Introduction

 

 

Test automation has transformed significantly over the years. It has helped QA teams reduce the chances of human error to a great extent. There are plenty of tools available for test automation, but identifying the right automation testing tool has always been a priority for an automation testing plan to be a success. Artificial Intelligence, machine learning and neural networks are the trending topics of discussion in the tech world today. And, AI inevitably makes its place even in the automation testing space. The use of AI testing tools has taken over the burden of handling repetitive tasks, saving hours of work so that the team can utilize its time for performing more complex and critical tasks. It has played a prominent role in catering to the rising need to develop swiftly and smartly. Even though the current practices of continuous testing, DevOps and Agile have kept the software development process in pace, introducing AI has subtly unlocked the true potential of software testing and driven a path to Continuous Test Automation.

 

What is AI automation testing?

 

AI, in simple terms, is the ability of a computer program or a computer to think and learn like a human being, i.e., the capability of a machine to reason itself – learn, modify data and use it in a beneficial way to handle any future scenarios. It applies reasoning and problem solving to automate the testing process. 

Before AI made a place in the testing world, it was just AI. AI automation testing means leveraging Artificial intelligence and Machine learning in the existing software automation testing tools to generate improved results and remove any of the common challenges of software automation testing.

Although AI automation testing has helped hasten the product lifecycle and driven up the organization’s revenue, it is still nascent. It needs improvement to be a standalone solution for automation testing needs.

 

How does AI help in automation testing?

 

 

Artificial intelligence when leveraged in automation testing helps in authorizing, executing and maintaining automation tests. AI automation testing improves the efficiency of the QA processes. AI generates the relevant data for decision-making, detection and correction of bugs ahead of time. It helps improve the overall efficiency. It provides transparency and expedites the automation testing process.

Testers usually have to maintain and modify thousands of test cases. AI automation testing tools handle all of this very smoothly by handling repetitive tasks, generating relevant data needed for decision making, detecting and correcting any issues early on in the development life cycle. AI also helps maintain automation test suites, easing Unit, UI and API testing. Thus, in a nutshell:

 – AI testing tools detect bugs, fix them and correct any errors at early stages. It helps to discover any changes in the applications and accordingly modifies the script using its intelligence, easing the task of maintaining test cases by testers.

 – AI automation tools help improve efficiency and transparency in the processes.

 – The tools also provide quality testing output with a lot more accuracy and speed.

 

Advantages of AI in Software automation testing

Using AI automation testing tools to overcome automation testing challenges

Artificial Intelligence has changed the way machines work by broadening the scope of their problem-solving capabilities. Using AI, machines can now learn, adapt, perform, think and decide like a human. Unlike traditional methodologies, AI-enabled automation testing can help solve complex problems in no time and without much intervention. AI has been transformational in improving testing efficiency and overcoming automation testing challenges. Let’s talk about some common test automation challenges:

 

Limited Expertise: There are open-source tools and inapt test automation tools that require a tester to have moderate testing skills to create an automation test suite, but not every tester has a background with the required programming skills.

 

Continuous Maintenance: The automation test suite has to be regularly updated with product updates and new features. Test maintenance is inevitable even with an evolved automation testing tool. Re-factoring the test cases is a common UI Automation challenge because once created, the test is stable only for a few days and has to be maintained regularly.

 

Test Reporting: A test automation framework should preferably have this feature. Many test automation tools either do not provide the reporting facility (because most amongst them are open-source) or provide minimal information and not complete insights. If you want to add additional reporting features, you need to perform custom programming or opt for external plugins.

 

Scalability: As the test automation suite grows, the automation framework should support a large number of tests, and provide quick test results, parallelly. Also, prioritizing and sorting tests to run on different devices and configurations is needed for a smooth test execution.

Selecting the right AI automation testing tool can tackle the above challenges. Let’s learn more about some of them as we progress. 

 

AI Testing Automation tools for the Future of Automation Testing

Today, there are so many test automation tools that are AI-enabled. Choosing the right tool that suits the purpose is a crucial job of the QA team to get the additional benefits of this new technology. 

 What do most of the AI automation testing tools most commonly do? 

 1. They perform predictive self-healing, i.e., updating the test suites whenever the application evolves. 

 2. Perform Intelligent Bug Hunting, i.e., discovering bugs intelligently through an AI-powered testing mechanism that crawls through the entire application and detects issues and fixes them.  

 3. Enable application resilience by applying predictive auto-scaling and continuous fitness functions

4. Most commonly automate the business processes and workflows for performing end-to-end testing and not just test automation.

