AI technology is fun to watch. It went from being a miracle to being despised, to being somewhat actually useful. And now, while some bank on artificial intelligence, and some grasp onto how things were, we prefer to take a different approach.
Instead of living in one’s memories or trying to ride the ever-changing tech wave, we prefer to adapt strategically. The crocodiles have found their perfect form. They’ve been the same for millions of years. But humans… we have a long way to go. And the secret to reaching your apex form is to evolve with your environment. Not against it. Not ahead of it.
So, let’s take a realistic look at AI automation tools for testing. Where they stand now. How they can be useful. And what you should watch out for.
AI Technologies in QA Services
There once was a big hope that AI automation testing tools would relieve QA specialists from their duties. But, surprise, people aren’t that easy to outshine. And very soon, we came to face the truth – AI is a tool. Not a replacement for the brilliant human mind.
Current State of AI Tools for Automation Testing
Now, around 37% of companies use AI/ML for software testing. And by 2025, G2000 firms plan to allocate 40% of their core IT funds to AI initiatives. So, we can clearly see that this tech’s usage is growing. It does so because it works. It offers you some enticing benefits.
- AI-based automation testing tools can handle repetitive tasks, improve accuracy in test creation, and even predict future testing trends.
- A key area is using AI to handle the tedious aspects of testing, freeing up QA engineers for more strategic work.
- These include “test bots” that can automate tasks like app discovery and test case generation.
For a while, automation testing tools were “the thing” for most testing endeavors. But with the implementation of AI, they’ve transcended to a new level. And at the moment, AI tools for automation testing are most used for the following:
- Automated test generation – analyzing requirements, code structure, and past test data to automatically generate test cases.
- Test data creation – generating realistic and diverse test data sets that simulate real-world scenarios.
- Regression testing – automating regression testing to speed up development.
- Defect analysis and prioritization – assessing test results and code to predict areas where defects are likely to occur.
- Test result evaluation – investigating test data to identify patterns and trends that might indicate underlying issues.
A few less mature but especially promising areas for AI-based automation testing tools’ usage are:
- Visual testing – analyzing screenshots and user recordings to identify UI inconsistencies.
- Self-healing tools – creating self-healing test scripts that can automatically adapt to changes.
- Declarative testing – examining what needs to be tested and providing efficient test cases.
- Intelligent test case prioritization – structuring test cases based on factors like risk, impact, and historical data.
- Dynamic test environment configuration – organizing test environments based on specific test needs.
- AI-driven code generation – writing code for automated tests based on app functionality.
But if AI is such a great thing, why aren’t we seeing more of it?
Well, it’s all very simple. AI testing automation tools, like artificial intelligence itself, are far from perfect. This tech is still developing. And the fact that it isn’t that widespread yet is actually a good thing. It means companies are taking AI potential seriously and prefer to work their way up to it rather than using anything for whatever.
We’ll definitely see AI-based automation testing tools become more and more prevalent. Yet, it will only happen after AI stabilizes. After it grounds itself as a tech valuable in many ways.
Another reason for the rather shy reliance on artificial intelligence is the challenges that come with its use. As per World Quality Report, the biggest roadblocks to AI implementation range from security and cost concerns to sustainability and regulatory issues.
To sum this situation up: AI needs to mature, but so do we. We need to be prepared and figure out how to utilize it meaningfully.
Now, let’s talk a bit about the wonders AI can do for you. Interestingly, around 31% of companies see no value in adopting artificial intelligence tech. That’s a mistake. Sure, not every project needs AI. But not recognizing its benefits is just denying the truth. And for the IT sector, seeing things for what they are is already a strategic advantage.
All in Favor of AI for QA
So, here are the practical impacts AI can have on the SDLC.
Enhanced Efficiency
AI and automated software testing services can take care of repetitive tasks like regression testing and data creation. This accelerates the testing process and allows QA specialists to center on higher-level tasks. It allows them to advance the project instead of balancing it, so to speak.
Improved Accuracy
AI’s ability to analyze vast amounts of data surpasses human capabilities. It can identify hidden patterns and edge cases, leading to more comprehensive test coverage. In turn, you get a cleaner product and stronger confidence in the results.
Reduced Costs
Faster testing cycles achieved through automation translate to significant cost savings in the long run. Companies spend less time and resources on manual testing efforts. Add to that self-healing tests and your budgets will thank you.
Proactive Defect Prevention
AI can analyze historical data and code patterns to predict where defects might pop up. It looks for error-prone areas, fostering proactive testing and preventative measures.
Enhanced Test Data Quality
Automation testing tools with AI features can generate more realistic and diverse test data sets that mimic real-world user behavior. And with a better understanding of how the software will perform in actual use cases, you get precise, targeted tests.
