What is AI Automation: Its Future, Use Cases and Examples

What is Artificial Intelligence?

In the practical world, we can define intelligence as using knowledge to make decisions accordingly. In simple words, based on experience, one can understand what would be the suitable next step ahead. For instance, if there is a faulty dice that has shown 6 in 40 turns when thrown 55 times, it is safe to say that the next step could be (or would be) 6.

The above example is a case based on probabilistic analysis, which you might have already guessed. We can decide in that favor if there is a higher probability of an event. This is an ancient artificial intelligence method, and this is what started the journey of current artificial intelligence in the 1950s.

By definition, artificial intelligence is a field where we try to provide intelligence to machines so that they can show intelligence as close to humans as possible. For instance, chatting with an artificial assistant should be an experience similar to chatting with a human.

Today, artificial intelligence is much more complex and processes large amounts of information. For example, suggesting what a user would want to buy if they are interested in item “X.” This cannot be simply coded or logically apprehended with a couple of lines of code. Such a type of analysis needs a learning mechanism that can learn the changes in data and patterns and make decisions that may vary over a period of time.

We term this type of deduction, inference, and logic building “artificial” because it is possessed by a machine rather than humans. This requires large amounts of data processing to think about the future reasonably. As evident from this statement, unlike humans, AI makes rational decisions without involving emotions.

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Components of Artificial Intelligence

The brief introduction of artificial intelligence, as explained above, makes sense but does not create a boundary between practical implementations we see in the real world. For instance, what if someone says, “My website can do this!” and you can confidently answer it with, “That’s artificial intelligence!”.

The rise of artificial intelligence in recent years has promoted not only this term (even though this could be the most popular) but also terms that work in similar domains. For instance, machine learning, deep learning, etc. These advancements make things confusing as people start to use the terms interchangeably, whereas, in reality, they could be completely wrong. If we can clearly distinguish between all these technologies, our understanding can be foundationally strong.

The hierarchical representation of artificial intelligence is as follows:

It contains four technologies which all work in “knowledge-based processing” fields.

Machine Learning

The branch of computer science that deals with the learning of a machine is machine learning. In this branch, we use methods that help us learn from past experience (which is past data) and come to a prediction for the future.

For example, if a person is diagnosed with symptom A, symptom B, and symptom C, through machine learning, one can deduce (or predict) the disease from it. Although, it is important to note that even if we may have large amounts of data, the algorithms may not be 100% accurate. So we generally “predict” with a certain probability which is rarely 1.

Neural Networks

The human brain processes information using neurons and their connections with other neurons. This complete arrangement is termed a neural network which leads to all the decisions we make hundreds of times in a day. Inspired by this extensive system of networks, scientists use “artificial” neural networks, a layer-based networking system used for processing large amounts of data.

Each of the circles in this figure resembles a neuron of the brain which in neural network terms is called a “node.” With each proceeding layer, we eliminate a few choices and, lastly, arrive at an output. For instance, on inputting a few parameters into the facial recognition neural network, the network might start with steps such as the skin color of the individual, gender of the person, etc., in each of the layers. Neural networking is a part of machine learning.

Deep Learning

Deep learning is a subset of neural learning and is categorized as a system when the network has more than three layers. Since it does not have a distinguished branch of work, sometimes, we may find the “deep learning” category removed from the above pictorial representation.

All these three categories are subsets of artificial intelligence. Whatever they do, is a part of the artificial intelligence space. However, there are a few things that are AI but not ML, a term often used interchangeably with AI.

Artificial Intelligence

Machine learning is often viewed as a synonym for AI, but in reality, it is just a part of it. The difference between both technologies is evident in their names. While machine learning only deals with the “learning” part of the machine, AI works on providing “intelligence” to it.

For example, based on the previous data, we can determine what a user would like and show them the same items. This is explicitly seen in applications like Netflix to recommend movies and Amazon to suggest items. This is learning through past experiences. But suppose we can search for a movie or an item while speaking to the system. In that case, it is termed as intelligence even if what we said was not in English or mixed languages or sometimes a slightly mistaken pronunciation or word than what we wanted to say.

What is AI in Test Automation?

With this high-level overview of AI and other related terminologies, we can now relate this technology to Software Development Life Cycle (SDLC) methods and practices. AI can be integrated into various if not all, engineering and software development domains. But it's interesting to know its relationship with test automation in current times.