Here are the most promising AI automation tools to look forward to:

pCloudy

pCloudy is a ground-breaking, AI-enabled test automation tool that’s revolutionizing the landscape of app testing. Known for its expansive testing capabilities, pCloudy provides an all-encompassing solution for various testing needs.

pCloudy’s standout feature is its Certifaya AI engine. This powerful tool uses AI and predictive analytics to deliver comprehensive reports on mobile application quality. Certifaya integrates seamlessly into the CI/CD pipeline, enabling automated execution of tests on an array of device-browser combinations, accelerating the delivery process significantly.

Certifaya is designed with simplicity at its core. Users can easily upload their app and instruct the platform to run a test. Within minutes, Certifaya generates a detailed report, highlighting critical issues along with screen grabs and videos of the sessions. Users also receive helpful recommendations to quickly resolve these issues.

Certifaya’s bots are specifically designed for running crash test scenarios and performing swift and deep exploratory tests. The bots not only run crash tests covering several installation/un-installation scenarios on multiple devices but also crawl through the app like a human user, collecting a plethora of relevant data.

The result is a comprehensive report on the app’s functionality and performance on multiple devices in a matter of minutes, a major leap from the hours of waiting previously required. This is instrumental in the fast-paced industry where developing high-quality apps quickly is mission-critical.

Additionally, pCloudy features Visual AI capabilities for automated visual testing. This feature enables effective screen comparisons, identification of visual bugs, and assurance of visual consistency across multiple devices and screen sizes.

Its cloud-based architecture allows testers to access devices and perform manual and automated testing anytime, anywhere. Plus, pCloudy provides insights and analytics on device performance, battery consumption, memory usage, and more, helping testers to understand the real-world impact of their applications.

Applitools

• A trusted AI automation tool for visual UI testing and monitoring.
• It is the only tool driven by Visual Artificial Intelligence (Visual AI) that enables a machine to mimic human eyes and brain to recognize functional and regressions. 
• With only a single line of code, the Applitool Eyes analyzes the entire screen of the application.
• It leverages Artificial intelligence and Machine Learning for test maintenance. Its comparison algorithms recognize whether the changes are meaningful or just bugs.
• It integrates smoothly with your existing tests, thus eliminating the step of writing and learning new tests and scaling up testing with existing ones.

 

Testim.io

• Testim is an end-to-end AI testing tool that authors automated tests, executes and maintains them, reducing test creation-execution time by running multiple tests parallely.
• Its focus is mainly on functional testing and UI testing. 
• It overcomes the problems of slow authoring and unstable tests that usually result from frequent changes and releases in the UI. 
• Smart Locators detect the changes in the app to run automatic tests. 
• It integrates seamlessly with CI/CD tools, provides detailed bug reports and performs root-cause analysis of the failed tests for quick remedial action.

 

Mabl

• It is one of the leading AI testing automation tools developed to create and run tests across CI/CD. 
• Mabl’s Google Chrome Extensions helps developers create scriptless tests to create/run tests on Firefox.  
• It is a cloud-based tool and has self-healing and visual testing features.
• Mabl uses ML algorithms to detect any threats or issues and improves the test execution.

 

Parasoft SOAtest

• It is an API and Web Services AI automation tool delivering end-to-end functional API testing, Web UI integration, mobile testing, load testing, performance and API security testing.
• Its intuitive interface automates API, Load, performance, and security related criticalities.
• It delivers constant analysis on the changes and their impact, thus simplifying test maintenance tasks.
• Its test data technology provides realistic test data for further modeling, masking, and generating additional data.
• Execution of Multichannel tests can be easily coordinated directly from the browser to allow continuous testing.

 

TestProject

• This AI testing tool eases testing efforts by removing test setup, maintenance and hassles of looking after drivers, servers, etc.
• It is equipped with a built-in Automation assistant, AI self-healing and adaptive wait features. It has an AI-enabled codeless test recorder that is Selenium API compatible with open-source SDK. 
• It also has an add-ons library, unconventional test reporting, dashboards and integrations to your CI/CD pipelines.

 

AccelQ 

• It is a cloud-based codeless AI testing automation tool. It focuses on automating the Web UI, API, Desktop and Mobile platforms. 
• It has natural English programming, intelligent element explorer, automated test generation and self-healing features. It integrates well with popular DevOps toolchains to provide a unified view of the complete QA lifecycle. 
• It provides Predictive and Path analysis for developing test scenarios, maximizing test coverage model UI and Data flows, open-source alignment are other distinct features of this tool.

