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New Era Coming

All modern technologies actively absorb the maximum numbers of human activity fields. And obviously, they affect such process as software testing.

Each of us is involved in proses of testing automation in such a way that will be glad to leave some part of his/her functional responsibilities (test plan creation, software validation) to artificial intelligence technologies.

Instead of manual creation of the automation tests, modern and future technologies will make and deploy such tests by themselves, moreover, on an ongoing basis, the interaction between technologies and testers will be improved.

Such mechanization of all testing means that any web development command will have tools for the deployment of virtual “testers” team that are different for improved operation speed and level of technical coverage more than the most experienced and high-qualified groups of QA departments.

Manual Testing

Nowadays manual testing is very time-consuming and costs a lot of resources, needed for its optimal maintenance. The most advanced project groups constantly make thousands of lines of program code for their application and programs: every time they create lines where is said what should be done to reach one or another result.

The relevance of manual testing in development of AI

The relevance of manual testing in development of AI

Such an approach, in part, has many disadvantages, including constant financing of a testing group. Continual work on the creation and support of such tests distracts developers and testers from their basic aim – actual work with web product.

Moreover, the start of such scripts needs the power of all available machines and computational technologies as well as human resources for doing some tasks manually. All this takes our priceless time really quickly.

Labor costs and autotests

Labor costs and autotests

Full execution of all created tests can take from couple days to the whole month. It’s totally against the rules of developers, who try to deploy and modify their applications continuously. Manual testing can’t keep this speed, no matter how hard developers try.

Complexity of Tests: Why Manual Tests Are Unacceptable

Unfortunately, manual testing processes can’t be scaled, because they are developed only once. Adding test scripts is a simple linear activity. Constant adding of functions to a web product can increase its main complexity in an exponential manner as most new functions are based on the interaction with the previous ones.

At the beginning of its development, manual testing can be alongside with increasing general functionality and productivity, but the more complicated and bigger a web application becomes, the more difficult it is to provide its test covering manually.

What really upsets in manual testing? It deals only with groups of test cases and nothing more. If a new function or a group of parameters is added to the product, the test case, that has been created before, will be finished successfully despite the fact that new features may be under a fault condition.

Only after exploratory testing, developers can see that something goes wrong. Precious time is wasted on the restudying of the product, so in practice, it often happens when manual testing checks the functionality of the product in general and doesn’t deal with its separate components.

Is There Profit from AI?

A methodology of the AI solutions usage for testing is a quite winning option in those cases if developers face a lot of problems during manual testing. If it was possible to operate with simple AI, whose parameters could do and record user metric when interacting with a web product, then it would be able to record up to several tens of thousands of test cases on the regular basis in 10-15 minutes.

What if it was possible “to introduce” the program to 1001 bugs and to show examples of a correct product test? AI could quickly foresee where exactly the project group should work for it.

If AI got the opportunity to test many other applications, bots would get priceless experience, which in the future could be useful for testers team to make a correct decision about the deployment of test technology. It means that in some time, the concept “bot” becomes true and reasonable.

AI in testing

AI in testing

AI notices every single detail that potentially can appear or be removed from the structure of a web app. In those cases, when manual automation misses serious changes in the last build, AI bots will automatically click on the button, that was added to a web product, and pay user’s attention to the image that disappeared from the application. Bots can continuously estimate all changes and put it in order of their role in the structure of a developed web product.

AI in Testing: “Opposition’s” Opinion

Advantage 1 – my app is unique; why do I need AI’s solutions?

In practice, your application or web product looks like hundreds of thousands of other ones. If we divide it into components, you’ll notice that the utility has buttons, info entry fields, images, database and many other things that are in any application.

It means logically that if AI can effectively analyze and test any application productivity, bots will “get on” easily with your technical components.

Advantage 2 – a human is always smarter than any programmed machine!

A few decades ago many spheres of human life “thought” that they wouldn’t undergo an insensitive automation process by technology and AI. But the truth is that you know exactly what you’ve learned doing one or another repetitive process.

AI bots are like Terminator: are never tired, forget nothing, go ahead to their end goal without fear and losses. Do you really think that you’re smarter than a hundred robots, which are connected in the same ecosystem and analyzing the work of your web product? Even if you’re better at something, don’t you want to have a few programmed robots that would do all that’s necessary instead of you? Meanwhile, you would deal with other creative problems.

Advantage 3 – Maybe bots are a good thing, but how does AI know what data should be used for tests?

If we take a guess and think carefully, we’ll make such a conclusion: today a lot of apps and utilities operate with the same data. Field names, email addresses, names, phone numbers, search inputs under specific profile, digital content and so on.

90 percent of data flow incoming to your app consists of instance data which can be classified and structured effectively. For a small input data set it’ll be enough to make a network of powerful testing, based on AI solutions. At least, all this will be totally enough for you to have a reliable and validate assistant on the stage of development and testing.

Advantage 4 – Ok, but how will AI know that app works well?

It’s a good question. Really, how can you find out that app is working good? You never did this (check its productivity). Yes, you have some test suites, for example, 100 computerized test scripts, they are doing great, but their functionality was programmed only for testing of some space or structure only.

Also, you know that the main thing is customer feedback, as well as work on systematic improvement of errors and bugs that were founded. All you want to know is if everything works as it should.

AI bots were programmed just for searching something “out of order” and unfixed, they can do hundreds of thousands of tests in your product, searching for errors to make sure that pre-set functionality is working by a completely bug-free script. On the basis of instant scanning, the bot can tell you that only 98% of your product is working properly, and all broken was recorded before the final release, and you need just to solve found problems.

Is it possible in the real world that bots will be in testing? For now, AI solutions have already started their functional studying, testing the productivity of hundred most various large apps and products on the market.

A Quick Look at Already Available AI tools

It will be logical to say that in the testing field it’s already possible to use some tools, based on AI usage, which really help testers to perform their functional duties high-qualitatively.


With its help, you can quickly find bugs inside the user interface. Applying some AI and machine learning solutions, testers can easily find errors in UI-interface.

The application lets quickly set test format to the necessary form of display (adaptive form, desktop resolution). If your product has an animation, you can program the utility to find it.

Sauce Labs

It’s one of the first programs that opened the way to start tests in a cloud. The service launches up to 1 million automation tests every day. On the basis of machine learning solutions, developers of Sauce labs work on the creation of powerful toolkit on analytics of web product quality.


With its help, you can merge AI and Selenium. The tests are created in a simple form, which is visually very similar to Gherkin. There’s no need to code anything or deal with locators. AI parameters identify dynamically utility window and elements as well as automatically start the test of web product functionality. With the help of this tool, it’s easy to fix the whole produced tools verification.


It allows starting a functional test of the utility. A programmed test can self-learn easily. There’re tools for a fight with flaky-tests. Also, there’re parameters to test the dynamic changes of elements and forms. Comparison of scan history can be done in the logs, it helps to find changes in the utility.


This is a specially programmed utility for a group of functional testers. Such an app allows creating tests even without programming skills. There are functions of test recording, standardizing of assertions and utility fields, which need special attention.

In conclusion, quality assurance companies in the near future will be able to use available AI solutions constantly, which will allow to bring new opportunities in the testing field and make QA department’s work quite easier.

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