Using AI in Test Automation to Avoid Pain and Up Project Quality

AI has made it a full circle. It was a miracle. Then it became a controversial trend. Finally, it transformed into the core of productivity. And now, AI expertise is among the top skills tech companies are looking for. Despite still being in development and not having established regulations, the field of artificial intelligence has already found tons of uses with the most popular one being automation.

Interestingly, over one-third of organizations still struggle with applying it. So, there’s no point for them to turn to AI-based automation testing. Yet, with the current IT landscape, if you’re not using it, you’re missing out. In that regard, this article has two goals: showing you why AI is worth a try and how to implement it in your project.

Understanding Automation Testing with AI

Despite the implications of the term “AI-driven automation testing”, artificial intelligence isn’t really involved in testing as such. It acts more as an assistant, helping engineers with repetitive or time-consuming tasks. You don’t just let it loose onto your project and allow it to do “its thing.” Teams need to be actively involved in its operations:

• Directing AI by setting appropriate goals.
• Feeding it precise, high-quality data.
• Offering in-depth reviews and corrections as it learns.
• Checking AI’s outputs and modifying its behavior.
• Monitoring its long-term performance, etc.

To put it simply, AI in test automation is like a ten-year-old kid. It doesn’t know much yet. But it can help with simple stuff and it learns fast. And what/how it learns is entirely up to you. And a realistic definition for AI’s applications in automated software testing services is the following:

The use of AI in testing automation means applying specialized tools to streamline QA processes and allow the team to focus on higher-value tasks.

• AI can create scripts based on system logs, user flows, and requirements.
• It can adapt test cases when it detects changes.
• You can also better prioritize testing scenarios and bug fixes with AI’s analysis, etc.

These AI in test automation examples are only scratching the surface. But they perfectly show the key difference its use brings to your development – a lot of manual tasks are taken off of your crew’s hands.

Challenges of Using AI for Automation Testing

As of 2024, over 30% of organizations are using AI in software test automation. Given the current boom of artificial intelligence, this number is rather small. And there are good reasons for it.

Implementation Difficulties

Integrating AI into your company is a huge undertaking. You need a lot of refined data, experts with skills in AI and ML in test automation, continuous maintenance, and, of course, money to sustain it all.

Lack of Appropriate Strategies

Well over half of organizations just don’t know how they can use AI in their projects. This points to two issues: AI isn’t that mature yet, and many prefer to stick to the familiar. It’s tough to shift from legacy procedures to tech that, while developed, isn’t fully defined or regulated.

Tool Complexities

Even if we consider ready-made AI automation testing tools, the fact they exist doesn’t simplify the situation. You still need to know how to apply them within your SDLC and appoint specialists to work with them. Plus, choosing the right tool among the many available options is tricky.

Ethical & Security Concerns

The very first official control-guide on AI systems was introduced just this year. Now, we’re not talking just about how the training data is obtained and used. You also need to consider how the new and upcoming rules of AI applications will change over time.

In other words, companies that are relying on artificial intelligence now will definitely have to tweak their processes once its utilization gets systematized. And those who plan on using it will have to adjust to new rules.

Skill Deficiency

Lack of skills in AI is the second most cited challenge to adopting it in automation testing services. Companies’ needs for specific expertise shifted rather quickly. So, now there’s a gap between sought and available specialists. Plus, while many opt for up- and re-skilling, there’s often not enough team members due to the previous years’ layoffs.

These are all pre-AI concerns. Yet, even those who use it aren’t free from certain troubles. AI automation testing also involves specific risks that you should learn to navigate:

1. Artificial intelligence is dependent on its training data. And if it’s subpar, AI tools can produce inaccurate results, most often, false positives and negatives.
2. AI models operate as “black boxes”. So, it’s difficult to say why they came to this or that decision, making algorithm adjustments challenging.
3. Network variability, server load, or slight differences in the environment can lead to inconsistent results in AI’s work.
4. You might need additional QA resources to integrate AI tools into your existing workflows and processes.
5. Over-reliance on AI in automation testing is the biggest potential issue. Trusting artificial intelligence too much can lead to incomplete coverage, overlooking the significance of human expertise, and overall botched testing.

