The Best 4 Benefits of AI-Powered Intelligent Test Automation
In this blog, we will learn about what is test automation and the 4 advantages of AI in software testing and many more. So let’s get started!
What is Test Automation?
Software Test automation makes usage of technical tools to regulate the implementation of tests and distinguishes the actual outcomes against the expected result. Generally, regression tests, which are repetitious actions, are automated.
Testing Tools not only assists us to conduct regression tests but further helps us to automate data set up creation, product installation, defect logging, GUI interaction, etc. Automation tools are utilized for both Functional and Non-Functional testing. The norms for tool selection are as follows:
For automating any application, the subsequent parameters should be considered:
- Data-driven capabilities
- Debugging and logging capabilities
- Platform independence
- Extensibility & Customizability
- E-mail Notifications
- Version control friendly
- Support unattended test runs
The types of frameworks
Commonly, four test automation frameworks are acquired while automating the applications:
- Data-Driven Automation Framework
- Keyword Driven Automation Framework
- Modular Automation Framework
- Hybrid Automation Framework
As we have learnt what is test automation so now you don’t get tripped up by giving attention to the wrong kind of artificial intelligence (AI) when it comes to testing strategies. It’s tinier about HAL, the living computer from the movie 2001: A Space Odyssey, and further about statistics-based, machine-learning AI.
This other type of AI is already utilized in some testing scenarios. But before glancing at automation testing instances influenced by machine learning, you are required to interpret what machine learning (ML) is. At its essence, ML is a pattern-recognition technology. It uses patterns recognized by your machine learning algorithms to foretell coming trends.
Machine Learning can consume lots of complicated information and discover predictive patterns, and then warn you to those discrepancies. That’s why ML is so significant.
Since the usefulness and efficiency of your testing process are necessary to the success of your product, there’s no wonder that we’re constantly looking for smarter, quicker, useful ways of testing. As the popularity of artificial intelligence rises, more and more testers are understanding its capacity to create cumbersome and time-consuming assignments easier.
Artificial intelligence is arriving, so we should take benefit of its proficiency. Let’s look at the four advantages of AI in software testing and detail.
- Improved accuracy
- Going beyond the limitations of manual testing
- Increase in overall test coverage
- Save time and money that leads to faster time to market
1. Infallible accuracy
Even the extensively experienced testers sometimes make blunders, particularly when they are dealing with repetitive missions. This is the primary reason automation became so prominent. Contrary to humans, AI constantly fulfils the essential tasks exactly as planned, completing the same repetitive tasks successfully, time after time. While AI functions on repetitive tasks, testers can concentrate on building beneficial automation solutions and on experimental activities that only humans can accomplish.
2. Enhanced flexibility
Even the easiest modifications in an application can direct to test faults in automation tools because traditional testing strategies consider a particular selector or path. Accordingly, such testing methods are somewhat formal. Machine learning and AI authorize for a more flexible testing process, understanding relationships between many components of documentation. Such systems can automatically adjust to any changes in actual time, being both flexible and reliable.
3. Increased test coverage overall
AI allows you to improve the scope and depth of tests considerably. It can test the file contents, data tables, internal program states and memory, being able to promptly determine whether or not the program works as planned. AI-powered test automation enables the execution of over a thousand test cases in a single test run, which is not possible through manual testing.
4. Visual validation
Pattern recognition and image recognition allow AI to inspect visual bugs by delivering visual testing of applications and making sure that all the visual components look and function appropriately. AI can identify dynamic UI controls regardless of their size and shape, assessing them on a pixel level.
Even though AI nonetheless cannot perform software testing with no assistance from humans, it is already worthy of enhancing the testing method significantly.
The primary advantage of AI in software testing is that it assumes automation to a new level so that testers aren’t required to handle repetitive tasks anymore. However, it also enhances the flexibility and accuracy of software tests and, through pattern recognition and machine learning, enables computers to accomplish tasks that are consumed to require human function. That leaves us extra time to do the creative, experimental elements of testing.
Software Development Life-cycles
Software Development Life-Cycle (SDLC) is a procedure used by the software industry to design, improve and test high-quality software. The SDLC seeks to generate high-quality software that fulfils or exceeds customer expectations, achieves completion within times and cost estimations. SDLC is the acronym of Software Development Life-Cycles. It is also known as the Software Development Process. Now, let’s look at the role of AI in software testing.
The role of AI in Software Testing
AI in Quality Assurance (QA) does not function by itself. It can’t transfer manual work or be an all-in-one treatment to formerly available testing tools. The major application of AI in software testing right now is to enhance automated testing tools. Prevailing automation software for QA experts assist in automating repetitive test cases, obtain statistics on their work, tech debt, detect bugs, and dependencies, and even create decisions. Nonetheless, it has many constraints. Automated tools need a long configuration like testers and architects require to enhance the tool’s functionality, exhibit wanted scenarios manually, and monitor its performance.
Artificial Intelligence can encompass the accountability of configuring automated tools, supervising their execution, and assuring correct conclusion, thereby providing accurate test automation. It will yet build detailed reports that furnish a huge outlook on the team’s growth from manual testing, likewise to how a person would analyze the method. So, AI can enhance the speed, translucency, and efficiency of automated testing.
AI is about to transform testing in various ways. So you should not panic. You should keep automating and as you have seen, machine learning is not mysterious. AI is already here. So stop worrying and do what you do best i.e. keep automating!!