Faster Time-to-Market
By streamlining the testing process, automation testing AI tools help deliver software products to market quickly. This allows you to capitalize on opportunities and stay ahead of the competition.
Better Scalability & Adaptability
AI tools can handle a larger volume of tests, making them ideal for complex and ever-evolving software projects. Additionally, AI models can adapt to changes in the application under test, ensuring continued effectiveness.
All Against AI for QA
Why a decent chunk of organizations are not that hyped about adopting AI? Well, there are two sides to every coin.
- First, AI isn’t all-powerful. It has its unique limitations.
- Second, artificial intelligence is complex, requiring extra effort to make it work.
Let’s take a closer look.
Limited Creativity
AI struggles with test cases that require human ingenuity and a deep understanding of consumer behavior. It can’t replicate the creativity and intuition people bring to designing scenarios that explore the full spectrum of user interactions.
Training Data Dependence
The effectiveness of AI tools heavily relies on the quality and quantity of data used to train them. Insufficient or biased data can lead to inaccurate results, unreliable predictions, and even perpetuate biases in the testing process.
Maintenance Challenges
Keeping AI models up-to-date and maintaining automated tests requires ongoing effort. As your software evolves, AI needs to be retrained, and the automated tests adjusted to keep up with the changes.
Initial Investment
Implementing and integrating AI automation testing tools can require a significant initial investment. You need to accommodate the technology, train your team, and potentially hire new experts.
Lack of Explainability & Trust
AI models can sometimes produce results without clear explanations for how they arrived at those conclusions. This lack of transparency can make it difficult to trust the results and identify potential biases in the AI’s decision-making.
Over-Reliance on Automation
There’s a risk of over-reliance on automation and neglect of areas that require human expertise. Testers should use AI as a powerful tool to augment their skills, not replace their critical thinking and judgment.
Ethical Considerations
Bias in AI models can lead to unfair testing practices. To avoid that, you ought to implement strict and logical measures that secure fairness and inclusivity. And that’s actually a lot of effort, as mistakes in ethics can prompt legal issues as well.
What to Expect from AI-Based Automation Testing Tools
We’ll see AI develop further. Its uses will amplify. And its benefits will multiply. That’s simply that. What we should pay more attention to is how this technology is likely to terraform the testing itself.
Automated Testing as a Staple
Since automation in its original form appeared, it’s been spreading like wildfire. Then, codeless automation tools showed up and have been gaining popularity. AI automation testing tools are likely to follow a similar path. As this tech matures and establishes itself as an asset, more companies will take advantage of it.
Why shouldn’t they? Every tech humans have created was designed to make someone’s job easier. AI is no different. And sleeping on it is just delaying the inevitable (or falling behind).
The Role of a QA Engineer To Transform Yet Again
With the active adoption of AI, QA specialists, and developers, too, will metamorphose. But it’s nothing new for them. That’s how they continued being irreplaceable for so long. They adapt incessantly. Something we all should learn from them.
To get from point A to point B, you need a vehicle. But that vehicle still needs a driver. And even if you use a self-driving car, well, someone still needs to train and maintain it. Same will happen to QA engineers and automation testing AI tools. Human experts will turn into AI’s captains.
They will oversee its use in projects. Analyze its data. Modify it, thus helping you improve your algorithms. QA professionals will become more intertwined with artificial intelligence, almost guiding it to new levels. Their duties will be split between:
- Leveraging AI.
- Advancing AI.
- Focusing on elaborate tasks that AI-based automation testing tools can’t handle.
AI/Human Collaboration as the Core of Progress
Unless we actually come up with a perfect (digital) replica of the human brain, AI won’t replace humans. At this point, such a scenario is purely speculative. And good for movies.
Realistically, it won’t be about AI vs humans, but AI + humans.
The best thing artificial intelligence does for any company is unloading human experts. Many don’t pay much attention to this aspect. So hear us out. With more time on their hands, people can learn and upgrade their skills. They can rise to an expertise level AI could never. They can have diverse real-life experiences, which AI will never.
And, at least in the near future, people will lead the way with the help of AI. Not lie down while AI tools for automation testing trample over them.
- AI takes care of repetitive and simpler tasks.
- QA engineers dedicate themselves to creativity and strategy.
In short, if you care about quality – bank on people, not artificial intelligence.
AI-Powered & AI-Augmented Tools
Don’t take our stance on the value of QA specialists the wrong way. Human effort and finesse are extraordinary. But AI-powered tools are incredible as well. So, let’s give credit where it’s due and overview some worthwhile AI automation testing tools.
Best AI Tools for Automation Testing
This list of AI tools for automation testing isn’t structured in any particular way. The “contestants”, however, were taken from Gartner’s rating and numerous independent reviews.
