he majority of advancements in application testing have been from manual testing to automation testing, which uses specialized technologies to run test scripts automatically without any human involvement. Automation is the most acceptable approach for increasing testing’s effectiveness, productivity, and test coverage.
With the use of testing frameworks and technologies, developers or testers create the test script for automated testing. The test script automatically tests the application without any human involvement, and the results are displayed. As opposed to automated testing, manual testing relies on the test engineer writing the test cases and implementing the application based on those test cases.
By automating repetitive processes and other related tasks, test engineers can increase test coverage while also saving a lot of time. However, manual testing requires testers to repeat the same tasks repeatedly, which is a tiresome process. Therefore, we can conclude that test automation primarily focuses on automating manual human work with systems or devices.
Using an automation testing tool makes it simple to approach test data, manage test execution, and automatically compare test results with expectations to determine whether errors or bugs are present or the application is free of them.
The technical landscape is evolving further toward codeless automation and automation employing bots that are utilizing next-generation technologies like Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning to ensure the deployment of applications that fulfill both compliance criteria and customer needs for quality (DL). To make the testing process easier, quicker, and more trustworthy, they have become more in demand in recent years.
Artificial intelligence (AI) was once simply a trendy term, but it is now an essential component of daily life. Artificial intelligence (AI) is prevalent today, from self-driving cars to smartphones, smart devices (Alexa, Siri, etc.), smart apps (Cortana, Google Assistant, etc.), and smart home appliances. Every organization uses AI, including banking, insurance, fintech, healthcare, and more. Without a doubt, this technology has a demanding presence in the expanding field of application testing.
While the application development process is kept moving along by modern methods like Continuous Testing, Agile, and DevOps, utilizing artificial intelligence is crucial for maximizing the effectiveness of application testing. Better test coverage, greater test accuracy, a shorter time to market, and a significant decrease in total testing expenses are all guaranteed by this advanced testing.
Automated application testing techniques incorporate AI to make tests smarter, more dependable, and more effective. Organizations still encounter several problems related to traditional automation testing techniques, but these problems can be solved by using AI in particular.
Before doing that, it’s crucial to thoroughly understand AI to gain a deeper understanding. In a later section, we will talk about how it might improve automated application testing.
Artificial Intelligence
Artificial intelligence is a technology that can support human cognition and activity. It has progressively self-learning capabilities. It comprehends the environment, resolves human issues, and carries out human tasks to accomplish its objectives.
For instance, AI is used by Google Maps to track traffic patterns and propose the best routes, by Alexa or Siri for voice control, by computer chess opponents, by self-driving cars, and by Netflix to recommend content based on user preferences.
Artificial intelligence (AI) systems work by inputting data into intelligent algorithms that may learn and develop by evaluating patterns within the data; the more data these algorithms acquire, the better they comprehend the environment and predict patterns. Depending on techniques for pattern matching, these systems can be programmed to carry out both straightforward and difficult tasks.
It can be defined as a computer program’s or machine’s capacity to reason independently, think critically, and learn through the gathering of information and the modification of future behavior in a positive way.
How can AI assist in solving problems associated with test automation?
We can expand testing coverage and speed up the application validation process with automated testing. However, implementing test automation comes with several difficulties that, if not resolved, could give testers endless nightmares and lead to the failure of application-automated testing. The difficulties encountered during automated testing are listed below with the ways that AI aids in overcoming these test automation issues.
Finding the perfect group of individuals
Finding the proper team with the right skill set for scripting is one of the biggest challenges in automated testing.
By utilizing AI-based test automation solutions, testers can overcome the difficulty of assembling the correct team and skill set by having access to a partially or entirely codeless scripting platform.
Time spent on repetitive tasks
No matter how reusable the components are, teams always find themselves creating a lot of the same code over and over again, which takes a lot of time.
By using AI to build test scripts fast and autonomously, this problem can be solved. To automatically generate test scripts for projects that are comparable, AI systems can be taught based on the inputs and outputs of past projects.
Flaky tests
Testing teams spend hours figuring out why a test failed, such as if there were problems with the application or with the test cases themselves. These kinds of test failures are referred to as flaky tests, and they cause releases to be unnecessarily put on hold, which delays the delivery of the application.
Once more, AI is there to assist the teams in overcoming the problem of inconsistent tests. AI expedites the process by producing more robust test cases and identifying patterns in seemingly random test failures.
Frequent script updates as a result of UI changes
Organizations frequently alter the app’s user interface to create a seamless user experience. Even if the changes are minor or barely noticeable, they could cause certain activities on the website to fail the test scripts.
Tools based on AI algorithms can be trained to identify minute changes in the code or application issues. These technologies can then respond appropriately and prevent the need for manual script updates for such small adjustments.
Choosing the right test size and test cases
Regression tests must be run after each modification to the application. The complete regression test suite, however, cannot reasonably be run after each change in a fast-paced workplace. Therefore, it has always been difficult for testing teams to choose the appropriate number and set of test cases to run for a certain update.
