Unleash the power of Artificial Intelligence in Automation Testing

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AI automation testing: We live in the age of disruption. Our daily lives have changed. Industries are getting transformed. From large to small businesses, AI is being used widely across the world. Let’s look at the key statistics about Artificial Intelligence. 

artificial intelligence growing market size

“Artificial Intelligence” and “Software Testing”

As the usage of AI has hit our day-to-day life, its impact on the software industry is not far away. And AI automation testing is one of the hottest trends of the industry.  If you remember the days when testers used to take several hours to manually test the software but still, it could not be tested comprehensively. 

Thanks to test automation that allows conducting software testing with reduced manual effort and high-quality results. AI continues to permeate the modern world, it has become more crucial to validate that the systems are safe, secure, resilient, and functional. Fortunately, the application of artificial intelligence in software testing is focused on reducing mundane and tedious tasks in the software development lifecycle. 

You must be wondering that test automation tools already do it. And the answer is – “Yes! They do” but their usage is limited. A few decades ago, manual testing ruled the testing world but today, AI automation testing has made a substantial contribution in the testing environment. Let’s understand more about AI-driven testing.

Before we dive into the details, here is everything you need to know about AI-based testing, approaches and tools of AI automation testing, AI-based software testing case studies, and more.

What is AI-based Testing?

AI-based testing is a technique of software testing using AI and machine learning algorithms. The objective of this technique is to make the testing process more effective and smarter. With the inclusion of artificial intelligence in testing, innovative problem-solving and logical reasoning can be used to improve the overall process of testing.

AI in software testing benefits

Need of Artificial Intelligence in Software Testing

Often, software developers cannot do exhaustive testing of the application due to a lack of time and resources. For the same reason, there is a dire need for AI-based software testing that can automate the processes efficiently instead of human testers that unnecessarily inflates effort and costs. The smart algorithms of AI automation testing help testers in funding the maximum number of bugs in less time and will make the application more accurate and reliable. Moreover, AI-based testing is tremendously helpful in safeguarding the software breakdowns while making more productive and smarter software for the end-users.

Use cases of Artificial Intelligence in software testing

On the bright side, testers have recognized the potential of Artificial Intelligence to bridge the gap between human and machine-driven testing capabilities. Here are some examples of how AI is used in the software testing process.

Detection of mobile app crashes

There are many frameworks that are used to index the software automatically after every crash so that the testers can focus on more human-centric tests. It allows the test engineers to accomplish more crucial tests like automating the detection of memory leaks (iOS) or troubleshooting the JNI integration (Android).

Review of static programs

Symbolic Artificial Intelligence is a smart decision-making tactic that employs rules. For example, Deepcode.ai leverages symbolic AI to review the software code that does not adhere to best coding practices but also extracts the potential attack vectors within the code.

Leveraging the user traffic to analyze the test coverage and automated test generation

Not every test can be assigned with equal weight. Some tests are more likely to result in user error than others. AI automation testing allows to assemble click data, and use the data analytics to determine which key user flows need to be examined. It automatically creates the test code to produce a comprehensive test suite.

Leveraging AI to decide whether to conduct a recall in case of faulty test implementation

A key issue with Continuous Development (CD) is related to the rolling back of deployment after the identification of any mistake. Harness.io helps in solving this issue by analyzing the observability data from application performance management tools such as New Relics, Sumo Logic to determine the need for rollback.

Monitoring and forecasting

System observability is one of the most common needs in the testing environment. The primary application in this context is log aggregation and analysis. It enables users to configure standard machine learning algorithms such as logistics and linear regression, and so on to Splunk the data. The Machine Learning Toolkit smart assistants make this process even more simple.

Searching errors

Perfection does not exist and cannot be attained, and this is why the developers and tests can write erroneous test codes. The flaky test codes have a direct impact on quality assurance and kill a lot of time. Mostly, the process of identifying the error code placement could be onerous. AI automation testing helps in examining the codes in real-time, identifying the errors, and making corrections in specific scenarios.

Approaches of AI Automation testing

Context-driven testing

It is a context-based model rather than going by the method of testing or fixed best practices. It allows testers to choose their techniques, objectives, documentation, and deliverables by looking into the details of the particular situation. It is not just a technique but a set of values related to the test methodology. So, no thumb rule applies to all cases when it comes to context-driven testing.