Tricentis NeoLoad
This tool focuses on performance testing, incorporating AI to:
- AI-driven scenario learning – learn user behavior patterns and automatically create realistic load testing scenarios.
- Predictive performance analysis – predict potential performance bottlenecks before they occur.
- Root cause analysis – assist in pinpointing the root cause of performance issues.
Tricentis Tosca
Another offering from Tricentis, Tosca caters to a broader testing scope, including functional, API, and mobile testing. Its AI features include:
- Risk-based testing – analyze historical data and code to identify high-risk areas.
- Test case optimization – optimize existing test cases by identifying redundant steps and suggesting improvements.
- Self-healing tests – automatically adjust test scripts as the application under test evolves.
BrowserStack
A popular platform for cross-browser testing, BrowserStack integrates AI to enhance its capabilities:
- Smart visual testing — automatically detect UI inconsistencies and regressions across different browsers and devices.
- Real-world user behavior simulation – simulate realistic user behavior patterns during testing.
- Self-healing tests – adapt automated tests to changes in web pages.
OpenText UFT One
Formerly known as HP UFT, this tool provides a robust testing framework with AI-powered features like:
- AI-driven scriptless automation – create automated tests using natural language descriptions.
- Smart object identification – accurately identify UI elements for test automation.
- Predictive analytics – gain insights from test results, identify potential issues, and optimize future testing efforts.
Testsigma
This all-in-one cloud-based platform caters to various testing needs. Its best AI-powered features are:
- Test case generation – analyze user actions and recorded workflows to automatically generate test cases.
- Smart data-driven testing – organize dynamic test data sets that mimic real-world scenarios.
- Visual testing – detect visual regressions.
Mabl
This machine learning-powered platform simplifies test creation:
- AI-powered exploratory testing – learn user behavior patterns and automatically generate exploratory test cases, mimicking real user interactions.
- Self-healing tests – keep tests productive with app changes.
- Visual AI for regression testing – automatically locate UI issues.
Functionize
This cloud-based platform offers a unified testing suite:
- AI-driven test data management – generate and manage test data sets efficiently.
- Smart performance testing – identify performance bottlenecks and optimize test execution for faster and more efficient performance testing.
- Visual testing with AI-powered image analysis – investigate screenshots and identify visual regressions.
Applitools
This platform focuses specifically on visual testing with a powerful AI engine:
- Smart visual AI for cross-browser testing – automatically compare UI elements across different browsers and devices.
- Self-healing visual tests – adjust visual baselines as the application evolves, ensuring tests remain accurate despite minor UI changes.
- AI-powered image diff analysis – analyze differences between screenshots to pinpoint even subtle visual inconsistencies.
Best AI Automation Testing Tools That Are Open Source
Open source tools are truly a wonder. They’re free, customizable, and have large communities behind them. They may not be an option for everyone, as you’d need to work with them before they can work for you. But for teams with smaller budgets or highly-specific testing needs, open source is the source of supreme quality.
Katalon Studio
This open-source platform supports various testing methodologies (web UI, API, mobile) and integrates seamlessly with AI frameworks like TensorFlow and PyTorch. Katalon’s AI capabilities include:
- Smart test case generation – automatically generate test cases from user actions and recorded workflows.
- Self-healing tests – identify changes in the application and automatically adjust test scripts to maintain accuracy.
- AI-powered analytics – gather insights from test results with AI-driven analytics, allowing for better decision-making and test optimization.
While open-source, some of Katalon’s AI functionalities might be available only in the paid editions.
Testim.io
This platform simplifies test creation using natural language:
- AI-powered natural language processing – create and execute automated tests using natural language instructions, making testing more accessible.
- Smart object identification – precisely identify UI elements within the application, streamlining test script creation.
- Visual testing with AI-powered image comparison – utilize AI for automated visual regression detection.
The open-source version of Testim.io has limitations on features and functionalities compared to the paid plans.
Appium, Selenium, and Robot Framework
These three giants are well-known to any QA engineer and developer. They have established themselves as trustworthy and versatile tools. They keep occupying the top spots among open source testing tools.
And here comes the twist – they don’t have AI-powered features. Open source options (with artificial intelligence) aren’t numerous. Primarily because they’re still evolving. And because any work needs to be paid for. Once AI becomes more trivial, so to speak, we’ll see more open source alternatives. But for now, we have to use what’s available.
Appium, Selenium, and Robot Framework offer strong foundations for building custom AI-powered solutions.
Integrating AI frameworks requires some programming knowledge and expertise in AI concepts. Yet, the open-source nature allows for exploration and experimentation at a lower cost compared to commercially available AI-based automation testing tools.