Summing this all up, we’d say that the big bad troubles of using AI are money, skills, and data. You need infrastructure and a budget to support it. Expertise to manage it. And information to keep making it better.

It’s quite a lot. We know. But there’s a catch (the good kind) – you don’t have to invest much to enjoy AI’s perks.

Benefits of Running Automation Testing Using AI

You can pick one AI-powered tool to gain significant advantages in your QA process. In other words, you don’t have to overhaul your entire project or pay for a ridiculously advanced system. You can start by adding bits and pieces to improve your SDLC.

• AI tools that auto-generate and execute tests quickly speed up testing and CI/CD cycles.
• Those that leverage AI-powered visual testing offer consistent UI checks across devices and environments, reducing human error.
• Some software uses ML to identify new test scenarios and automatically adapt to changes, ensuring broader test coverage and edge case identification.
• You can use certain tools to get advanced test analytics, obtaining actionable insights into test performance and trends to optimize the QA process.
• And rely on others to adapt and execute tests even on complex, dynamic web apps where traditional scripts might fail.
• Plus, AI tools are highly flexible. You can scale them how you see fit and train them to learn new things. This means your testing will be more versatile and cost-effective in the long run.

In fact, it’s often better to start small with AI. This “don’t put all your eggs in one basket” strategy gives you time and experience to refine its applications for genuinely valuable outcomes.

Just remember that, in that case, expertise becomes your priority. You ought to make sure your team understands and knows how to use your tool of choice for desired results. You could either hire the required specialists, train your in-house crew, or use QA outsource to get quick access to diverse skills.

How to Use AI in Automation Testing

Now, let’s talk action. What are your possibilities and how to use AI in test automation? Briefly, there are countless applications of this tech in software development. But there are specific areas that obtain the most significant advantages when combined with AI’s capabilities. Particularly, you should use AI to:

• Analyze app behavior, past testing data, and user interactions to automatically generate relevant test cases.
• Investigate historical defect patterns, code changes, and usage data to predict high-risk areas. This enables teams to prioritize testing where failures are most likely.
• Write test scripts in various languages based on user input or app behavior. It also creates clear, detailed test reports, summarizing outcomes and insights in natural language.
• Simulate complex scenarios, optimize test coverage, and even suggest improvements based on performance analysis.
• Dynamically prioritize and schedule test execution based on test impact, risk, or real-time conditions.
• Examine logs, stack traces, and error reports to identify root causes faster and suggest fixes or improvements.

These are universal, simpler, so to speak, uses of AI testing automation. You should also consider how it can be applied in narrow scenarios if you need artificial intelligence’s help with specific cases.

AI in Automated Functional Testing

Generative AI in test automation analyzes app workflows, user interactions, and historical data to generate test cases tailored to critical functionalities. It also uses historical test results and change impact analysis to rank test cases by their likelihood of detecting defects.

Perks:

• Faster test case creation.
• Better coverage.
• Reduced manual effort.
• Time savings.
• Improved defect detection rates.
• Optimized resource usage.

AI in Automated Regression Testing

AI tracks changes in the application codebase or UI and evaluates their impact on existing functionality. And gen AI in automation testing adjusts scripts in response to changes without manual intervention.

Perks:

• Ensured focus on impacted areas.
• Reduced redundant testing.
• Decreased maintenance effort.
• Enhanced test suite reliability.

AI in Automated Load Testing

AI predicts how an app will perform under future loads by analyzing historical performance and usage patterns. Plus, it simulates user traffic by replicating real-world interaction patterns.

Perks:

• Proactive identification of bottlenecks.
• Accurate capacity planning.
• Realistic performance assessment.
• Better alignment with actual user experiences.

AI in Automated Security Testing

AI scans codebases, applications, and networks to identify vulnerabilities using pattern recognition and anomaly detection. It can also continuously monitor for compliance. And generative AI for automation testing regarding security can provide reports on potential risks.

