AI or Artificial Intelligence has already started its journey from the future to the present. It is no longer just a project topic named “AI and Machine Learning - The Future”. In the present world, AI systems are being used in numerous fields with continuous expansion in its scale and quality. You may surely get astonished by seeing how the voice assistant applications (e.g. Google Assistant, Amazon Alexa, Apple’s Siri, etc.) and all other voice search features execute complex tasks by just some simple voice commands from us. Extremely powerful and data-filled AI is working behind the functionality of those features. You may also have noticed bots performing unimaginable things in sci-fi movies. You must know that many of those magical things are no longer treated as just fiction. With the supreme power of AI, machine learning, and deep learning, many of those fictional things have become reality combining the power of machines with human intelligence.
Now, what does AI have to do with software testing or quality assurance? Well, it does a lot that we can even barely imagine. From all our articles, you must have already understood how important it is to perform continuous testing on every software to ensure that it is working as expected. Traditionally, such tests were mainly executed based on the test codes written by developers considering the normal actions of the applications and comparing them with the new version that is going to be released. So, it looks like most of the work here is being done by testers’ intelligence and coding skills applied in authoring the test scripts. Now, you surely know if the testing frameworks that are testing the applications can use its own intelligence, the results will surely be beyond imagination. That’s where AI comes in to do all the work by itself and help you achieve much better results. However, there is more to the massive contributions of AI in the agile software development & testing process as well as the entire DevOps so let’s dive into it.
AI-based software testing means using the technologies like Artificial Intelligence (AI), Machine Learning (ML), and some other relevant technologies for testing software applications to ensure they are looking & performing properly to provide the user experience as expected. The main working method of software testing is performing different iterations of user behavior on the application and observing if every functionality of the application is completing the task expected for those conditions. Now, AI and ML algorithms have huge data sets available to them and they mainly work by acting as neural networks making predictions based on those test data sets. Hence, you can clearly understand that those technologies can predict a lot more scenarios of different choices made by the end-users and eventually, turn out to be great testing solutions to execute more efficient tests.
However, there is often a debate that test automation tools can also execute the tests without human intervention during the process. So, how does AI do that differently or as widely said, more efficiently? For that, you need to carefully go through the further sections of this article.
Test automation is indeed an extremely helpful invention of the modern tech world but without the support of AI in it, you can still experience some challenges that we have jotted down below.
Everyone knows that AI is extremely powerful but it is a technology that needs an efficient medium to perform its tasks. In case of software testing, AI-based test automation tools like Preflight are the medium to bring this awesome technology into use. These tools follow really effective approaches to deal with the challenges and reduce their impact on the entire testing process. Now, let’s check out their actions against the above mentioned challenges.
All the effort is for reducing the time and resources used for software testing so, naturally, you don’t want the test suite to take so long that there remains no existence of efficiency in the entire process. Let’s see how AI fulfills that wish of yours by supporting the test automation process.
You got to know about how fragile test cases lead to more efforts on maintaining them. Such tests tend to break easily due to any small change in the code or the traditional element identifiers like CSS classes.
On the other hand, advanced AI-based test automation tools like Preflight uses highly effective AI features like context awareness, optical character recognition (OCR), computer vision, etc. to identify different elements on the applications’ interface. Hence, those test cases are extremely robust and have very little to no chance of breaking. As a result, there remains no significant hassle for test maintenance.
As you already know that even after using efficient test automation tools or frameworks like Selenium, if the test cases have low-quality, the overall efficiency of the testing process gets reduced. In the article “Best AI in Automated Software Testing Tools”, you have already seen how the advanced AI features efficiently analyze the elements, their usability, and identify reusable components that can be called rather than creating duplicate test cases. Hence, it is evident that the use of AI in software testing increases your capability to create high-quality self-healing test cases.
As we discussed in the previous point, duplicate test cases make the test suite extremely time-consuming and make you waste a lot of effort & resources on them. Now, duplication in the test cases cannot totally be eliminated. A huge test suite can surely have some test cases that are mostly unnecessary as they are just carrying duplicate efforts of some useful test cases. Now, an ordinary testing tool may not be able to identify those duplicate test cases but an advanced tool like Preflight can easily can. Using such AI testing functionalities from the beginning can help you avoid adding such unnecessary test cases during the coding phase.
Also, sometimes, a few parts of some test cases are repeated multiple times and naturally, testing a specific part of a component is not always necessary. Use of AI during creation of test cases can identify such elements and keep them in reusable groups so that the necessary test cases can be properly executed.
Test coverage means what percentage of your application as well as its functionalities are successfully tested. A higher test coverage is one of the prime requirements for a successful technology business. In all our articles, you have already gone through discussions on the importance of using codeless test automation tools like Preflight for improving your test coverage. That’s because Preflight makes the best use of advanced AI features to identify -
Identifying the above-mentioned portions is a part of a risk-based approach that AI-based testing tools follow. Such automated software testing tools evaluate past exploratory testing sessions to new test generation while focusing on more comprehensive coverage. The efficiency can be further improved by combining it with a risk-based approach as mentioned earlier.
An in-depth analysis of the benefits of using AI in software testing can surely motivate you more to start using it now. So, let’s take a look at them.
Users get the first impression of your application by primarily looking at it before interacting with its components. That’s why it's extremely important that the components of your application’s interface look as they are designed and provide a satisfaction to the users through their visual representation. AI-based testing features include amazing pattern recognition and image recognition capabilities that can easily detect visual bugs through efficient visual testing practices. AI is easily capable of recognizing dynamic UI controls regardless of their size or shape, and it analyzes them at a pixel level to understand them better. From all the discussions in this point, you can surely understand that AI is highly effective in ensuring that all the visual elements on the screen are truly engaging and working properly.
One of the greatest targets in efficient software testing is increasing the test coverage to provide well-tested properly performing software products to the users. The capability of AI for smoothly checking every minute detail of the file contents, user actions, data tables, application memories, internal program states, etc. can let you test more within less time and achieve remarkably high test coverage.
Human beings hold an unimaginable level of intelligence but they are not machines. Hence, in over workload situations, human errors can sometimes occur. However, when you are doing business in a highly competitive market, even the smallest flaw in your source code as well as in your product can cause massive harm to your business.
On the other hand, AI performs through machines, which do not have problems like fatigue, deviated attention, etc. Hence, naturally, AI can perform the testing operations seamlessly, and eventually, bring better accuracy to the test results.
Time, money, and effort are the top three resources that decide the success or failure of your product as well as your business. If you end up wasting a lot of these resources on developing the product, the efficiency and competitiveness of your product gets negatively affected. And, one of the major reasons behind such wastage is dependence on human resources for everything in the software development life cycle.
That’s why using AI through advanced test automation tools like Preflight helps you get rid of the time-consuming manual testing process, and you can save all your resources including time, money, and effort.
Well, it’s kind of the result of the other benefits that you receive from AI-based software testing. That means as this efficient testing approach enables you to achieve a higher test coverage with great accuracy through least efforts & expenditure in the least amount of time, you can surely get your product from the development stage to the released stage much faster than would happen in the AI-independent process.
After all this discussion, it goes without saying that the use of AI testing can detect bugs significantly faster and lead you to faster debugging to provide your users with nearly perfect, bug-free applications.
This article shows you how the advanced AI-based software testing features can -
Now, this set of amazing software development and testing experience can only be achieved through using advanced AI-based test automation tools like Preflight. The remarkable AI testing capabilities of this highly effective tool can easily be known from the article “Best AI in Automated Software Testing Tools”. To experience such unimaginable automated testing features, you just need to sign up with us.
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