Future trends in software testing: The role of AI and ML

Cloud computing, microservices architecture, and the proliferation of Internet of Things (IoT) devices drive the software development field even more than other advancements in the previous ten years.

Artificial Intelligence (AI) and Machine Learning (ML) — definitely transformative forces now changing the parity. Machine Learning in software testing enables intelligent automation, predictive analytics, and adaptive learning, which, in turn, enhances the effectiveness of quality assurance efforts.

To be more specific, integrating AI in software testing accelerates release cycles and improves the ability to detect complex defects that traditional methods might overlook.

Want to maintain a competitive edge or grab a new market share? Then you should catch up with these advancements. For instance, AT&T lost phone records of “almost all” its customers. Catastrophic issue? Unfortunately, yes: the company even tried to pay the hacker for deleting the stolen data.

All of this could have been avoided if software testing norms had been followed (and if they had even been ahead of the curve, it would have been a miracle).

This article explores the most significant future trends in software testing driven by AI and ML so that your QA professionals can prepare for these changes.

The evolution of software testing with AI and ML

Manual testing → automated testing

Manufacturing, healthcare, and other industries have evolved drastically over the past few decades. So have technologies and software testing in particular.

Early days: Testing was entirely manual, relying on human effort. Very labor-intensive, time-consuming, and prone to human error, innit? Applications grew in size and complexity, and the limitations of manual testing have become obvious.

1990s – early 2000s: Emerged first testing frameworks like JUnit and Selenium and tools like QTP (QuickTest Professional). From that time, testers could automate some tasks and execute bigger volumes of test cases. While improved consistency and reduced execution time, testing still required substantial human effort to create and maintain test scripts. There were frequent changes in apps, which forced testers to update test scripts.

2010s – Nowadays: Agile and DevOps methodologies contributed to faster and more reliable testing processes. Continuous integration and continuous deployment (CI/CD) pipelines demanded that testing keep pace with rapid development cycles. This necessity set the stage for implementing Artificial Intelligence in test automation and creating Machine Learning software testing.

The role of AI and ML in modern testing

Intelligent automation and data-driven decision-making sound much better and easier than burning the midnight oil testing applications round-the-clock. How do we use software testing AI:

• Autonomous test case generation: AI algorithms analyze application code, user interfaces, and how users interact with the app, and generate relevant test cases. This reduces the research and investigation time and ensures more comprehensive coverage.

• Increased test coverage: ML models learn with each new test cycle and user interactions and identify areas that require additional testing. This nonstop learning increases coverage, including edge cases.

• Defect detection: AI-powered tools detect patterns and anomalies in large datasets, identifying potential defects more effectively than manual analysis. They can process log files, performance metrics, and user feedback to uncover issues before they impact users.

• Natural language processing (NLP): The technology interprets requirements and user stories written in plain language and converts them into executable test cases. This bridges the gap between non-technical stakeholders and the testing team.

• Visual testing: AI-driven visual recognition technologies help validate user interfaces across different devices and screen resolutions.

Test automation Machine Learning helps companies handle complex applications more effectively and release updates with greater confidence.

The shift to predictive and autonomous testing

These moves have paved the way for predictive and autonomous testing, moving beyond traditional automation.

> Predictive analytics: AI models analyze historical data (e.g., past defects, code changes, testing outcomes) and predict where new defects are likely to occur. This way, testers can focus on high-risk areas, improving overall effectiveness.

> Autonomous testing: AI-driven tools autonomously create, execute, and adapt test cases without human intervention. With nonstop learning, they adjust their strategies and improve over time.

> Self-healing test scripts: When applications change, traditional test scripts may fail due to hard-coded values or outdated references. Autonomous testing tools can detect these changes and automatically update the test scripts. Consequently, maintenance efforts decrease, and disruptions become rarer.

Key future trends in software testing with AI and ML

Hyper-automation in testing

Hyper-automation in software testing places the automation matter at the top of a corner and implies automating everything that can be automated. How? Through co-working between UI/API test automation, RPA, digital business process automation (DPA), AI/ML, and low code automation. This approach automates not only test execution but also test design, analysis, and maintenance:

• Intelligent test generation: The AI model analyzes the code base, user interfaces, and the ways users utilize the app and creates relevant test cases.

> Process:

1. AI algorithms scan the application’s source code, user interfaces, and user behavior analytics.

2. They identify functionalities, dependencies, and potential risk areas.

3. Automatically generates test cases covering these areas and related ones.

> Practical impact: No need for manual test script writing. Expands coverage, including edge cases. Prioritizes test cases based on risk assessment, focusing on critical paths first.