Perks:

• Faster detection.
• Coverage of subtle or complex attack vectors.
• Enhanced efficiency.
• Reduced manual oversight.
• Better compliance management.

AI in Automated Accessibility Testing

AI assesses website or app elements (e.g., alt text, keyboard navigation) to detect accessibility flaws. You can also analyze interface components and user flows to evaluate compliance with standards like WCAG.

Perks:

• Faster, more thorough testing.
• Improved inclusivity.
• Refined adherence to regulations.
• Reduced legal risks.

AI in Automated UI Testing

AI identifies UI elements (e.g., buttons, fields) using image recognition, even when element locators change. It executes tests across diverse devices, OS versions, and screen resolutions simultaneously.

Perks:

• Resilient test automation.
• Reduced failures from UI updates.
• Faster testing.
• Broader device coverage.
• Consistent quality.

AI in API Automation Testing

Generative AI in automation testing can be used to produce diverse, realistic test data for various scenarios. Artificial intelligence can also validate API responses, detect anomalies, and monitor API performance in real time.

Perks:

• Simplified test setup.
• Comprehensive validation.
• Improved reliability.
• Early detection of API issues.

Where to Apply AI First

When choosing where to apply AI in your SDLC, you ought to consider two core aspects:

1. What you want to achieve with it.
2. The maturity level of AI as a technology.

For example, artificial intelligence can be used in areas dominated by human expertise, such as exploratory and localization testing. But you should remember that AI’s applications in certain niches are very limited. It could translate texts for different locales or suggest exploration vectors. Yet, there’s no guarantee that it’ll do it all correctly.

So, always keep this in your mind – AI can’t do everything, and human creativity is superior. To better allocate your resources and get the results you want, place AI where it’s actually useful at the moment.

Integrating AI in Testing Automation

While on the subject of using AI mindfully, let’s discuss how to implement it. Note that we’ll offer a more generalized process. You’ll definitely have to tweak or expand this guide based on your project and team skills. So, consider the below a blueprint, not a definitive instruction.

Assess Your Processes & Needs

Understand your current testing workflows, pain points, and inefficiencies. Analyze which parts of your testing process are repetitive, time-consuming, or error-prone. After that, you should evaluate the need for AI-driven solutions, such as predictive analytics, self-healing scripts, or test data generation.

Identify the Areas for AI Integration

Pinpoint the specific testing areas where automation testing with AI can provide the most value. Don’t go broad here. You should apply targeted solutions to solve specific problems. For instance, when test case writing takes long, use AI to create them from user stories, requirements, or code.

Choose the Right Tools

Select AI-powered tools and frameworks that align with your project’s goals and technology stack. You need to make sure the software can do what you want it to. You should also consider how well it would fit within your existing practices so as not to change it all just for the sake of AI.

Also, keep in mind that your team should be able to use the AI tools from the get-go. Alternatively, you ought to allocate sufficient resources and time for them to learn. And do take note of the support the tool provider offers. If you have any questions or troubles, you should be able to solve them quickly, not wait for your “savior”.

Build Proper Expertise

Working with AI and automation testing requires diverse and robust skills. So, equip your team with the specialists needed to manage AI-driven testing. Make sure they can:

• Work with AI-powered tools and interpret results.
• Monitor AI decisions (e.g., identifying false positives in anomaly detection).
• Fine-tune AI models when required.

You could also involve AI-savvy developers or data scientists if customization is needed.

Start Small & Scale Gradually

Begin with a focused, low-risk implementation before rolling AI out broadly. Implement AI for a single testing phase or project, such as automating regression testing for a specific module. Gradually expand to other areas, like API or load testing, based on success and feedback.

Monitor & Optimize

Continuously track AI’s performance and refine its integration. Use relevant metrics to evaluate its effectiveness:

• Reduction in test execution time.
• Improvement in defect detection rates.
• Maintenance effort savings (e.g., self-healing accuracy).

Regularly update AI models and tools to keep pace with evolving app code and testing needs.

