monitoring means fewer service outages.
• Improved User Satisfaction: Continuously improving based on live data keeps users happy.
• Cost Avoidance: Identifying issues early reduces the cost of post-release fixes.

Are outdated systems and manual workflows slowing down your growth? Codewave’s Digital Transformation Services can help modernise your infrastructure, improve operational efficiency, and get real-time insights for better decision-making. 

Whether it’s Application Modernisation, Agile Infrastructure, or Real-Time Intelligence, we have the solutions to accelerate your business.

Trend 2: AI‑Assisted and Autonomous Testing Takes Off

AI and machine learning are rapidly transforming QA by automating repetitive tasks and improving test efficiency. Predictive defect detection, self-healing scripts, and generative test data creation are just a few ways AI is being embedded into testing workflows. 

In Australia, where the skilled testing workforce is in short supply, automation paired with AI offers a scalable, cost-efficient solution to meet the demands of modern software development.

Application Examples

• Predictive Risk Modelling: AI prioritises test cases for components most likely to fail.
• Generative Test Data: Automatically generates test data to simulate various edge-case scenarios.
• Self-Healing UI Tests: Automatically adjust test scripts to accommodate frequent UI changes.

Challenges and Risks

• Tool Maturity: Not all AI tools are ready for enterprise-scale testing.
• Data Quality: AI requires high-quality, well-organised data to function effectively.
• AI Governance: Ensuring AI-driven tests are valid and aligned with business goals can be challenging.
• Skill Gaps: 58% of enterprises are upskilling their QA teams in AI and cloud testing, but there’s still a shortage of skilled professionals in this area

Business Benefit

• Shorter Testing Cycles: Automation speeds up repetitive tasks, allowing quicker release cycles.
• Fewer Regressions: AI’s predictive capabilities reduce the chances of regressions.
• Lower Maintenance Costs: AI-driven testing tools require less manual intervention, reducing long-term maintenance costs.
• Faster Time-to-Market: AI’s efficiency improves development speed, leading to quicker launches.

Also Read: AI in Project Management: Tools and Best Practices 

Trend 3: Low‑Code/No‑Code and Democratisation of Testing

Low-code/no-code platforms help empower non-technical staff, such as business analysts and product owners, to participate in testing. This shift reduces bottlenecks and accelerates feedback loops, especially for smaller teams without large QA resources. In fact, 84% of enterprises now use low-code technologies to speed up delivery and reduce IT burden. 

In Australia, industries like agriculture tech and logistics are adopting low-code tools to improve testing without relying on specialized testers. These tools make testing faster, more scalable, and more accessible for everyone involved.

Techniques and Tools

• Codeless UI Automation: Automate UI tests without coding.
• Visual Test-Flow Design: Drag-and-drop interfaces to create test scenarios easily.
• Scriptless Integrations with CI/CD: Low-code tools integrate directly into CI/CD pipelines.
• Business-Driven Test Case Creation: Allow business teams to define test cases based on user needs.

Benefits

• Fewer Bottlenecks: Less reliance on specialist testers speeds up testing.
• Shorter Feedback Loops: Quicker testing and faster feedback.
• Increased Agility: Non-technical teams help speed up development.

Implementation Caution

• Governance: Ensure low-code testing is well-governed for quality.
• Full QA Integration: Avoid siloed testing; integrate it into your overall QA strategy.

Business Outcome

• Faster Iterations: More teams contributing to testing leads to quicker iterations.
• More involvement in Quality: Collaboration across teams improves product quality.

Trend 4: Quality Strategy Anchored to Business Metrics and Risk, Not Just Bugs

Traditional QA focuses primarily on defect counts, but this approach is no longer enough. In 2026, leading companies are aligning QA with business outcomes, such as uptime, reliability, user satisfaction, and regulatory compliance. 

In Australia’s highly regulated industries, such as fintech, health-tech, and government, QA must be treated as a business discipline. Research shows 81% of executives tie software quality to customer satisfaction and revenue.

What to Do

• Define QA KPIs: Measure the business impact of QA, such as “% of user journeys without crash,” “MTTR of production incidents,” or “mean time between defects.”
• Risk-Based Testing: Focus on testing the most critical processes that directly impact revenue or user experience.

Frameworks

• Risk Matrices: Map test cases to business value to ensure critical areas are tested first.
• Security Testing: Include penetration and security testing as part of the core QA strategy, not as a separate function.

Business Benefit

• ROI Justification: A business-driven QA approach allows you to justify testing budgets and demonstrate value.
• Strategic Asset: QA becomes a key business function, driving better decisions and outcomes across the company.

Trend 5: Skills, Tooling, and Ecosystem Must Evolve – Australia’s Gap and What You Can Do

As testing environments evolve, so too must the skills and tooling in your QA team. The rise of cloud-native applications, microservices, mobile-first designs, IoT connectivity, and AI-driven systems demands new testing competencies. 

The Australian market is seeing an 11.4% CAGR growth in testing services, but businesses are struggling with fragmented toolchains and a shortage of skilled testers in emerging technologies. Many organisations need to bridge the skills gap in cloud-native testing, AI/ML literacy for QA, and observability.

Key Skill Areas

• Cloud-Native Testing: Testing in distributed, multi-cloud environments.
• Container/Microservice Testing: Validating microservices in a containerized environment.
• Security and Penetration Testing: Testing for vulnerabilities in complex systems.
• AI/ML Literacy: Understanding how AI/ML can be leveraged in testing.