> Use case: In an e-commerce application, AI identifies the checkout process as high-risk due to recent code changes and generates targeted test cases to validate payment integrations.

• Dynamic test planning: Test automation machine learning allows us to assess risk and prioritize test cases based on code changes and historical defect data. A sort of prioritization as well since critical areas will be tested first.

• Self-healing test scripts: When the application changes, the system automatically adjusts test scripts to accommodate updates.

• Automated defect detection and analysis: Along with the usual results, there will be anomalies and atypical patterns in test results. And these “oddities” also speed up the debugging process.

> Process:

1. Machine learning models analyze test results, log files, and system performance metrics.

2. The AI identifies patterns and correlations that indicate underlying issues.

> Practical impact: Accelerates root cause analysis by pinpointing the source of defects. Improves defect triaging by categorizing issues based on severity and impact. Enhances collaboration between QA and development teams with detailed insights.

> Use case: Memory usage exceeds a threshold, and AI detects the crash.

• Continuous feedback loops: Integrated with CI/CD pipelines, AI-powered testing tools provide real-time feedback to developers. This way, it’s much easier to collaborate.

Software will become even more complex and sophisticated. Consequently, there is a need for tech raiders for QA teams to handle this increasing complexity. “Here, we must run as fast as we can to just stay in place.”

Autonomous testing systems

Autonomous testing systems are a significant trend that can hardly be called a development from the future. Because leaders in almost any niche actively use new technology. Over 20% of US companies have already implemented AI tools for coding and testing, and 20% more expect to implement them in the next six months.

• End-to-end automation: Managing the entire testing lifecycle, from test case creation, through execution to analysis.

• Continuous learning: Adaptation based on previous data (both learning and “experienced” with the latest testing outcomes) and ongoing feedback; improving test accuracy and coverage.

• Optimization algorithms: Optimization of testing strategies by identifying redundant tests and focusing on high-risk areas.

💡OwlityAI with its advanced autonomous testing capabilities exemplifies this trend. Its killer feature is the ability to rapidly adapt to changes in the application using AI-driven self-healing mechanisms. Make your testing keep pace with development without your extensive attention.

Predictive and prescriptive analytics

Predictive and prescriptive analytics are the process whereby an AI tool anticipates potential issues and specifies optimal testing strategies, reducing the need for reactive testing.

• Defect prediction: Based on code complexity, change history, and past defects, AI predicts future failures and areas that may contain bugs.

• Optimal test planning: Prescriptive analytics suggest the most effective testing strategies, including specific tests and the order.

• Resource allocation: AI allocates testing resources efficiently, focusing efforts where they are needed most.

Example:

IBM developed their Inspection Suite to significantly reduce defects by leveraging predictive analytics. Historical defect data and code changes allow them to predict high-risk components with up to 95% accuracy. This approach can save USD 20 million annually in maintenance costs.

Continuous testing in DevOps

Continuous testing within DevOps pipelines couldn’t be imagined without ML now. Modern testers are used to rapid and reliable feedback loops that support faster releases. Let’s break down this aspect.

• Automated pipeline integration: AI-powered testing tools integrate seamlessly with CI/CD systems (tests run automatically upon code changes).

• Real-time analysis: ML algorithms provide instant feedback on test results, highlighting defects and performance issues immediately.

• Adaptive testing: Testing strategies change on the fly based on changes in the codebase and current results.

This integration allows teams to maintain high-quality standards even as they accelerate release cycles.

AI-driven test data management

Artificial intelligence in software testing mostly relies on further development of test data management. As we approach the limit of quality (and real) test data, the more often we hear about next-gen AI models that will produce synthetic data to train the next models. Like expected Orion by OpenAI.

Synthetic data generation: ML algorithms create realistic test data that mimic production data while ensuring privacy compliance. Although this method has its flaws, it seems to be the most realistic way of testing development.

Data optimization: If you have duplicative or unnecessary data, AI will identify it and streamline test datasets.

Data provisioning: Automated systems provide the right data for the right tests at the right time.

Evolution of QA roles and skills

The rise of Artificial Intelligence and Machine Learning in software testing is transforming QA roles, but at the same time, requires new skills. What does it mean in practice?

> The need for data analysis skills: QA professionals will need to interpret data analytics results to make informed decisions.

> Ability to train AI models: Understanding how to train and fine-tune AI models is crucial for effectively leveraging AI-driven tools.