Generative AI for Automated Testing
Generative AI is very likely to become a great helper for software testing services in the future. But GenAI now is still very green. Let’s get to know this little kid and prepare for what he can do for us when he’s all grown up.
How GenAI Works
Generative AI is a subfield of artificial intelligence and machine learning. Where it differs from the two is its focus – creating new data rather than analyzing existing info.
- AI encompasses a wide range of technologies that enable machines to mimic human cognitive functions like learning and problem-solving. Think self-driving cars, chatbots, and recommendation systems.
- ML is all about training algorithms to learn from data and make predictions or decisions without explicit programming. Consider a spam filter learning to identify spam emails based on past data.
- GenAI centers on generating entirely novel data (text, images, code) based on learned patterns from existing data. For example, that’s how DALL-E, Jasper, and GitHub Copilot work.
There are different techniques used in generative AI. But let’s review a simplified explanation of a common approach – Generative Adversarial Networks (GANs).
Two Neural Networks
A GAN consists of two neural networks working against each other. The first one, the generator, tries to create new data samples that resemble the real data it has been trained on.
The Discriminator’s Role
The second network, the discriminator, acts as a critic. It receives both real data samples and the generator’s creations and tries to distinguish between the two.
Continuous Learning
Through an iterative process, the generator keeps creating new data. And the discriminator keeps refining its ability to identify fakes. As they work against each other, the generator gets better at creating realistic data that can fool the discriminator.
The Result
Over time, the generator learns to produce high-quality, realistic data that closely resembles the real data it was trained on.
You might think that GenAI isn’t that different then. It learns from real data. And creates data that’s original but similar. But it’s the same as a book inspired by another book. Or like a painting of the same scenery but drawn by different artists. Would you say that such works are replicas? No. They’re their own creations, albeit somewhat related.
So, the big thing about GenAI is that it creates, not reproduces something else.
How Can You Use GenAI for Software QA Services
GenAI tools for automation testing aren’t really a thing yet. Don’t get too excited right now. But GenAI has immense potential. Yet, since it’s just taking root, we’re not entirely sure of how this potential will turn into practice.
So, for now, the baseline for GenAI use looks like this.
- Test case generation. Generative AI models can be trained on existing test cases and user behavior data to automatically create new test cases.
- Data augmentation. GenAI can be used to produce synthetic test data. It can be particularly useful for testing web apps or APIs where real user data might be limited or sensitive.
- Automatic test script repair. AI models can be trained to identify and fix minor issues in existing test scripts. Remember those self-healing tests we mentioned earlier? GenAI can take that to a new height.
The studies and experiments with GenAI are still ongoing. And you shouldn’t jump onto shiny headlines that promise for GenAI to abolish QA engineers and developers. It’s simply not where GenAI is at right now. And it won’t be for quite a while.
Limitations of GenAI & Why It’s Not Your Dream Solution
Specifically because generative AI is still so young, its limitations are mostly related to development. For instance, GenAI needs specific data sets. Gathering, filtering, and analyzing this data is a task to behold. And AI models need gargantuan amounts of info and lots of “learning rounds” to produce a decent output.
Next, to consistently provide reliable results, GenAI is to be transparent and realistic. In other words, we need to be able to explain how and why it arrived at a certain outcome.
If we talk about GenAI’s general constraints compared to human abilities, well, there’s nothing really new about it.
- AI might struggle to understand the overall context and purpose of the software. While QA engineers bring their real-world experience and understanding to identify creative and unexpected test scenarios.
- Algorithms might not be able to adapt to situations outside their training scope. A person, on the other hand, can use their reasoning skills and problem-solving abilities to identify the root cause of issues and suggest appropriate solutions.
- It may be tricky for GenAI to replicate the intuitive and exploratory nature of human usability testing. Observing user interactions and identifying usability issues often requires human judgment and empathy.
- Artificial intelligence models can inherit biases from the data they’re trained on. And that is a sure way to get skewed or incomplete test coverage.
Overall, we’d like to highlight one thing – take every resource with a grain of salt. Some articles hail AI. Some bash it to direct attention to particular issues (like fear of AI replacing humans). You need to focus on gathering info from both sides and come to your own conclusion. Better yet, try experimenting with AI tech in your project. If your budget and product allow for it and can profit from it.
To Sum Up
We can’t really say whether AI is objectively “good” or “bad”. It’s helpful. But what would be the long-term impacts of it on the IT sector and the world – that we just don’t know. The only thing we’re sure of is that human intelligence and creativity won’t be overshadowed by AI for a while. Perhaps never.
And for your business to thrive now and in the future is to work with genuine professionals. Knowledgeable developers and QA specialists are what secures a remarkable product. Not the sparkly tech stuff.