Of course, the above process will be very different if you’re not focusing so much on tools. They have a predefined work mode and are comparatively simple. But if you’re working with your own AI systems, that’s a whole another story. A much more convoluted one. If that’s something that interests you, feel free to check our guide on testing AI-powered apps.

For now, we move on.

Best Practices for Using AI in Testing Automation

We can’t offer you a precise roadmap to implementing AI. It’s an insanely project-specific procedure. But we can provide you with expert insights that make it easier. Our QA experts have ample experience with working on AI-involving projects, be it testing AI-powered products or using AI tools to enhance development.

So, here are some QA Madness’ wisdom nuggets for refined automation testing with AI.

• Begin with low-risk, focused tasks to test AI’s effectiveness before scaling.
• Ensure your data is accurate, relevant, and representative of real-world conditions.
• Collaborate with domain experts to ensure AI properly integrates into your system.
• Choose AI tools that provide transparency, helping you understand their logic and outputs.
• Use AI to complement, not replace, traditional testing to maximize efficiency and accuracy.
• Regularly evaluate AI’s effectiveness, ensuring it provides value and accuracy over time.
• Use AI to automatically adjust test scripts when the code or environment changes.
• Ensure your team is well-trained to operate AI tools, maximizing their potential.
• Secure data privacy and avoid bias in AI models by using diverse and ethical data sets.
• Collaborate across teams (dev, QA, etc.) to ensure everyone is aligned with AI goals.

Speaking of expertise, there’s something we really need to mention. The pressing issue with AI development is the value of human skills. If you can use artificial intelligence to take care of so many tasks, does it mean you can cut your team? After all, AI is progressing fast. So, eventually, you’d need fewer and fewer specialists on your crew…

Skills Needed for AI Automation Testing

AI will not replace humans. Ever. AI can already write text, create music, and produce images. Writers, artists, and painters are still thriving. It can also generate code and test cases. Yet, tech specialists are in insane demand. So, there will never be this takeover of artificial intelligence. What will happen instead is the shift in who does what.

• AI will focus on time-consuming tasks.
• And people will handle complex, creative, and more challenging stuff.

We’re already seeing this transition. Everybody realizes that while AI is useful, it’s far from self-sustaining. And to get its advantages, you need to put in a lot of work. That’s why companies that use AI in automation testing not only need automation specialists but also those who can sustain AI solutions.

The complex digital mind could create a you-tailored automation strategy. But would you trust it enough to implement it right away, without checks or analysis?

If we’re talking simpler stuff, AI can also create test cases based on available data. Yet, you don’t just take the provided scenarios at face value. You’d need to evaluate their relevance and refine them if needed.

Plus, to make sure your AI tool actually understands what it’s doing, you should carry out a tech deep dive into its algorithm. Otherwise, you won’t know if its success or failure were one-off occasions or fully working processes.

The “trust but verify” approach is the only currently available mode of cooperation with AI solutions. They have plenty of unique values. But they’re only available when you control and direct AI into doing what you need it to do.

And that’s why, when you decide to implement it in your project, you ought to be prepared for hiring specialists who will be AI’s shepherds. So, for AI automation testing, you should have the following expertise minimum.

Data Scientists & Machine Learning Engineers

They ensure the AI components driving automation are accurate, adaptable, and continuously improved. Their expertise provides the intelligence needed for advanced test optimization and error prediction.

Key role:

• Develop and maintain AI models used for automation testing, such as test case generation, anomaly detection, and predictive analytics.
• Work on data preprocessing, model training, validation, and optimization to ensure the AI’s accuracy and reliability in testing scenarios.

Required skills:

• Proficiency in programming languages like Python, R, or Julia.
• Familiarity with AI/ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Expertise in data analysis, feature engineering, and statistical modeling.

Test Automation Engineers

They bridge the gap between AI and practical testing. Test automation engineers ensure AI-driven solutions are applied effectively and generate actionable results.

Key role:

• Implement and manage AI-driven test automation frameworks.
• Integrate AI models into testing workflows and ensure their compatibility with existing test tools and environments.
• Write, execute, and debug test scripts for functional and non-functional testing.

