Software Testing Trends & Expectations for 2026

Software Testing Trends: Latest Quality Assurance Trends for 2026

We’ve seen immense shifts in software testing and QA over the past few years. Much of this has been driven by advancements in generative AI, but some of these tendencies are vendor-driven without consideration for customer convenience or data governance. Keep reading to see our picks for the top software testing trends heading into 2026.

Summary of trends:

Current/Established Trends

Many trends that started or gained traction in 2024 or 2025 will continue into 2026. Our picks for the most relevant software testing trends that will carry into 2026 include:

1. Self-Healing Test Automation

Self-healing test automation enables test scripts to adapt as your application or system changes (e.g. when a UI element is renamed or moved). This has been around in more basic implementations for years at this point, but more recent advancements have elevated these capabilities. By using AI/ML techniques, advanced self-healing test automation (like that found in Rapise) testing tools can expand their capabilities to include historical element locator data, patterns of previous UI changes, fuzzy matching, and even recovery strategies for alternate selection logic when a test fails.

This is critical for modern software development, as UI changes happen faster (especially in Agile projects) and maintenance overhead increases. Advanced self-healing tests will continue to improve in 2026, helping teams spend less time fixing tests and more time fixing their app. Market leaders like Rapise allow for better test coverage across more features, devices, and platforms, resulting in a higher-quality product with fewer risks.

2. Convergence of QA & DevOps

We’ve also seen the shift-left (or shift-everything-left) mindset continue, with QA becoming less and less isolated. Instead of happening after development, it’s woven into CI/CD pipelines with tests that are triggered on every commit, build, merge, or deployment. In fact, we’ve already been seeing some in the industry refer to this as “QAOps” as QA teams collaborate more closely with DevOps teams.

When testing and QA become less siloed, it facilitates faster feedback cycles to catch bugs, regressions, or integration issues earlier. This directly contributes to lower costs and reduced risks, saving your organization valuable time and budget. This convergence also means that testing is no longer a singular bottleneck at the end of development, it’s an embedded enabler that aligns QA with rapid and Agile delivery. For example, SpiraTeam or SpiraPlan act as a central QA hub for DevOps pipelines instead of bolting a separate QA tool on at the end.

3. Low-Code/Codeless Testing Tools

As AI has lowered the barrier to entry for coding and other development activities, low-code (and no-code) testing tools have gained massive popularity. These typically feature visual test builders, drag-and-drop workflows, natural language test definitions, and other intuitive tools. The result is more accessible test automation for non-developers, such as QA analysts and product owners.

While some may see this as an unnecessary complexity or hurdle for more experienced developers, these platforms can still speed up test creation, improve coverage, and support more cross-functional collaboration (even in technical environments). It also accelerates time-to-test by making it easier to edit or modify tests without frustrating delays. Thankfully, Rapise is designed with hybrid testing approaches in mind, so team members can be flexible with scriptless or scripted testing. Its Rapise Visual Language (RVL) looks like an Excel spreadsheet, making it familiar to set up actions, loops, and data inputs without writing any code.

4. The Push to Cloud SaaS

Speaking of flexibility, deployment options have also been restricted, with many organizations forced into less-than-ideal scenarios. Lots of major vendors (including Atlassian, AWS, Azure, Salesforce, and more) have recently pushed for cloud-only platforms, if not requiring cloud deployments outright. While this can bring some conveniences to customers, it’s primarily done so that vendors can save on overhead and other costs. This has become increasingly prevalent in testing and QA tools, but it can have significant downsides.

Besides the simple lack of flexibility or choice, on-prem deployment is still incredibly important for many organizations that prioritize data control and governance. Cloud-only platforms introduce significant concerns over data breaches, multi-tenancy, compliance, and more, which all complicate QA activities — especially security testing. This migration to the cloud reduce the control that companies have over their data and governance requirements. Inflectra is one of the few major vendors that offers both cloud and on-premises (air-gapped) deployment options for these organizations that require more visibility and administration of their data and testing activities.

5. AI-Driven Risk Management

Another area that AI developments have bolstered is risk management and mitigation. AI-driven tools and methods are increasingly being applied to risk-based testing, test selection optimization, and test case prioritization for more efficient QA. This can also result in more data-driven decisions about testing instead of more subjective selections/prioritizations or brute force “run everything” regression tests.

