The majority of the advancement in application testing has been from manual testing to the incorporation of automation testing. The practice of test automation is essential for quality assurance. Organizations aim to move to automated testing because it offers quicker and more efficient results than manual testing.

The competitive market and growing technical complexity have strengthened the Software Development Life Cycle (SDLC) to deliver error-free applications to the market quickly. Automation of the testing procedure and quality control results in considerable time and resource savings. A solid quality assurance plan, the appropriate tool, and the appropriate technology are crucial for success in any test automation and the delivery of error-free applications.

Using artificial intelligence (AI) and machine learning (ML) in combination with test automation greatly improves the QA experience while also helping to reduce the need for manual labor and achieve additional benefits.

Without the need for manual work, or for rewriting and improving test cases for various scenarios, AI and ML in test automation offer better and more efficient outcomes which free teams from repetitive tasks and improve testing.

This article will examine how ML and AI are used in test automation, including what factors to take into account while using them, and how they might improve application testing. To start, let’s define what AI and ML are.

Artificial Intelligence

Artificial intelligence is machine intelligence that can learn on its own and make decisions that can support human thought and activity. It achieves this by understanding the environment, resolving human issues, and carrying out human duties.

These kinds of AI systems work by feeding huge amounts of information into smart algorithms that can learn and develop by identifying patterns in the data and using those patterns to predict future events or produce the desired result. In addition, using algorithms for pattern matching, these systems can be trained to carry out both easy and complicated tasks.

In brief, machine learning is a crucial aspect of the rapidly expanding field of data science. Testers can enhance their tests due to this technology. It deals with creating useful algorithms and relying on a set of instances of specific events rather than having them explicitly written.

For instance, google maps make use of AI to track traffic trends and suggest the best routes. An intelligent home assistant, a fitness tracker app that uses AI to enhance training, and an app that makes product recommendations based on a user’s purchasing habits. There are some streaming services like Netflix that use machine learning, a kind of artificial intelligence, to suggest shows based on user preferences.

Machine Learning

The study of machine learning focuses on how to make computers behave without explicit programming. It is a type of software technology that stores and performs statistical analyses on data using mathematical models and then uses the analyses findings to improve the models.

Machine learning mimics human learning by using data and algorithms, gradually improving its accuracy. Every day, ML is becoming more and more prevalent in our lives, from e-commerce recommendations and self-driving cars to computer games and speech recognition.

Replicating human intelligence in robots is the aim of artificial intelligence or AI. It endows systems with the ability to reason, perceive, and make judgment calls. The goal of AI technology is to create a problem-solving method that will allow the software to manage operations without the need for manual programming.

Whereas a subset of artificial intelligence called machine learning enables machines to learn and perform a variety of jobs. AI systems use ML technology to interpret learning processes and obtain insights.

Using Machine Learning and Artificial Intelligence in Test Automation

Using testing technologies, test automation minimizes the need for manual testing and the labor-intensive process of application testing. To get the results, developers write test scripts with the appropriate input variables and execute them. In this case, the only manual work needed is to specify the test instance using a programming script; the tool will take care of the remainder of the testing process.

Yet, when testing applications are modified or upgraded, test automation necessitates continual monitoring. ML and AI enter the picture to solve this problem. A concept known as artificial intelligence involves the development of machines that can carry out human jobs. A kind of artificial intelligence called machine learning enables computers to learn on their own from data.

Manual testers gain from machine learning and artificial intelligence models because they make their jobs easier. They help with error detection, test case generation, updating automatically, and enhancing the scope of the current code. As a result, it helps organizations produce more work of higher quality and quantity in less time.

With test automation, machine learning ensures that testers can run every change with every potential input through quality assurance. It would take a lot of time and money to do this using manual testing or even test automation.

Ways Machine Learning Helps with Test Automation

For development teams, maintaining and extending test automation is a challenge. This is because components that are either changeable by definition or modified by developers typically have an impact on the testing consistency of mobile and web applications.

The stability of the testing could also be compromised by changes to the data or by updates to the application. Additionally, because non-ML testing scripts are static, they cannot quickly adapt to and get around the modifications noted above. This lack of flexibility leads to test failures, erratic checks, build errors, incorrect test data, and other problems.

Development teams may use machine learning while creating and running test scripts. They are employed in the post-execution test evaluation stage, which entails studying data, trends, and organizational effects.

At each stage of the test automation process, there are a few ways that machine learning could assist development teams. For instance, it helps with handling large amounts of test data, determining exact quality ratings, and enhancing test accuracy by keeping the scripts up to date whenever a new version of the application is provided for testing or new features are added.

However, there are some guidelines to keep in mind while creating AI/ML-powered automation. Here we will go through some important factors to take into account when using artificial intelligence and machine learning to automate tests.

Factors to take into account for successful ML/AI application in test automation

The combination of machine learning and artificial intelligence in test automation improves testing efficiency by developing a robust and dependable application testing method. Using these technologies makes it possible to release bug-free applications with little resource usage. But, before incorporating them, it is essential to consider a few factors.