Test automation started as a process of handing repetitive testing tasks to the machine so that they can be performed error-free and in minimum time. One of the most effective results of this collaboration is regression testing which is often attached to pipelines and runs automatically. But our tests need intelligence of their own. They can only do what they are scripted to do.

Artificial intelligence in test automation means making decisions (preferably intelligent ones) based on the scenario it is facing. These decisions may impact any part of test automation where frequent changes are expected in the form of scripts or manually. The type of scenario may differ, and the same problem in different scenarios may also expect different decisions. For example, if a change is detected in the application's user interface, the corresponding test cases should also change.

Another part of inducting AI automation is intelligently creating test cases based on requirements or directly from the application like a human would do. This is also termed intelligent test case generation. These tests should cover all the parts of the application and work on increasing the overall coverage too. A data-driven testing angle is a good choice to start, but eventually, we may want to dig deep into artificial intelligence.

Like anything in the current times, test automation has yet to be left isolated regarding AI. As we explore in the subsequent sections, the AI applications over testing phases have given us good benefits that open an optimistic door for the future.

How AI is Revolutionizing Test Automation?

AI automation can be implemented anywhere where intelligent and conscious decision-making is required. This area has already started to be explored by developers and organizations, leading to a few changes (as described) we might expect in almost every tool within a couple of years.

Codeless Testing

The first area we could think of to apply AI automation is to remove the process of writing scripts. What if AI could write scripts automatically based on the tester’s input which would be in forms other than programming languages? This is the primary approach that has expanded over many tools today in testing due to its benefits and easy-to-learn nature.

Codeless testing is referred to the type of testing where a tester need not write automation scripts (or code) to perform actions on the application. How this is implemented depends on the organization and tool developers. It’s just that this foundational fact of not writing programming scripts should remain true in the end.

For instance, a few codeless testing tools have started taking input as English language sentences from testers. AI then applies natural language processing to understand the sentence and convert it to the test case for execution. To which language they are converted or how the AI output is understood by the tool is always kept secret, which is why AI-based codeless testing tools are available only on the cloud.

A simple example of a codeless test case is as follows:

open www.lambdatest.com 
click on “Login”
enter username
enter password
click on “Sign In”

Obviously, this is simple and does not require a “tester” to write the test case. In addition, such test cases are readable and maintainable, and people from other non-technical teams, such as business analysts and stakeholders, can also provide their input.

Self-Healing

AI automation has also given rise to self-healing technologies, which are also adopted in many tools today. Conventionally, automation test scripts were written according to the most recent UI and environment. When any of these two changed (even slightly), the scripts would fail instantly, adding the debugging time, maintenance time, and costs to the cycle. This is a fragile system, and as we see how frequently the environment and UI change, it’s also high maintenance.

Self-healing tests overcome this by applying AI to detect any changes and adjust the test cases accordingly. This also includes the changes experienced in the environment and user interface of the application.

AI achieves this by “learning” which test case corresponds to which objects and to which environment going as fine as the single line to understand the relationship. Before the execution, all the environments and tests can be scanned to determine if there are any changes and change the test cases (or the object repository) accordingly.

Object Location

Object detection or object locators is another problem currently solved by AI automation. Object location is the identity attached to any object on the application through which the automation test script would know on which element that test needs to be executed. Considering the large number of objects one screen might accommodate, object location is a time-consuming and hectic task for the testers, especially for the dynamic objects. For this, relative XPath is conventionally used.

Artificial intelligence works in object detection and locations to automatically detect the required object rather than continuously searching for one through Locators. This task is complex but better than the “ID and Class locator” type of rigorous methods. How artificial intelligence can be leveraged in this area depends on the organization. For some time, Appium and Test.ai have collaborated to produce an object detection model for mobile applications in Appium. For this, the tester can provide input in English for the object they wish to detect.

driver.findElement(MobileBy.custom(” ai:house “)).click();

AI will then scan each object to locate an icon that looks like a “house,” referring to the above code. This is, however, just an example of Appium. Different tools will define different methods for the same behavior.

Visual Testing

Visual bugs are of utmost importance as they are the first thing a user would notice when the application opens. For drastic visual bugs such as this:

It is easier to identify by looking at it. However, when the difference is just a few pixels in any direction, humanly, it is not possible to just look and point it out.

Conventionally, manual testers would do this job. Later on, with the high adoption of test automation, optical visual testing tools entered the market. These tools would keep a screenshot of an earlier version of the website for reference, which is the stable version. Then, they compare each pixel of the screenshot with the current web page to find the difference.