Codebases are endlessly growing as applications and integrations get more complex and capable, which diminishes the practicality of running every single test. Thus, the importance of risk-based and AI-driven selection helps balance coverage with speed. SpiraPlan and Inflectra.ai combine to provide your team with powerful tools that speed up their workflows instead of getting in their way. SpiraPlan’s dashboards and advanced risk management features upgrade your testing strategies for a faster time-to-market with fewer risks.

6. Test Data Management & Synthetic Data

Generative AI has also increased the feasibility of creating synthetic test data to replace or augment sensitive, regulated, or user-specific information. However, it’s critical that this data is of high quality and not lazy data that will degrade the effectiveness of your tests and QA. TDM is the full process of generating, anonymizing, managing, and governing these datasets for testing and typically enforces key compliance guidelines like privacy and data protection laws.

As mentioned, the quality of this data is vital to the success of its use, and AI models have only recently reached the ability to generate truly realistic synthetic data. This enables better coverage of edge cases, more compliant processes that don’t require production data for testing, and more thorough management of complex data models, multi-tenant SaaS, and data privacy regulations. Rapise can quickly generate synthetic test data that mimics real users with diverse coverage for more efficient testing — without the privacy risk.

Emerging/Upcoming Trends

However, many trends are still emerging or on the horizon. These are longer-term and less established or recognized than the previous trends we discussed, but will be equally important in the coming years. Some of the biggest upcoming software testing trends we anticipate include:

1. Agentic Testing & QA Assistants

While we’ve seen some coding assistants popping up (e.g. GitHub Copilot, Qodo, and Bolt) with mixed effectiveness and adoption, 2026 will likely start to see the emergence of true agentic testing. In other words, we’ll likely see more autonomous agents that can read requirements, infer test plans and test cases, generate test code, execute tests, orchestrate multi-layer testing, and even maintain their test suites over time.

This goes beyond simple automation or debugging suggestions and towards truly helpful QA assistants. The result is significantly less human effort dedicated towards test creation and maintenance, freeing up time for higher-value tasks and strategic decisions. This is especially helpful for large and fast-moving organizations with rapidly evolving codebases. It also further enhances consistency and accelerates time-to-test, providing fully automated and versioned test suites based on requirements. Our Inflectra.ai tool is already an incredibly powerful tool and assistant, but we’re currently refining and improving it by working on new additions like autonomous agentic workflows that can execute entire work streams (so stay tuned).

2. Predictive Quality Engineering

Similar to AI-based test selection discussed in the risk management section above, predictive quality engineering aims to use historical data to proactively anticipate risk areas and guide QA strategy. This will primarily be based on data like previous defects, code churn, module complexity, test results, failure patterns, and more. This becomes more and more important as codebases and test suites scale, which we also discussed in the risk management section above.

Not only does it keep QA teams up-to-date and ahead of potential issues with data-backed decisions and recommendations, but it also helps maintain quality while optimizing resource usage. This makes it easier to identify where to invest effort, which modules need more regression coverage, and much more. This can even translate into competitive advantages by being more agile than others in your industry, which has become increasingly important as development and deployment speeds up.

3. Sustainable Software Testing

As awareness of our environmental impact grows, there is a corresponding increase in the interest and value of “sustainable testing.” In essence, this involves minimizing energy and resource consumption during testing, such as compute usage and the number of redundant tests run. The goal is to optimize your test suite in order to reduce carbon footprint, specifically designing QA practices to be mindful of resource efficiency.

This might involve smarter test selection, optimized environment provisioning, avoiding redundant tests, using virtualizations/containers more efficiently, and other methods. While the focus is on environmental impact and sustainability, more efficient resource usage also translates to cost reductions (especially for larger organizations) and a competitive differentiator in enterprise procurements. As the costs of cloud hosting and energy rise, efficiency transitions from a “nice to have” into a much more economically relevant factor. Thankfully, Inflectra platforms are lightweight and optimized for efficiency (unlike heavy Java-based ecosystems).

4. AI Red Teaming & Adversarial Resilience

With cybercrime on the rise and new attack vectors being created by generative AI and advanced computing, software QA must evolve beyond simple functional correctness. The core objective of AI red teaming and adversarial resistance is to mitigate the expanding attack surfaces of new AI and ML features. This requires embedding security and compliance into your QA processes and pipeline from the beginning, instead of a simple add-on.

