Software Testing in 2030: 4 Ways QA Will Change

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This edition of Testing Times explores four key trends already taking root that will play an increasingly pivotal role in shaping the future of software testing by 2030.

The Future of Software Testing: 4 Trends Defining QA in 2030

Over the next five years, software and software testing are set to evolve at a rate we've never seen. In fact, it has already started. Over the last few years, everyone remotely involved in tech has witnessed the constant change in the way things are done. This seemingly non-stop innovation has been driven by emerging technologies, shifting development paradigms, and businesses reevaluating their priorities… and is set to accelerate.

As we've consistently seen with other technological revolutions, there will inevitably be winners and losers in this transformation.

Technological evolutions benefit large enterprises or newer companies unencumbered by legacy systems first—those with substantial financial resources or without technical debt can adopt innovations quickly and gain competitive advantages.?

However, if we consider the adoption of these technologies according to Everett Rogers’ Diffusion of Innovations model, the winner's circle isn’t limited to the innovators or early adopters; it also includes the more strategic early majority, who time their entry perfectly. This category takes minimal risk while riding the wave created by the innovators and earliest adopters.

Four Software Testing Changes by 2030

With these adoption patterns in mind, let's explore four testing trends that—in my opinion—organisations need to embrace to remain competitive in the evolving technological landscape.

Some of the coming changes won’t be wholly revolutionary but will follow this evolutionary path at ever-increasing rates. That’s not to downplay their impact; make no mistake, these changes will significantly affect the day-to-day activities of testers and QA teams. Others will be game-changing.

This edition of Testing Times explores four key trends already taking root that will play an increasingly pivotal role in shaping the future of software testing by 2030.

I’m not making these predictions on a whim. I’m involved with multiple testing projects across many different organisations and am closely tied to the development of the leading test tool vendor.

The evidence is all around us, and these predictions are based on my observations of current trends, AI's rapid rise and ongoing acceleration, and the ever-changing software landscape.

1. Enhanced Focus on UX Testing

The increasing sophistication of applications, coupled with ever-rising user expectations, has already made UX (User Experience) testing more important than ever.

This shift towards more comprehensive UX testing methodologies is necessary in the evolving digital world, where the rise of AI is driving more and more of our daily activity online.?

Fittingly, the most significant change in UX testing will be integrating AI-powered tools into the testing arena. While this integration is already underway, we can expect a significant acceleration in the coming years, with AI tools increasingly used to analyse user behaviour and emotional responses.?

The trend towards gathering real-time user feedback will also gain momentum. This will enable UX designers to make quicker, data-driven decisions based on user input and even allow interfaces to adapt dynamically to meet individual user needs.

This approach will facilitate continuous improvement cycles, requiring lightning-fast testing powered by AI-generated automation and efficient test packs.

By embracing these advanced UX testing methodologies, companies will be able to create truly user-centric applications. The result will be digital products that exceed increasingly high user expectations, driving higher satisfaction and adoption rates.

This evolution in UX testing is not just about improving the quality of digital products; it's about ensuring they are accessible, enjoyable, and beneficial for all users, regardless of their abilities or preferences.

2. Lean Test Packs through AI Optimisation

As testing suites grow more complex, efficient test management becomes increasingly crucial. Artificial Intelligence is making significant inroads in this area, and by 2030, we'll see widespread adoption of AI-driven test optimisation.

We’ve already seen automated test maintenance reduce the need for human intervention in automation packs. AI is already optimising tests for cross-browser and cross-device mobile testing to allow the testing of critical combinations with a single script.

In the coming years, testers will increasingly leverage AI in other areas, such as analysing historical data, to identify redundant, flaky, or ineffective tests. Additionally, AI will be used to prioritise test cases based on factors like code changes and business impact, ensuring critical areas are tested first.

This will allow teams to streamline their test suites, reducing execution time while maintaining or even improving test coverage. The result will be leaner, more efficient test packs that deliver faster feedback without compromising quality.

There is also a strong chance that AI will even be used to adjust test suites dynamically on the fly based on emerging patterns. AI will react to test failures, add additional tests and datasets to investigate why tests fail, and remove redundant tests if they confirm defective functionality.

3. AI-based Forecasting and Predictive Defect Prevention

The shift towards proactive defect prevention is already underway and will become the norm by 2030. AI-powered systems will predict where likely issues will occur in development programs, allowing teams to prepare for and target vulnerabilities early in the development cycle.

Machine learning models will analyse code complexity and historical defect patterns to prioritise testing efforts in high-risk areas. As these systems continuously learn from previous testing cycles, their prediction accuracy will improve over time, leading to higher-quality software from the outset.

Furthermore, AI will analyse code quality in real time, flagging potential issues before they become defects. It will predict user behaviour, identify potential usability issues, and generate realistic test data sets to improve defect detection.

These proactive approaches will lead to higher-quality software and faster development cycles.

4. Shift-Left and Shift-Right Testing

Shift-left and shift-right testing principles are gaining traction and will be deeply ingrained in software development practices by 2030. This evolution will lead to a more comprehensive and continuous testing approach throughout the software development lifecycle.

Shift-left testing will move testing activities earlier in the development process, allowing for increased collaboration between developers and testers.

AI will become crucial in analysing requirements documents and identifying ambiguities or inconsistencies. These AI systems will be able to understand natural language, compare requirements against industry best practices, and flag potential issues before development even begins.