 

Functionize

• It belongs to the declarative category of AI testing tools. It is a cloud-based automation testing tool that uses Machine learning and artificial intelligence to create, verify and maintain tests.
• The AI-powered smart agent creates tests quickly, uses Natural language processing to automate English-based procedures.
• Its SmartFix feature detects UI changes and test failures with ease.

 

TestCraft

• Another AI-powered tool for automation testing for regression and continuous testing.
• It uses a Machine learning algorithm that identifies web elements correctly even during the app changes.
• It enables testers to visually create automated Selenium-based tests and run these on multiple browsers and platforms.
• It’s On-the-Fly mode enables the creation of test models out of the test scenario, making it easier to reuse test steps 
• It also has self-healing capabilities.

Comparison of AI Test Automation Tools

 

Tool	Key Features	Best For
Applitools	Visual UI testing and monitoring, leverages AI for test maintenance, single line of code analysis	Visual UI testing
pCloudy	Certifaya AI engine for running crash and exploratory tests, Visual AI for visual testing, cloud-based architecture	Comprehensive app testing on real devices
Testim.io	Automated test authoring and execution, parallel test execution, root-cause analysis, functional and UI testing	Functional and UI testing
Mabl	Scriptless test creation, self-healing and visual testing features, cloud-based	Comprehensive AI testing
Parasoft SOAtest	API and Web Services testing, load testing, performance and API security testing	API testing
TestProject	Removes test setup and maintenance hassles, AI-enabled codeless test recorder, Selenium API compatible	Testing with less setup
AccelQ	Cloud-based codeless testing, natural English programming, intelligent element explorer, automated test generation	Codeless testing
Functionize	Cloud-based testing, uses ML and AI for test creation, verification and maintenance	Declarative AI testing
TestCraft	Regression and continuous testing, self-healing capabilities, visual test creation, Selenium-based tests	Regression and continuous testing

Remember that the best tool often depends on the specific needs and context of the project. Each of these tools offers unique benefits, so it’s important to consider the nature of the testing you need to perform, the skills and experience of your team, the complexity and scale of your application, and your budget when choosing an AI test automation tool.

Challenges and Limitations of AI in Test Automation

While AI and ML have indeed begun to revolutionize the field of test automation, it’s important to recognize that these technologies are not without their challenges. One of the key limitations is the significant learning curve for development and testing teams. Getting to grips with the intricacies of AI requires considerable time and effort, and can initially slow down testing processes.

Another challenge lies in the quality of data. AI models thrive on high-quality, diverse, and extensive data sets. The efficacy of AI-powered testing tools may diminish if the quality or the quantity of the data isn’t up to the mark.

Also, AI models can sometimes introduce biases in testing outcomes, especially if the training data is skewed. This could lead to overlooked bugs or errors in certain scenarios. Therefore, it’s important for testers to be vigilant about potential AI biases and take steps to mitigate them.

The Impact of AI on the Role of Testers

The rise of AI in test automation doesn’t spell the end for human testers. Rather, it highlights the need for testers to evolve their skillsets in line with technological advancements. Testers will still be needed to create test scenarios, analyze complex results, explore edge cases, and validate AI outcomes.

However, the focus will shift towards understanding AI and machine learning algorithms, data science fundamentals, and how AI can be effectively incorporated into testing strategies. The role of a tester will move beyond just identifying bugs to creating an environment where AI can help improve the overall software quality.

CONCLUSION

As we forge ahead into the digital age, AI and machine learning are taking center stage, pushing the boundaries of what’s possible in numerous fields, including software testing. AI-enabled test automation tools are becoming increasingly sophisticated, providing significant benefits such as improved efficiency, reduced errors, and faster testing cycles.

However, while AI has immense potential, it’s crucial to remember that it’s not a silver bullet. These technologies come with their own set of challenges, from the steep learning curve associated with their use, to the quality and bias in data, among others. Moreover, the integration of AI into testing practices will require testers to enhance their skills and adapt to new roles.

In the future, we can anticipate further advancements in AI test automation, including the rise of continuous testing, predictive analytics, and AI-led performance testing. The choice of an AI test automation tool should be based on an organization’s unique needs and resources. And as AI continues to evolve, ethical considerations, especially concerning data privacy and algorithmic fairness, will become increasingly important.

In conclusion, AI is set to revolutionize test automation, driving increased productivity and quality. However, this transformative journey needs to be navigated wisely, keeping in mind the challenges, opportunities, and ethical implications. As with any technological evolution, the key to success lies in embracing change, continuous learning, and adaptability.