Required skills:

• Knowledge of automation tools like Selenium, Cypress, or TestComplete.
• Programming proficiency (e.g., Java, JavaScript, or Python).
• Understanding of AI integration techniques and APIs.

QA Managers & Leads

They provide strategic direction and ensure the AI integration aligns with organizational objectives. They’re also in charge of optimizing resources and fostering collaboration among teams for successful AI adoption.

Key role:

• Oversee the integration of AI-driven testing solutions into the overall testing strategy.
• Define objectives, allocate resources, and ensure alignment between AI initiatives and project goals.
• Evaluate the ROI of AI implementation in testing and monitor its progress.

Required skills:

• Strong project management capabilities.
• Expertise in test strategy development and quality assurance best practices.
• Familiarity with AI concepts and their implications for testing.

Now, a brief question. How can AI replace people if for it to work you need at least three separate specialists? The “humans vs artificial intelligence” debate aside, AI-based automation testing needs pristine skills of highly knowledgeable experts. Yet, given the Tech Winter, massive layoffs, and the overall mess in the world, finding such talent is a task to behold.

Luckily, we can help you with that.

How QA Madness Can Help

You can get immediate access to specialists with over a decade of experience in diverse niches and projects. From healthcare to gaming, from manual testing to cybersecurity, our team can cover pretty much every one of your needs.

• We can work on a multitude of testing types, like functional, compatibility, end-to-end, accessibility, etc. So, if you need to up and sustain your development quality, our engineers will be your dedicated partners.
• Our talent acquisition team can help you locate your ideal talent and build a retaining and onboarding strategy. They can even take on employee management if you wish. Finding expectation-exceeding candidates will be easy and fast with our tech skill-oriented specialists.
• We can help you refine your development or set it up from scratch. We learn your existing processes or requirements to polish or design workflows that bring in the desired results. And if you have specific concerns, like collaboration, resource optimization, or quality standards’ enhancement – we’ll ensure you achieve what you want without procedure or team disturbances.

Wherever you are in your AI automation testing journey, you’ll be able to find exactly what you need with QA Madness. Implementing AI into your project, selecting specialists, or executing your vision – we’ll be with you every step of the way. Our talents know how to transform your bold ideas into profitable reality. And here’s how they ensure it:

• You are our sole priority. And your dream of a high-quality product becomes our dream, too.
• No matter the duration of our partnership, we make sure to become a valuable part of it. We learn everything about how you do things, get to know your team, and study your goals.
• Based on our research and your direction, we build a you-oriented strategy toward achieving what you want. There’s no out-of-the-blue solutions or pushing for change. Everything we do is tailored to your project and your targets.
• You chose what specialists work on your project and how. The selection process, onboarding, and management can be handled by you or taken care of by us. Whatever option suits you, we can support it.
• We make sure that the talent you work with reflects your needs. Skills, work ethics, and culture – our team guarantees that QA Madness’ experts become your crew’s integral part. Their work won’t feel like a separate process.
• Our experts operate with diverse and tried methods and technologies. Whether you need us to do everything your way or help you build or adjust your processes, you can be sure things will be done to the highest standard.

We’re a company of quality-obsessed people. And that’s what sets us apart. We make quality a priority in everything we do. From the tiniest of details to the most challenging tasks, for us, the mission is to surprise you with how much better things can become. And for automation testing with AI, that perspective is what turns it from a solution to an asset.

To Sum Up

AI automation testing will become more and more widespread as the tech evolves. And sooner or later, companies will have no choice but to apply it if they want to keep up with the competition. The important thing is that they do it right. We don’t need even more half-baked initiatives, forever-in-development innovations, or sticking to the old because “it worked fine so far.”

We need to finally embrace the amazing progress unfolding before our eyes. We need to prepare for the big changes coming. And the first step toward that is finding experts who can support your project and lead it to new horizons. Everything great always begins with great people. So, start looking for them. Or work with an organization that already knows where to find them.