With digital transformation emerging to be one of the integral parts of our lives in the 21st century, businesses somehow are employing an ample level of engineering in its systematic process to cope up with the revolutionary evolution. Be it incorporating CRM or utilizing the latest technological tools to make the business functions as smooth as possible, the enterprises are putting everything to the test they find capable to streamline the operations. The advantages of implementing technology to various verticals are not masked to many business owners. On top of all, the newly-discovered intuitive technologies are proving to be beneficial for the companies in several possible ways.
There are various reasons why the majority of enterprises focus on investing their time in deploying ultramodern tools in operations that could transform the workplace profoundly. Some do it to gain a competitive advantage over others while many set sights on improving productivity and establishing sanguinity among employees. Since various groundbreaking technologies like AI, ML, software unification, automation, etc. are gaining traction among business enthusiasts, enterprises in 2021 are zooming in to utilize such technologies in a very penetrating manner. For eg, employing AI in quality assurance, creating a marketing plan by using avant-garde solutions, etc.
Artificial intelligence is one tech-friendly invention that is revolutionizing the world with its immense benefits and potential. Ever since businesses have discovered the dynamic prospects of AI, they are lining up to integrate intelligently advanced tools for different business uprights in order to simplify actions and reduce costs. The implementation of artificial intelligence in quality control is one such endeavor companies have chosen to carry on with to cater to challenges developers and testing departments face more often.
In this article, we will learn how AI, being an undeniable reality of this century, is transforming the quality management system in an informed manner making developers’ and testers’ jobs undemanding.
What Is AI and How It Can Be Utilized in QA Automation?
When we stumble on the term AI, we, oftentimes, form a picture of the smart labs where robots are taking care of most of the things by keeping humans at bay. Since AI is a branch of computer science that does something smart, it can be defined as the machine simulation of human intelligence that is programmed to employ human-like experience and techniques to perform tasks.
Artificial intelligence is one of the widely popular technologies that is discussed in the technology and business ecosystem time and again. Flying drones with 3D model sensors, virtual assistant, chatbot development with behavioral supports are some common yet great examples of AI.
While being qualitative while performing any of the tasks is one of the essential areas companies feel responsible about, the incorporation of AI in quality assurance and the software development division, allows businesses to speed up the measures and reduce the potential of error. The practice of using AI for quality management system is perceivable to many enterprises because of the newly-adopted contrivance the technology follows. The exemplification of AI and machine learning in software testing and allied areas has made the process easier and faster. AI has enabled testers and developers to run QA strategies effectively, let enhanced accuracy be alone.
The Significant Role of AI in Software Quality Assurance
AI technology has been a subject of debate ever since the term is first coined by John McCarthy, an extremely talented computer scientist. Not mainly about how this inventive technology is beneficial for different industries but what potential it holds to revolutionize the business operations beyond expectations. By mimicking human-endeavors, AI can predict customer behavior, detect fraud, and help in personalized marketing. Now the new set of circumstances allow business owners to yield the benefits of AI in quality engineering as well.
With AI being the fascinating box of mysteries, let’s first discuss some path-breaking roles of AI in quality assurance and how it can change the way organizations operate before giving ears to something else.
Abolishes Test Coverage Overlaps
Over the years, quality assurance has come into view as a subject in which enterprises are getting themselves involved more. While AI in quality assurance makes certain of predicting software performance, it also enables developers to meet the demand of finishing testing on time and deploy the software asap. This is why enterprises contemplate integrating AI-based technological tools that could eliminate the code coverage density and overlap of testing effortlessly.
Optimizes Test Automation
AI as a service and testing have become an integral part of the business ecosystem in order to meet the critical elements of quality assurance. Coming of age enterprises believe that manual testing is on the verge of extinction. In a survey conducted among the developers and coders, more than 90% of the respondents revealed that they use new-age testing tools and automation to support testing efforts. The assimilation of AI in QA testing enhances the process of automated test script creation which results in effective software testing.
Improves Agility and Predictability
Enterprises are realizing the importance of agility in business and how essential it is to implement reactive machines and solutions in almost every department sooner than later. The world is now leaning towards adapting contemporary tools to boost the basic quality engineering in business functions. In contrast, the ultramodern companies are eyeing to involve a high-level product inspection that is why they want to bring AI in quality assurance. The embodiment of artificial intelligence also gives a remarkable lead to predictability in software testing and deployment.
Automates Bug Reporting or Change in Code
The intuitive solutions that are restated with AI technology have a great effect on the bug tracking process. While artificial intelligence has a path-breaking impact on many notable industries, software testing companies are the ones reaping the fruits to the core by utilizing smart machines powerfully. Either it is about finding errors in code, changing the code in between according to the requirement, or getting a hold of the entire software development lifecycle, artificial intelligence quality inspection is immensely helping companies to perform qualitative testing.