Obviously, this is not a guaranteed method in all the use cases and often results in false positives. Such use cases include elements that are not visual and those that move across web pages.

Inducting AI in visual test automation can detect differences similar to how a human would use computer vision. This is not as time-consuming as a pixel-to-pixel comparison and has the intelligence to know what to consider and ignore.

The following are steps (in-brief):

• First, the baseline image and DOM are captured by the AI model.
• Then, the new web page is scanned, and the DOM elements are captured.
• The elements that are not “visual elements” are removed from consideration. This is done by learning from past experiences.
• The elements from the base image are located on the new image (regardless of their position).
• The absent elements are quickly flagged and reported as “Not found.”
• The AI algorithm then scans the page and looks for visual errors.

It is important to note that as AI can detect visual changes like a human, it can also ignore changes that it thinks do not matter much. For example, a minor shade change in the color on any screen can be ignored as the human eye might not detect it. The AI solution behind the scenes is rarely exposed as complete businesses depend on their working and originality.

API Testing

Another major application of incorporating AI automation is API testing. APIs have become an integral part today carrying over data between various layers quickly and economically (as far as network and memory are concerned).

Due to this, its usage has increased significantly in a single application ranging from tens of APIs to even hundreds. Unsurprisingly, API testing has become as essential as any other testing method today and requires in-depth test cases for quality checks. But the problem is since it is so deeply woven into the system, even a minor change can ask for a change in more than one API and vice-versa. This makes the overall process expensive and time-taking.

The introduction of AI into API testing makes it a perfect candidate for a process that can heal and learn from experience. AI can learn and make relationships between test cases and APIs.

When a change is done on a test case (which can be handled through another AI process where the test case is linked to functionality), AI can sense it and make appropriate changes in the APIs. This effect can also be seen when APIs are changed, and the automatic change takes place in the test cases. Integrating AI into API testing is worth each penny if your organization has many of them.

These are some areas where we are already starting to see significant progress in artificial intelligence. In other words, these areas are the most visible ones if we wish to see how AI is changing the test automation landscape today. However, a lot more adoption has started to take place, which will take time to come to this level. These areas include intelligent test case creation, smart regression testing, and prediction of failures on the tests or the application features before running the tests, etc.

Advantages of AI Automation

By incorporating AI automation, we get the technology that is a “computer” but works closely like a “human.” This unique characteristic brings a lot of features for the testers in the process:

Need not code everything

When we perform test automation using scripted methods, we need to define everything we want it to execute. If any action is not scripted, it will not be performed. This is a challenge because of the ever-changing application and its elements.

Artificial intelligence replaces this problem by introducing methods of its own to learn, restructure and generate new things based on current scenarios and past learnings. So, if a new element appears on the web page, AI will detect it and keep it for matching when it comes to visual testing. We need not write a new code for it.

Can be implemented on complex products

As the technologies are changing, so are the products that are using them. With time, we see how the complexity of products has changed and increased to the extent that sometimes we can’t even match it with test cases.

When there are too many dependencies, variables, and parameters, artificial intelligence is found to understand the pattern and implement test automation based on its understanding. This definitely surpasses our experience with automation scripts and could be a great choice for complex products.

Improvement in test accuracy

Artificial intelligence facilitates greater test accuracy in two ways. First, it can write test cases that are accurate and generate accurate data for those tests. This process is done manually in some organizations, but that is no match with AI considering that AI rarely writes something that will raise bugs in the future.

The second part through which AI increases test accuracy is by improving other areas of test automation. When AI takes over this task of tests and data, testers do not need to spend any time going through automation scripts and manually optimizing everything. Their time can therefore be devoted to other areas which used to get left due to time shortage. Now when those areas are improved, the overall test accuracy automatically sees a surge.

Time saver

All the things achieved through artificial intelligence are done extremely fast. The amount of data AI works on to make decisions and improve accuracy and efficiency can take long hours if done through automation and days when done manually. Therefore, no matter how we look, it is not rocket science to deduce that AI automation saves a lot of time in that area. If this time is converted to money, we may see up to $1.2 million of ROI on just a single implementation, given that it is used aggressively.

Cost saver

The most direct impact an organization would see from adopting AI automation is on their expenses. AI does not need manual work, and even more than that, it does not even need constant maintenance according to changes. For the most part, it can maintain itself by constantly changing its behavior and methods based on learnings and changes experienced in the application. This saves a lot of man-hours, which directly means we save a lot on salaries.