AI can assist testers in creating algorithms that establish the regression test suite for the specified change based on various characteristics and anticipate the appropriate size.
keeping the test scripts and suites up to date
Maintaining a large number of test scripts becomes difficult when an application grows in size. Since AI technologies may be used to maintain and extract the necessary test scripts following testing criteria, AI can be utilized to overcome this difficulty.
As a result, AI assists in overcoming the difficulties associated with conventional test automation, which is regarded to be the beginning of a revolution.
How AI Can Improve Test Automation
Over the Past Decades, application testing has advanced significantly. Manual testing was the first step, followed by early automation, agile testing, and finally continuous testing. It has now attained the testing phase for AI-augmented autonomous testing.
Although achieving autonomous testing may seem impossible, you can begin integrating AI features that will provide the groundwork for it. AI can improve testing by expediting test generation, increasing test coverage, and lowering test upkeep.
Unit testing, user interface testing, API testing, and maintaining an automation test suite are just a few advantages of using AI in test automation.
Enables the unit testing process to be automated
Unit testing is frequently used as a component of continuous testing or CI/CD in DevOps. Unit tests take a lot of time for developers to update and maintain while building application code is far more enjoyable. In this situation, AI-based techniques can be utilized to create automated unit tests.
AI can analyze the program statically to determine which code areas aren’t covered by unit tests. This data is used by AI-based software testing tools to create unit tests for identified code. These tools can update the tests when the source code changes, and even produce new unit tests.
UI testing procedure visual validation
To travel through the application to look for visual faults and assure the visual validity of the program, UI testing can make use of AI by using picture recognition algorithms. AI-powered visual testing is a QA technique used to ensure that the application’s user interface appears as intended to the user. It makes sure that the various visual elements, such as color, size, shape, arrangement, etc., do not mistakenly overlap or go unnoticed.
The AI tool for final validation by QA engineers creates the screenshot. Because the human eye can miss these minor visual flaws, AI is beneficial in precisely identifying them at a much faster rate.
Assist in the execution of the API testing process
AI algorithms comprehend the relationships and patterns between various API calls and properly categorize them according to the situation. It uses current tests to understand how APIs relate to one another, then makes use of this knowledge to comprehend API changes, update existing tests, or develop new scenario-based tests as needed. AI aims to create more powerful tests that are more comprehensive, reusable, scalable, and change-resistant.
Creating more reliable automated tests
Most of the time, tests fail because developers alter the program since they can’t locate the elements it needs to communicate with. AI tools can automatically adapt to these changes, making the test cases more reliable and manageable. AI can also be used to write test cases for the application.
Additionally, the tools can identify places in the application that are weak or indicate locations with insufficient test coverage by analyzing the present test coverage.
Make it possible to run more effective automated tests
Developers need to perform the full test suite because of a minor modification found in the application. However, they can use AI technologies to examine the present test coverage and identify places with inadequate coverage or highlight vulnerable spots in the application.
For instance, AI testing tools begin to learn and watch how the application changes, autonomously making decisions at runtime all without the need for tester intervention. And if the application is constantly evolving, it is no longer a concern because AI can automatically change the script if any deviation is found. The testing is more dependable and consistent since the test scripts can automatically adapt to the changes.
Enhances continuous testing
AI enhances continuous testing by removing the possibility of manual intervention. AI in the testing process aids in early and quick bug detection and prompts corrective actions to ensure testing process continuity. This minimizes defects, makes sure that only bug-free code moves on to the next stage, and contributes to an overall improvement in the quality of the software development lifecycle, making the program stable and free of bugs for users.
Leveraging Artificial intelligence (AI) for test automation with LambdaTest
AI in test automation for applications has become a powerful trend since it can advance testing. Without a doubt, it encourages testers to create additional tests and guarantees the speed and reliability of automated testing.
However, incorporating AI into application test automation is a complex process, and support from professionals is required. A Next-Gen QA and independent application testing services provider like LambdaTest can help organizations use AI testing for high-quality applications and quicker time-to-market.
It is possible to automate both functional and non-functional testing (load and performance testing) with LambdaTest at every level of the application development lifecycle, from code analysis to user interface testing. This platform accelerates test generation and maintenance using AI algorithms. The user can use this platform to test mobile apps on several Android and iOS devices by utilizing the power of AI.
The LambdaTest AI solution aids in the early detection of flaws and ensures compliance with numerous functional safety, security, and coding standards. It lessens the workload on teams and guarantees that critical design flaws are fixed first. It offers support for all major browser versions and can seamlessly integrate with Jenkins, TeamCity, CircleCI, Jira, and other CI/CD tools. And iIt also allows users to test the functionality and appearance of the app on a variety of devices, including smartphones, tablets, laptops, and external monitors.
LambdaTest is a cross-browser compatibility testing platform that is hosted in the cloud. It enables developers and testers to manually and automatically carry out Selenium, Cypress and Plywright testing on a cloud of more than 3000 real devices, browsers, and Operating System configurations.
It enables users to run end-to-end Selenium automation tests online on a scalable and reliable cloud infrastructure or perform live interactive cross-browser testing online.
Additionally, it allows users to reduce build times by running automated test scripts in parallel on an online Selenium grid that is dependable, secure, and scalable. It also accelerates test build execution speeds.