Automation of user interface testing

Application developers perform visual testing as part of their quality control procedures. They assess if the application functions and appears as intended for the user. Manually testing website graphics is typically exciting, but the human eye may still miss out on some errors on the page. Developers may eliminate manual testing and immediately find visual issues by implementing a quick machine-learning test. To identify and validate User Interface, ML uses picture identification technology and delivers quick, precise results.

Unit Testing

By consuming less time and resources for the creation, management, and execution of test cases, the use of machine learning in test automation for unit tests can help the testing process become more effective and efficient. Later in the life cycle of an application, writing and maintaining AI-based unit test scripts is also beneficial.

Testing API

Software testing of the Application Programming Interface (API) facilitates data flow and communication between two systems. The benefit of API testing is that it is more accurate than UI testing at identifying application flaws.

However, testing at the API level necessitates developing test cases and scenarios as well as a greater grasp of how the API operates. In addition, it requires testers with a greater level of technical proficiency, and in-depth familiarity with various application interfaces, and tools to achieve full test coverage.

Hence, using machine learning and artificial intelligence in test automation enables the recording of API actions and traffic for analysis and test creation. Moreover, machine learning can be utilized in API traffic management to spot requests that take longer than expected to complete and take remedial action.

Several test scripts

When there have been updates, alterations, or changes to the code, it might be challenging for testers to estimate how many tests are necessary. Artificial intelligence and machine learning-based automated testing systems can determine if an application needs multiple tests or not.

This helps to save time and money by avoiding the execution of ineffective test cases. Also, it is practical to evaluate a system’s overall performance without repeating the test scripts.

Test data creation based on AI and ML

Datasets are used by AI algorithms. Similarly, test scripts also need input data to function. Machine learning techniques are used in test automation to create data sets that resemble human profile pictures and details like age and weight.

The data is based on trained ML models that were acquired from production datasets already in use. These datasets can be used for application testing because they closely resemble production data.

Regression Testing with RPA

Regression testing can be automated using RPA (Robotic Process Automation). It looks over the screen, uses the application’s functions, locates, and gathers data. All tasks are automated, operated through web or mobile applications, and are only carried out by robots.

It can automate time-consuming, repetitive operations like test case execution and data entry. It may aid in requiring less time and resources for regression testing. Additionally, it enables codeless testing, boosts efficiency, and delivers accurate results. One can get correct findings faster by using AI in test automation. When AI handles the majority of the testing process, time is saved as a result.

Creating more reliable automated tests

Many times tests frequently fail because application developers make modifications. The tools can automatically adapt to these changes when machine learning is used with them. This improves the reliability and maintenance of tests. Once such a tool becomes familiar with and begins to notice how the application evolves, it may decide on its own, at runtime, which locators to use to identify elements.

Optimizing AI/ML Powered Automation Testing using LambdaTest

Nowadays, testers need to use highly advanced methods to guarantee the quality of the applications provided at the rate of Agile/Continuous Delivery. The most effective way for testing in a continuous delivery cycle is without a doubt automation testing. Furthermore, automation testing can handle rapidly altering application demand.

However, employing basic automation testing to provide acceptable test coverage in a short amount of time to address the changing application needs of the expanding number of devices is difficult to achieve.

As a result, by leveraging cloud platform testing it becomes easier for testers to scale up testing from local environments and remove environment-related schedule delays.

One such cloud-based automated testing platform is LambdaTest, which employs Machine Learning and Artificial Intelligence to accelerate the creation, and maintenance of automated tests. Users can run automated tests using a variety of languages and frameworks using LambdaTest to test AI systems like chatbots.

LambdaTest is a secure, reliable cross-browser compatibility testing platform. With this platform, users can perform manual and automated testing for both mobile and web applications on a robust, Selenium Grid of more than 3000 real devices, browsers, and operating system combinations. Because of this grid’s parallel testing capabilities, QAs may speed up testing.

Furthermore, by testing in actual user environments, testers can make sure that the website or app is free of issues that can harm the user experience. This platform enables developers and testers to construct and run automated functional UI tests for online and mobile web apps quickly and at scale.

The ability to build reliable, codeless, and scriptless automated tests is among its benefits. Moreover, testing infrastructure is fully maintained in the cloud, tests can be scaled infinitely, and they can all be run simultaneously. In addition, it offers features like increased test coverage, less code maintenance, quick debugging, cross-browser, cross-device testing, responsive design testing, etc.

Conclusion

In conclusion, it can be said that Artificial Intelligence (AI) and Machine Learning (ML) are the next steps of automation as testing progresses toward higher automation.  Using these two approaches with automation testing can significantly improve its ability to interact with an organization’s logic, boost the quality of the applications, and accelerate delivery.

The testers can utilize AI and ML to examine and enhance the automated testing process with little to no human involvement. It gives testers the ability to automate test cases, improving accuracy and efficiency.