> Tools proficiency and learning on the feet: Being fast on the uptake and the ability to teach colleagues new things. Also, a deeper understanding of AI-powered testing tools, apart from their basic capabilities and limitations.

> Strategic focus: QA won’t be just executors, but rather a supervisor over the automated systems. They will set testing strategies and focus on areas where human insight adds the most value.

QA teams will become more technical, acting as orchestrators of intelligent testing systems rather than executors of manual tests.

Preparing for the future: Strategies for QA teams

Wide use of AI and ML creates global change in the software testing field, and preparing for this future requires a fast yet streamlined approach. Deloitte states that AI coding tools may save about USD 12B annually for the US economy. However, they could also increase technical debt. That’s why a deep dive and experimenting with next-gen AI/ML tools is so significant.

Top three AI technologies you should keep an eye on

1/ Predictive analytics for defect prediction

Previous data can tip you off where defects are most likely to occur.

Benefits:

• Focuses testing efforts on high-risk areas.

• Reduces time spent on low-impact tests.

• Improves overall product quality.

Practical impact: Teams can catch defects early, reducing costly fixes later in the development cycle.

2/ Natural Language Processing

NLP technologies convert requirements and user stories written in plain language into executable test cases.

Benefits:

• Bridges the gap between business and technical teams.

• Ensures comprehensive test coverage of requirements.

• Accelerates test design processes.

Practical impact: Streamlines communication and reduces misunderstandings. This way, the testing cycle becomes more effective and efficient.

3/ Robotic Process Automation (RPA)

Uses intelligent automation to perform repetitive tasks that were traditionally done by humans: extracting data, filling in forms, moving files, etc. RPA combines APIs and user interface (UI) interactions to integrate and perform repetitive tasks across various enterprise and productivity applications.

Benefits:

• Reduces labor costs and allocates human resources to more strategic activities.

• Easily scales up to handle increased workloads

• Ensures that tasks are performed consistently and according to predefined rules.

Practical impact: In the financial sector, RPA can automate the processing of invoices, financial reports, and income statements, significantly reducing the time and effort required for these tasks. In healthcare, it can streamline the retrieval of medical invoices, making the process 80 times faster without human involvement.

Upskilling and reskilling

Want to scale easier than now and stay at the top of the market? Then, your QA pros should embrace life-long learning and develop new skills.

Consider these programs and certifications

1/ ISTQB Certified Tester AI Testing (CT-AI)

Provider: International Software Testing Qualifications Board (ISTQB).

Focus: Testing AI-based systems and utilizing AI in testing processes.

Value: Recognized globally, it enhances credibility and demonstrates expertise in AI testing.

2/ Certified Machine Learning Tester (CMLT)

Provider: American Software Testing Qualifications Board (ASTQB).

Focus: Machine learning concepts applicable to software testing.

Value: Equips testers with the knowledge to implement ML techniques in testing.

3/ Google's TensorFlow Certification:

Provider: Google

Focus: Deep learning applications in testing.

Value: Deep knowledge of a popular open-source machine learning framework.

Integrating AI and ML into existing workflows

Drawing from experience, here are practical steps:

1. Assess current testing processes

• Determine areas with high and time-consuming manual effort; where testing is slow and inefficient.

• Assess the team's familiarity with AI/ML technology and the organization's infrastructure capabilities.

2. Start a pilot

• Choose a non-critical project with clear objectives to minimize risk.

• Clarify the goal and what success in your opinion looks like (e.g., reduced testing time, improved defect detection).

• Implement AI tools gradually; first, with specific testing phases like regression.

3. Collaborate across teams

• Engage developers, product owners, and IT to ensure alignment. Do it as early as possible.

• Document the pilot project's outcomes and share insights with the broader team.

4. Leverage existing tools

• Choose AI tools that work with your existing CI/CD pipelines and test management systems.

• This approach reduces the learning curve and maintains productivity.

5. Monitor and adjust

• Regularly review the AI tool's performance and adjust if needed.

• Expand AI integration to other projects and testing phases if your pilot has succeeded.

Creating a culture of innovation

Encourage experimentation

• Allocate time for innovation: Set aside dedicated time (e.g., 10% of work hours) for team members to explore new tools and methodologies or brainstorm on improvements.

• Support even smaller pilot projects: Provide resources for small-scale projects that allow teams to test new ideas without fear of failure.

Change mindset

• Not a failure, but a lesson: Throughout the company’s history, you will have different QA specialists, with different skills and mastery level. Try to establish an internal rule of support and consider unsuccessful tests as an opportunity to find a new way.