This early intervention will reduce defects at the source, leading to more precise requirements and more accurate implementations.

Shift-right testing will focus on continuous monitoring and testing in production environments where advanced monitoring tools will perform ongoing tests, detecting issues that may not have been caught earlier.

As AI frees up bandwidth for more creative testing approaches, techniques like Chaos engineering will become standard practice. AI will design experiments to test system resilience and identify potential weaknesses.

This all-encompassing approach to testing will result in higher-quality software and improved user satisfaction.

What Won't Change

While we anticipate significant evolution in software testing, some fundamental aspects will remain constant:

• The need for human judgement in interpreting test results and making critical decisions
• The importance of clear communication between testers, developers, and stakeholders
• The value of domain expertise in understanding business requirements and user needs
• The necessity of maintaining a balance between automated and manual testing approaches
• The critical role of testing in ensuring software quality and reliability

You’ve Read My Thoughts… What Are Yours?

The journey to 2030 promises exciting developments in software testing, and those who stay ahead of the curve will reap the rewards of improved efficiency, quality, and user satisfaction.

Each of these four trends offers distinct competitive advantages:

• Organisations that embrace enhanced UX testing will deliver superior digital products that exceed rising user expectations, directly improving customer acquisition, retention, and loyalty in increasingly crowded markets.
• Companies implementing AI-optimised test packs will achieve faster time-to-market with higher quality, outpacing competitors burdened by bloated, inefficient testing cycles.
• Those who adopt AI-based forecasting and predictive defect prevention will dramatically reduce costly production issues, allowing them to reallocate resources from firefighting to innovation while competitors continue tackling preventable bugs.
• Finally, businesses that fully integrate shift-left and shift-right testing practices will implement solutions that more accurately match business needs and deliver consistent and reliable user experiences.

Conversely, organisations that fail to evolve their testing practices sufficiently over the next five years will become progressively less competitive in a market where quality at speed becomes the standard expectation.

On an individual level, testers will need to adapt their skills and methodologies to leverage these evolving trends effectively as we move towards this future. Those who don't embrace this AI-augmented future may find their careers stalling as our profession transforms around them.

Do you agree with these predictions about both the opportunities and risks? What are your thoughts on how testing will evolve by 2030? Let us know in the comments below!

Software Testing AI: 4 Breakthroughs You Can’t Ignore in 2025

We’ve talked about 2030, but what about today?

The integration of artificial intelligence into QA processes has passed the tipping point, with recent advancements addressing longstanding bottlenecks in test maintenance, resource allocation, risk management, and other areas.

Find out which AI breakthroughs you can’t afford to ignore

Test Tool Checkpoint: Latest Software Versions

OpenText (previously Micro Focus) have a wide range of software tools to help test professionals simplify testing, test faster & deeper.? Testing the widest range of applications and technologies. We know keeping track of the latest versions can be hard, but we’re here to help.? Products that have changed recently are shown in brackets after the product name.?

If you are not on the latest release, we recommend you check out what is new, using the link below. It is always best to plan to upgrade at least once a year, ideally more regularly. Being on an unsupported release will cost you more for your support. For further information, have a look at: OpenText Customer? Read This to Avoid Overpaying for Support Costs

OpenText have renamed the test tools. The new names use industry-standard terms. The goal is to use commonly understood terms that clearly convey their differentiation and better illustrates how their solutions deliver value against our customers’ pain points. Where a product has the word “Core” in its title it typically refers to a SaaS product, although there are some SaaS without Core.

Below we’ve listed the current releases of the industry-leading OpenText (Formerly Micro Focus) test tools suite:

• ALM Octane/Value Edge 25.1 (New version)

- Your ideal agile and DevOps test management tool.

• ALM/Quality Center 25.1 (New version)

- The perfect test management tool for traditional (e.g. waterfall) development methods.

• ValueEdge 25.1 (New version)?

- The solution for Value Stream Management. Use modules together or as standalone products. The following modules have been updated since last month:

• UFT One 24.4?

- Automation tool supporting the broadest range of applications & technologies.

• UFT Developer 24.4?

- IDE-based test automation.

• UFT Digital Lab 25.1 (New version)?

- Previously UFT Mobile

- Your mobile testing toolkit includes access to labs and virtual devices.

• LoadRunner Cloud 25.1 (New version)

- Simple, scalable, and efficient cloud-based performance testing with excellent analysis and reporting.

• LoadRunner Cloud 25.1 (New version)

- On-premises performance testing with massive support.

• LoadRunner Enterprise 25.1 (New version)

- Global performance powerhouse for large companies.

• LoadRunner Developer 25.1 (New version)

- Free IDE-based performance testing.

• Service Virtualization 2023 R1

- Accelerate functional and performance testing with virtual services (mocking services) for testing your applications. Shift left and save money.

You can download and install the new software without further cost if you have a support contract or have bought term licences.? If you have a SaaS licence, the software will be upgraded for you.

If you are out of support, contact Calleo, and we will help you.

Lessons from Space X: When Testers Meet Rocket Science

Imagine a world where failure is a desirable and crucial step towards success—anathema to most testers.

At SpaceX, the boundaries of innovation are being pushed to new heights, partly due to a culture of accepting and embracing failure. Next month, join us in exploring how a culture of rapid prototyping and iterative testing has helped SpaceX revolutionise the aerospace industry.