The constantly growing need for technology, the evolution of the web, and the ever-improving data services have all called for digital transformation. Therefore, it is quite natural to see technology thriving in almost every industry with operations being led through software and applications.
However, the surge in demand for technology that can create the revolution made it difficult for developers and development companies to foster the need for quality when the user demands rapid deployment.
Here comes the role of software testing and quality assurance services.
From driving high-performing software solutions to advancing web solutions that can elevate direct interactions with consumers, Quality assurance has streamlined everything. Especially, when quality assurance services have the potential to nurture faster deployments, quality deliveries, reduced time to market, and of course cost-effectiveness, it is quite natural to witness software testing and QA services thriving.
According to statistics, “the Software Quality Assurance Market is expected to register a CAGR of 9.6% during the forecast period to reach USD 14.01 billion by 2025. Technologies like AI, ML, Automation, Agile, and DevOps are some significant factors that have helped improve the QA operations for testers making it a more approachable process to integrate with the high-demand software development market.
No doubt, the advanced testing practices like continuous testing and shift-left approach have added to the development pace, and bringing AI into software testing could help reinvent the entire Quality Assurance process.
In this blog, we will aim at highlighting the role of AI in test automation by shedding light on the most important benefits of AI in Test Automation. Let’s begin.
AI & Test Automation: Applications & Benefits
Since the software testing industry is quickly progressing toward automation testing solutions to overcome the intricacy of the software development lifecycle, Artificial Intelligence is one such solution that can help smarter testing.
Moreover, the studies suggest that “AI could contribute up to $15.7 trillion to the global economy by 2030.” Let us quickly jump on exploring all the applications and benefits that AI could bring to Test Automation.
Testing UI With AI
Since UI is a highly significant part of the test process for releasing software or applications, machine learning can be leveraged to upgrade the UI testing process. AI paired with Machine Learning could help document the response on UI controls taking UI to next level. Moreover, AI can even be used to analyze the user interface to identify any complexities in the graphics or pixel-related issues that might hamper the ultimate output.
Flexibility In Automated Testing
Until the application lands on the market, it often goes through an extensive list of changes. However, the inability to work on these changes could result in test failures which often come out from testing focused on an inflexible approach to testing. AI testing could help testers to identify associations between different segments and functionalities of the product under test.
Moreover, AI can help in tracking real-time changes in the software or application with automated test scripts that follow the changes. Ultimately, AI in Automated testing can help yield consistent and sustainable test results.
Automated Visual Validation
Another significant application of AI in software testing can be bringing machine learning to track visual bugs in software. It can be worked by using the pattern recognition capabilities of AI and ensuring visual validation. Also, visual testing through AI can be used to verify the potential of the user interface on a product for precisely meeting the end-users expectations.
Wondering how Artificial Intelligence Could Help Improve Quality of Assurance Industry?
Read Here: Improving Quality Assurance Using Artificial Intelligence
Machine learning could help testers to ensure that all the visual components of the software must not overlap. Also, AI can effectively track the most minute yet highly possible visual errors on the application bringing accuracy and rapidity to the visual validation process.
Writing Test Cases
Artificial Intelligence along with Machine Learning could help develop test cases by crawling through requirements, past test data, and data collected through manual tests. The data can be used to train the algorithms on ideal output while detecting any deviations that may appear with a new run or change made to the software/application.
API Test Generation
APIs are meant to simplify the interactions between databases, servers, and protocols in order to improve communication between two parties and offer a stable connection on requests generated.
AI for Automating the test of APIs can work by generating unique test cases for the software under test. Especially, when you have software that caters to dynamic functionalities with the help of third-party tools, AI can easily evaluate the functionalities for critical results. Also, AI can even help software testing service providers to establish relationships between different APIs to improve their test data and ensure better outcomes. In addition to that, the information fetched can work as an avenue for new tests based on unique and different test scenarios.
When frequent changes to code are made, it usually breaks the test cycle. It needs testers to revise and rerun the whole test suite for changes that are not traceable. Also, identifying any such changes need a continuous check on the evaluations made and therefore to fix the issues with the code.
However, the inability to take appropriate steps and efforts required to make appropriate fixes brings a lot of data through continuous integrations and testing. Since it is time-consuming to track all the common patterns, AI could be used here to aid developers as well as testers.
By using AI-based tools that can monitor tests, testers could identify the best UI elements that must be added to the modified codes while skipping anything average. Also, machine learning technology can be used to work on test cases that can work on modified code and create a better version of the final product. All in all, AI can be used to supplement gaps in the tests to ensure software or application success.
Automation testing solutions are a highly critical part of modern-day software development. From early error tracking to reducing the costs and simplifying the launch process, automation allows faster error correction to meet the defined requirements.
In a nutshell, automation drives all the efficiency to the software testing process which is not possible to achieve with manual efforts only. Though it helps yield highly reliable and quick results, technologies like Artificial Intelligence could help take it even further.
Developers can simply aim to execute test cases with wider applications and machine learning could be used to generate more reliable results on the existing AI-based test strategies. Overall, AI and test automation can both help the quality assurance industry to grow a long way while rendering innovation and growth to the digital landscape.
Working on software or application? Our experts at BugRaptors can help you with all the expertise you need to reinforce the quality of your products. For more information, reach us at firstname.lastname@example.org