In addition, the time a tester would have denoted here in maintenance can now be used elsewhere. So, we can push the application faster and save on additional man-hours here as well. When all this is combined, according to one study, an organization may expect as much as a 32% reduction in the costs for the project.

Work with a “human” angle

Artificial intelligence is capable of ignoring the changes that it thinks will not be noticeable to the end users. What if it couldn’t do that? We would first stop the execution as a failed case would appear. Then, we would debug the error, rectify the UI or test accordingly and then run again. All of this is when there are no impacts on the user’s side.

Artificial intelligence brings a “human” angle into the picture, which is one of the most significant reasons it is much more impactful than automation. When we look at things like a human, we can identify different errors and glitches in the system that would not be possible with automation. We could do that manually, but the time and money we save is what makes it most attractive.

Increased test coverage

AI automation can work on areas and features that are complex and cannot be manually worked on. Automation testers fear going deep into such areas as they increase the complexities and hence the maintenance work in the future. In reality, when we need to choose between high-maintenance work and increasing test coverage, it is always practical to stop at a certain point.

AI can simplify this and can crawl into corners of the application to create test scenarios that are not only simple but also of high quality. As a result, the test coverage increases, which is a great sign of high-quality tests and products.

Complex problem solver

In the previous section of this AI automation tutorial, we highlighted a few areas that have already been covered by AI. No one would have thought while writing scripted tests that someday, plain “English” would become a scripting language. This may look simple at a high level, but it is a very complex problem that could not be solved before natural language processing came into the picture.

There are many such problems that have been solved, are in progress, or are currently just hidden, waiting to be discovered by an AI model. Test automation needs such implementations to create newer and more efficient ways of testing.

When AI solves such problems, it makes test automation much simpler and faster, which is the end result that everybody desires.

Lower maintenance work

As we have repeatedly focused on this point, it is a critical point to remember that AI automation can work on its own most of the time. The solutions implemented by AI are simpler and easy to maintain. They bring down overall maintenance requirements and help provide additional time for the tester to work on other tasks.

Replaces the need for "human-executed" testing

Automation shares a lot of work with manual testers in almost each domain. However, there are a few areas or types of testing where automation cannot be applied. Actually, it should not even be tried because it invalidates the whole idea of such areas. This is because testing methods such as exploratory testing and usability testing are performed to know how a user would explore the application and uncover the bugs accordingly.

Artificial intelligence’s whole working criteria revolves around making testing more “human-like.” Due to this, when we try to establish exploratory testing using AI, it can learn and explore different areas providing different results each time. Mimicking such a type of testing eliminates the idea of hiring end-users, and on top of it, AI can do a much better job. It will ensure that each area of the application is explored, which is not always the case with end-users.

Ensures diverse collaboration

Finally, AI invites people from other teams as well to contribute as the process is easily understandable, including the results. This helps in diversifying the input methods and takes contributions from non-technical people as well. The more everybody understands, the better their input becomes, which in turn improves testing and hence the application quality.

This list can be extended to even further points as AI’s introduction to test automation has been a lot fruitful. While these could be the most popular benefits, we invite suggestions from people who have been working on their projects with AI and have experienced a benefit they would want to share.

How does AI Automation help in Digital Transformation?

Digital transformation has been a constant process not only in test automation but in any domain of information technology. We first started by converting physical objects to digital elements, but at this point, we transformed from one digital landscape to another. AI is that technology after automation that has helped in digital transformation in the overall testing phase. The following points will help prove this transformation with examples:

• Before AI, automation engineers needed to convert the requirements of the stakeholders to test scripts using different scenarios. Artificial intelligence can transform this process by introducing chat-based interfaces. These interfaces can take the input from a tester and generate test cases themselves which can then be attached to the suite.
• Conventionally, whenever there is a modification in UI or other parts of the application, automation scripts are required to be modified appropriately. AI has changed this scenario by introducing a self-healing mechanism.

With this in the picture, AI can automatically detect the changes and apply them to their respective test cases. A tester need not do anything about this. This part, however, is currently applicable to small changes such as locator-based change. Although, it is slowly expanding to more complex and broad areas of test automation.



• The digital transformation brought by AI automation has significantly impacted how reports were analyzed before. If the tests are extensive and the application is large, we may brace ourselves with a report with too many pages.

Such large reports take time to analyze. Due to this, we may see many comments from other team members to display the results in graphs and pie charts for better understanding. Hence, more code was written to accommodate these requirements.