Adversarial resilience is a key part of repelling threats like malicious actors, supply chain attacks, and more. It will involve more rigorous testing of security boundaries, more advanced attack simulations, and more thorough input sanitization. On top of these, teams will also need to verify defenses against injection vulnerabilities and malicious inputs like data poisoning.

5. Quantum Readiness Testing

We’ve seen major announcements and advancements in quantum computing recently, from companies like Google, IBM, and Microsoft. While this is exciting news, it also further opens the door to more complex codebreaking and cyber attack methods, easily bypassing current security standards and encryption.

This is where quantum readiness testing comes in — verifying that your apps and systems will be prepared for these shifts (preferably sooner rather than later). Companies in regulated industries with strict confidentiality requirements may need to incorporate this security earlier than you thought. We’ve previously covered the risks of this technology in more detail, so check out our deep dive below:

Learn more about the risks and challenges of post-quantum cryptography here.

6. Testing Neuromorphic Computing Systems

Neuromorphic computing is an emerging subset of AI that designs hardware to mimic the neural architectures of human brains. This is similar to neural nets, but incorporates specialized hardware like AI accelerators, neuromorphic chips, and edge AI devices. As these technologies become more accessible, software testing will need to start considering these cutting-edge environments and devices, especially for things like resource monitoring and fault tolerance.

This frontier is especially important for industries currently pushing the boundaries of AI (robotics, IoT, smart devices, etc.), meaning that it could be relevant sooner than many expect. As with other shifts, it’s critical for software testing to prepare for this now instead of playing catch-up once competitors have already moved ahead. This can also act as a competitive differentiator, highlighting your software’s forward-thinking resilience and future-proofing.

Future of Software Testing: Our Expectations

• Fully Autonomous & Adaptive QA Agents: Extending the QA assistant trend mentioned above, we expect fully autonomous agents to become more commonplace in software testing. They will build and maintain test suites, reducing script brittleness, lowering maintenance costs, and speeding up software feedback loops and delivery.

• Multimodal & Context-Aware Testing: Software testing will begin diving deeper into environments beyond static “lab conditions,” becoming more adept at simulating real-world contexts. This is key because many software failures already come from conditions like slow networks, concurrency, resource constraints, etc. instead of logic errors.

• Automated Security, Privacy, & Compliance Testing: We’ve discussed the importance of security testing at length, but the goal is to eventually automate this to more effectively keep pace with changing regulations. Also referred to as “policy-as-code,” this would manage increasingly complex infrastructure to maintain compliance.

• Testing for AI/ML Systems: Software platforms like the ones you build are already integrating AI-powered tools and features, which is only expected to expand and accelerate. Therefore, it will become more and more important to thoroughly test these AI models for drift, bias, performance, and more, incorporating new testing methods into your QA pipeline.

• Crisis of Interoperability: Software architecture has continued to grow increasingly modular (microservices, APIs, cloud services, on-prem services, etc.), meaning that testing the interactions between all of these systems is turning into a full-time job. Users expect data to flow seamlessly, provide backward/forward compatibility, and offer service-to-service communication. This means new tools and techniques to manage the ever-growing complexity to avoid interoperability issues.

Future-Proof Your Software Testing & QA with Inflectra

We’ve seen how software testing has rapidly evolved, accelerated further by advancements in automation, AI and computing. What use to be simple functional testing is being outpaced by more complex architectures, demanding delivery cycles, regulatory pressures, and soaring user expectations. The key to future-proofing your processes is finding a parter, not a simple tool — a partner that works tirelessly to provide the best platforms that keep pace with broader innovation and customer expectations.

You need a unified, flexible, and intelligent test management and execution suite, which Spira and Rapise provide. They come together to consolidate traditionally disparate tools and features into a centralized pane of glass to perform and oversee all testing and QA activities. From self-healing tests and generative AI-driven workflows to unmatched tranceability and auditability for compliance, Inflectra’s suite of software is the ideal environment for future-proofed testing and QA. With powerful risk management capabilities, a variety of testing types, and support for Agile, waterfall, and hybrid workflows, we provide the best foundation for modern development. Learn more about our ecosystem below:

All platforms also come with our cutting-edge Inflectra.ai to further enhance capabilities with truly useful and time-saving features.