Tooling Trends

• Unified QA Platforms: Platforms that handle UI, API, security, and performance testing.
• Playwright and Native Mobile Testing: Tools like Playwright and mobile testing frameworks to handle complex modern apps.
• Cloud Test Environments: Testing platforms that scale in the cloud, reducing costs and increasing flexibility.
• Test Data Management: Tools for creating, managing, and securing test data.

What You Should Do

• Audit Your QA Team Skills: Identify any gaps in your team’s skills relative to your tech stack.
• Consolidate Your Toolchain: Streamline testing tools to reduce inefficiencies and integration problems.
• Invest in Training: Upskill your team to stay up to date with modern testing practices.

Struggling with delayed releases and costly post-launch fixes? Codewave’s Automation Testing Services ensure your apps are bug-free, fast, and ready for deployment. With faster testing, improved accuracy, and comprehensive coverage, you’ll release with confidence. Discover how Codewave can help you automate your testing today.

Trend 6: Cloud‑Native Testing and Test Automation for Modern Architectures

Australia’s move to cloud-native systems, microservices, and containerised applications is driving a shift in QA testing strategies. Traditional testing methods simply can’t keep up with the scale and complexity of these distributed systems. 

As businesses increasingly adopt cloud-based testing, many are seeing reductions of 60–70% in infrastructure costs and 10x acceleration in test cycle times.

Key Tools and Techniques

• Cloud-Based Test Environments: Scalable, on-demand testing environments in the cloud.
• Containerised Testing: Isolated testing of microservices in containers like Docker.
• Automated Performance Testing: Simulating real-world conditions to test scalability and performance.
• Dynamic Test Environments: Quickly creating and destroying test environments using cloud resources.

Why It Matters for You

As Australian businesses increasingly adopt cloud and hybrid systems, traditional testing methods won’t keep up. Cloud-native testing allows for faster, more scalable testing, enabling quicker releases and reducing the risk of post-deployment issues.

What You Should Do

• Automate Cloud-Based Testing: Implement tools suited for cloud-native environments and microservices.
• Use Containerised Testing Tools: Leverage Docker and Kubernetes for isolated testing.
• Integrate Performance Testing: Incorporate automated load and stress testing to ensure product stability.

Business Outcome

• Efficiency: Faster, more accurate testing improves delivery timelines.
• Faster Releases: Cloud-based testing speeds up development cycles.
• Better Reliability: Ensures robust performance under real-world conditions.

Codewave: Driving Innovation in QA Testing for the Future

At Codewave, we are at the forefront of digital transformation, delivering innovative QA testing solutions tailored to modern business needs. 

With over 400 successful digitisation projects, Codewave helps enterprises with AI-powered automation, enabling faster, more reliable releases, a better user experience, and reduced operational risks.

Our Approach to QA Testing

• AI-Powered Automation: We incorporate AI and machine learning to automate repetitive tasks, reduce human error, and accelerate testing cycles.
• Cloud-Native Testing: Our cloud-based test environments are scalable and flexible, allowing businesses to run tests efficiently across multiple platforms.
• Continuous Production Validation: By integrating Shift-Left and Shift-Right strategies, we ensure continuous quality validation throughout the entire development process.
• Custom Solutions: We create bespoke testing solutions for businesses, ensuring that each strategy aligns with their unique requirements and goals.

Explore our portfolio to see how Codewave has successfully transformed QA testing for companies across industries such as fintech, health tech, and retail.

Conclusion

To remain ahead of the curve in 2026, use AI where it adds value. Let AI highlight high-risk modules, generate test data for edge cases, and optimise regression testing.

 Additionally, empower non-technical teams, such as business analysts, product owners, and marketers, to directly contribute to testing through low-code tools. This approach accelerates feedback, cuts bottlenecks, and improves agility. 

If you’re ready to optimise your QA strategy and accelerate your release cycles, Codewave is here to help. We design solutions that scale with your business, ensure reliability, and reduce time-to-market. 

Get in touch to learn how we can support your next digital transformation.

FAQs

Q: How can AI help identify high-risk areas during testing?
A: AI can analyse historical data and patterns to predict which parts of your application are most likely to fail, allowing you to focus testing efforts on the highest-risk areas. This reduces the chance of critical failures after release and improves resource allocation.

Q: How can low-code tools improve collaboration between technical and non-technical teams?
A: Low-code tools enable business analysts and product managers to create and execute tests without relying on developers, fostering collaboration across teams. This speeds up testing cycles and ensures business requirements are met without delays caused by technical bottlenecks.

Q: What challenges should we consider when transitioning to cloud-native testing?
A: Transitioning to cloud-native testing requires ensuring your team is equipped with the right skills for cloud infrastructure, adapting your test environments to handle dynamic scaling, and choosing the appropriate tools for continuous integration and testing in cloud ecosystems.

Q: How does automated testing impact the cost of quality assurance in the long term?
A: While the initial investment in automated testing tools and training can be high, the long-term savings come from faster testing cycles, fewer post-launch bug fixes, and the ability to scale testing as your product grows. This reduces overall maintenance costs and improves testing efficiency.

Q: How do you ensure that automated testing is still effective in dynamic environments like microservices?
A: To ensure effectiveness, it’s critical to update automated test scripts as the microservices architecture evolves continuously. Implementing self-healing scripts and using containerised testing environments ensures that automated tests remain relevant and resilient to changes in your microservices ecosystem.

Q: How can I measure QA performance beyond defect counts?

A: Replace raw bug counts with metrics such as “percentage of error‑free user journeys,” “mean time to recovery,” and “production incident down‑time.” These tie test efforts directly to user satisfaction and operational cost.