• Foster ambitions: If the established workflow doesn’t work, encourage employees to figure out why or to create a brand new one. It may sound weird, but breakthrough technologies were sparked by small crazy ideas.

Promote continuous learning

• Provide access to training: Offer online learning platforms and cover costs for relevant courses.

• Host knowledge-sharing sessions: Encourage team members to present what they've learned to the group, fostering a learning community.

Recognize and reward innovation

• Acknowledge contributions: Recognize team members who introduce valuable innovations out loud. This makes not only your staff motivated, but also allows you to grow as a manager/owner.

• Incentivize creativity: Implement programs that reward innovative solutions that improve outcomes.

The long-term impact of AI and ML on software testing

Artificial Intelligence and Machine Learning are gaining ground as a strategic asset within Quality Assurance practices. It’s supposed that higher-quality software will be cheaper, and the process of its development will be smoother.

What does it mean in practice?

• Decision-making with confidence: AI in software testing analyzes vast amounts of data and helps QA teams prioritize testing efforts based on risk assessment and predictive modeling. With adequate resource allocation and a greater focus on impactful areas, business owners can make bolder decisions.

• Innovation for granted: While AI handles routine and repetitive tasks, QA professionals are making up new or more creative testing approaches (e.g., exploratory testing or usability assessments). This shift leads to the development of more user-centric software.

• Gaining competitive advantage: In 2023, only 5.9% of AI-based projects gave back. However, a strategic approach to AI in QA definitely gains a competitive edge: time-to-market, software reliability, and time savings. In fintech, biotech, and other fields with high demand for innovation, this advantage can be the difference between leading the market or falling behind.

Ethical and governance considerations

Like every cloud has a silver lining, similarly every advancement has problems. We all see that AI brings up ethical and privacy issues.

• Hallucinations and bias: AI systems are like a mirror: when you train them on the wrong data, they will reflect. Who needs unfair or discriminatory outcomes? In testing, biased AI models might overlook defects affecting underrepresented user groups.

• Transparency and accountability: Even the creators of particular AI models can’t explain how their technology works in determined detail. The complexity of AI algorithms creates a renowned “black box” problem. In testing, a lack of transparency can hinder trust in AI-driven results.

• Governance challenge: The possibility to audit the decision-making processes of a particular model is more crucial than ever. This is one of the reasons why OpenAI rolled out a so-called human-reasoning model GPT-1o. Marketing move? Maybe. But anyway, companies need practices that promote transparency.

• Data privacy and security: Open a tech news feed for 2023 and early 2024. You will be surprised how many times OpenAI and other tech giants were blamed for breaking intellectual property. It’s challenging to notice sensitive information in an amazingly large amount of data. Protecting this data from unauthorized access and ensuring compliance with regulations (e.g., GDPR) is utterly important.

💡French regulators fined Google USD 57 million for not providing transparent and clear information about data consent policies. Considering the future AI pact, this will only tie up the compliance question.

How to address

• Ethical guidelines: Develop and stick to ethical guidelines for AI usage in testing.

• Audit and monitor: Continuously check AI systems for biases and errors.

• Involve stakeholders: Involve as many stakeholders as needed for the result you expect. Better prevent and mitigate ethical risks than turn to a firefighter.

The future of QA with AI

AI/ML-powered Quality Assurance will drive continuous innovation and quality improvements in software development.

• Integrated AI ecosystems: Next-gen models will be embedded throughout the software development lifecycle, from requirement analysis to deployment.

• Collaborative human-AI teams: QA professionals will work alongside AI systems, backed up by their speed and analytical capabilities and by their own human creativity and critical thinking to complex problems.

• Real-time testing and feedback: AI will enable instantaneous testing and feedback as code is written, catching defects immediately and reducing rework.

• Personalized user experience testing: ML algorithms will simulate diverse user behaviors and environments, ensuring software performs optimally for all user segments.

Bottom line

Artificial Intelligence in software testing gains ground. With hyper-automation, autonomous testing systems, predictive analytics, and other features, it will further transform how we approach QA.

And if you want to stay ahead of the curve, you should think about embracing software testing AI now and the day after. Future investments, upskilling team members, and nurturing a culture of innovation — the only way to conquer these trends and maintain a competitive edge.

OwlityAI is a next-gen testing tool that helps your company exceed the market and become a trendsetter. At least, in terms of software quality. OwlityAI represents the latest advancements in Artificial Intelligence and Machine Learning in test automation, offers advanced capabilities, and boosts software quality.