Although it didn’t solve the problem of finding out what you are looking for in a long report. AI changes this process by introducing a system where all you need to do is raise a query in English on a system, preferably chat-based. The AI system analyzes the query such as “Show failures in a graph” and provides an appropriate input to the tester.



• Cloud-based test automation has also picked up in the last decade. This is a digital transformation on its own as it provides browsers and devices online accessible through any modern browser.

But since all these browsers are made and work differently, there are challenges in running the same suite in all of them. AI can help us take this step further and create tests according to the device and target platform, which can even be magnified according to usage.



• The most powerful weapon of artificial intelligence is to predict the future with very high accuracy. With these prediction algorithms, we can accurately change our strategies even before those scenarios occur. This can prepare us for the future, saving time and essential resources.
• With the same technology, we can predict bugs early on without applying a complete run which generally takes hours.
• Finally, AI can help us understand customer needs very accurately and suggest tests and strategies accordingly.

Digital transformation is a constant process, but seldom is there a new technology that turns it around. When it happened with scripted automation, it stayed for a while, but advancements kept happening in the same domain. A similar thing will be noticed with AI as well. Artificial intelligence is here to stay, but we will keep seeing improvements and adding more points to this list in the future.

Difference between AI And Machine Learning

The terms "artificial intelligence" (AI) and "machine learning" (ML) are often used interchangeably, yet they hold distinct meanings. AI pertains to a machine's capacity to execute tasks usually carried out by humans, encompassing learning, thinking, and problem-solving. In contrast, machine learning is a subset of AI, entailing the utilization of algorithms for scrutinizing data, deriving insights from it, and formulating predictions or decisions based on the data.

Incorporating AI and machine learning into software testing can usher in automation across diverse facets of the testing process. For instance, AI-driven testing solutions can assess the software under examination and formulate test cases that encompass pivotal aspects of the application. This automation not only conserves time and effort for software testers but also guarantees comprehensive testing of crucial sections of the program.

Moreover, machine learning can be harnessed for scrutinizing data in testing and recognizing emerging patterns. This approach streamlines the focus of testers onto the most noteworthy features of the application, thereby diminishing the number of requisite test cases. Additionally, machine learning has the capability to autonomously pinpoint defects and errors, which proves particularly beneficial in the context of extensive applications, where manual error identification can be a protracted and less efficient endeavor.

AI-driven key testing approaches?

• Differential Testing: Differential testing is a method wherein we meticulously categorize and scrutinize disparities between application versions, comparing them iteratively across different builds.
• Visual Testing: Visual testing represents a sophisticated software testing technique that involves a comprehensive evaluation of an application's aesthetic and user interface elements. This involves leveraging image-based machine learning and conducting meticulous screen comparisons to ascertain the consistent visual experience.
• Declarative Testing: Declarative testing sets its focus on articulating the intended outcome of a test using natural or domain-specific language. In this approach, the testing system is responsible for determining the optimal test execution method, aligning with the specified intentions.
• Self-Healing Automation: Self-healing automation denotes a dynamic testing process where the system autonomously rectifies the selection of elements within tests, especially in response to alterations in the user interface (UI). This adaptive mechanism ensures the uninterrupted functionality of tests as the UI undergoes transformations.

Conclusion

Artificial intelligence has been in the spotlight recently, not only in information technology but in any field we can imagine. Want the quick formula for something? AI chatbots can help. Want automatic adjustments in test cases? AI can do that within minutes. Want to generate patterns out of data quickly? AI can understand and draw informative patterns quickly! With so many AI-based technologies popping up in the market, test automation has not been left alone.

Today, test automation has adopted AI in its methodologies, and some of the frameworks have completely shifted the way they used to work before. These tools want to make the tester’s life as easy as possible, and currently, AI seems to be the best choice for it.

As a tester, there are a few prerequisites we should always keep in mind before starting with AI automation. The first and most prioritized is a basic understanding of AI. I know that some tools provide an easy approach where only “how to do” is important rather than “how it is happening.” But as AI grows as a branch, a deep understanding of it will help you grow professionally as an AI tester.

Another important aspect is what are the current applications and which application suits us well. Currently, there is no single tool that contains all the AI capabilities that are available in the market. Some will provide visual detection, while some automatic dynamic locator detection, etc. Combination of blockchain and AI can produce automated decision making systems that are far more intelligent you can learn more about blockchain testing through our hub.