Model-based testing

It means using models for describing the test strategies, environment, generating test cases, executing tests, and evaluating the overall design quality. This approach ensures the possibility to trace the relationship between models, requirements, test cases, and codes used for the tested system. The models are utilized to generate the test cases automatically and represent the expected behavior of the system under test.

Linear scripting

This approach is based on the record and playback testing process which always leads to linear results. In this script-driven framework, the test scripts are created and executed individually to perform the tests. The automation scripting is also conducted in an incremental manner where the new interactions are added to the automation tests.

Modular testing

This framework is built on modules. The independent test scripts are developed based on modules to test the software. The abstraction layer is built specifically for the components and ensures that the changes made to a part of the application do not affect the other related components.

Data-driven testing

In this framework, an individual file is used to store the input and expected output. A single driver script executes all test cases with multiple data sets. This script includes navigation to spread through the program and covers the reading of data files and information about the logging of test status.

Keyword-driven testing

It is an application-independent framework and utilizes the keywords and tables to explain the actions to be performed within the testing environment. This approach can be considered as an extension of the data-driven testing framework.

Behavior-driven development (BDD)

In this method, the tests are more focused on the behavior of the system. The testers can easily create the test cases in the English language. It helps the non-technical users to easily understand and analyze the test cases.

AI Test automation tools

While the first wave of testing was filled with manual testing methods, Selenium began the second wave of test automation in the world of software testing. Now, the current buzz is around AI automated testing. Businesses are rushing to use AI-based testing tools and taking the move to the next wave of test automation. Here are a few of the new wave automation tools that are leveraging AI technology.

AI tool types for test automation

Differential tools

Launchable

The tool is based on a machine-learning algorithm and predicts the likelihood of the failure of each test. The tool enables the user to record the test suite so that the tests that are likely to fail can be identified.

Google OSS-Fuzz

This tool combines the modern techniques of fuzzing with distributed and scalable execution. The tool is well-supported by Rust, C/C++, and Python.

Visual AI testing tools

Applitools

It is an AI-based visual testing tool. Applitools is a next-generation platform for test automation powered by visual AI. The grid helps in cross-device and cross-browser testing to accelerate visual and functional testing by 30 times.

Percy by BrowserStack

It helps in high-speed and cross-browser rendering along with parallelization capabilities. BrowserStack is typically used to capture screenshots and compare them against the display visual changes and baselines.

Declarative tools

Tricentis

This tool combines different aspects of software testing like the design of test cases, test automation, generation of test data, and analytics to test APIs and graphic user interfaces from business perspectives.

UiPath Test Suite

This tool is used to automate tests and helps in managing and executing the tests. This tool is helping businesses with 360-degree testing and helping robotic process automation developers to build and test better.

Self-healing tools

Mabl

It is one of the leading AI automation testing platforms built for continuous delivery and continuous integration. It crawls the application screens and runs the default tests while using machine learning algorithms to detect defects and improve test execution.

Testim

This tool utilizes machine learning and artificial intelligence algorithms to automate testing. The AI automation testing helps in the authoring, execution, and maintenance of the tests rapidly. The tools also include the self-maintenance of machine learning-based tests.

The Future of AI in Software Testing

The use of AI in software testing is not a new concept anymore. Initially, it started with visual testing of user interfaces, auto-correction of element selectors, locating the UI elements, and alike. AI has continued to evolve by enhancing the existing tools and frameworks of the testing environment. As technology progresses and machines are getting trained on higher-order tasks, AI is likely to take over all the activities and tackle the contextual problems smartly. Let’s see how AI testing is progressing into the next decade.

AI in software testing projection

With the progress of AI automation testing, the fact remains that mimicking the human brain is not an easy task. After all, humans are the users of technological innovations and for the same reason, human understanding and creativity are required to ensure the best product quality. Manual testing remains essential in the future, but it is more likely to be complemented with automation and AI. Rather than AI replacing the human testers, AI can augment the software testing practices and infuse human testers with the efficiency of superhumans. So, the leaders and innovators in the industry will continue to discover and dissolve new boundaries with machine learning and AI. Now is your turn to take the test automation strategy to the next phase. Whether you are just beginning automating tests or reaching continuous testing, QAonCloud can help you with end-to-end test automation. Don’t wait anymore to step into the future of the software testing world.

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