Read About – Top DevOps Trends for the Year 2021
Six Stages of AI-Based Testing for Improvised Quality Engineering
We cannot deny the fact that how revolutionary and groundbreaking AI technology is proving for different types of business models. There are plenty of applications and tools available in the market which are developed keeping the AI technology on the footing. According to the current business needs, quality assurance is now being leveraged by AI to solve various complexities in the testing process. Since AI testing is gaining ground, there are a total of 6 levels in which AI testing trial is performed by a bunch of coders. Although, it is important to know that the stages of quality assurance are way different than AI testing.
- No automation – This is the earliest level of testing conducted via artificial intelligence. This is the stage where developers write codes and check if anything needs to be added or repeated for comprehensive functionality.
- Testing framework – In this stage, the developers do not have a much significant role to play but testers do have. The framework which is created by developers, keeping include programs and code libraries in mind, needs to be put to test via an autonomous QA process with the assimilation of AI in quality assurance software testing tools.
- Sectioned automation – This is the initiation of partial automation in which the testers are involved in performing AI tests against a baseline. This is the stage where the role of QA in AI is well-judged by carrying out trials on softwares to check for bugs or any other error.
- Conditional automation – This is the type of testing that is often conducted when the software is prone to impact by potential code error. The conditional automation is inducted by the tester to discover the coverage of possible outcomes of automation.
- High-level automation – Till now every step was followed by a little or medium human intervention, but from this stage, the quality testing will be driven automatically with the help of AI-implemented software. At this level, the tools understand the framework semantically and carry on easily with the flow of communication.
- Complete automation – This is often termed as a thought-provoking stage for coders and testers. By far this level, the AI involves itself with the product directly and runs all the quality assurance checks without a single human intervention. Hence, the entire testing is performed by AI in software quality assurance matters.
Read About – Why Should IT Companies Switch to Automation Testing
Benefits of AI in Quality Assurance: 5 Essential Means to Look Into
No one could have thought of driving quality assurance through the implementation of smart machines a decade ago. But now this concept has become the truth of most prominent companies in the world as many enterprises like you are looking forward to invest in AI application development and incorporating tools with machine learning theory. Since AI is growing across industries, the development and deployment process of the software is also witnessing a significant rise. Using AI implemented solutions and tools for QA usher businesses in to notice many unraveled paths such as improved quality inspection. Before hopping to the advantages, let’s have a look at how AI software solutions have been beneficial for businesses in generating revenues with the help of graphical representation below.
Let’s have a look at the advantages AI brings to quality assurance setting and how essential the role of QA in AI is.
Reduces the Time Spent on Manually Testing
With AI setting foot in different stages that took place between software development to deployment, the evolution of QA testing has been programmed by companies to know the power of transformation. While many enterprises operate with the mindset of – not everything can be automated, AI to most of the segments has put an end to manual testing and problems arose due to testing done with a little or more human intervention. Many software development companies have noticed the surge in demand for AI integrated solutions which is allowing enterprises to have ready-to-use softwares.
Useful in Regression Testing Tasks
AI testing softwares are substantiating to be a boon for testers who are in dire need of updating existing softwares with little changes in the framework and make it perform with the same perspective with improved functionality. While the regression testing cycle is one of the time-consuming processes if undertaken to be done manually, AI quality assurance with an accelerated process enables testers to yield the benefits of automation and ease the burden of QAs. AI testing tools ensure that new changes in codes do not affect the existing functionality of the software.
Easy to Determine Testing Failures
The market is swamped with the best artificial intelligence android apps and there are solutions developed with the integration of computer science technology. The reason why the majority of the enterprises have leaned to adopt machine learning in order to run testing and detect bugs is that the AI testing models splendidly predict how tests behave on changes in the code early. A test-failure means that an application has a broken path or functionality that needs to be rooted as early as possible. The AI and ML technology-driven tools enable organizations to understand actual reasons to test failures.
Enhancement of the Software Development Process
SQA which stands for software quality assurance incorporates all the methods that enrich the software from defining requirements to writing codes and deployment. Software development is a huge process that takes place in stages. Since AI in quality assurance is insightful for software professionals to provide them with accurate quality assurance, it also plays a major role in the development process such as automating the code writing, implementing scalable workflow, and makes dozens of tasks easier for developers and coders.
Quickly Gather Relevant Test Requirements
With the deployment of AI in QA testing phase, the testing experts aim to make the process smarter and more efficient than it ever has been. The solutions fashioned with artificial intelligence focus on streamlining the testing automaton and bring innovative suggestions regarding the software to the table. AI-driven testing is proving to be beneficial for automating activities and making use of different types of testing as per the requirements. There are many automatic processes closer to artificial intelligence in quality control inspection that makes the relative architectures of the sophisticated softwares potentially powerful.
Three Widely Popular AI Testing Softwares
Ever since AI-powered tools have gained access to the market, the gleam of testing solutions like Selenium has doubled as there are plenty of tools in the market which is built upon the Selenium open-source with the assimilation of AI. Since AI is playing a vital role in transforming the digital space as well as various business verticals, AI in quality assurance, and the tools that are being used widely have also set the heat high. Let’s have a look at three AI-driven testing tools that are gaining traction in the industry.
TestCraft is a test automation tool built upon the Selenium open-source platform. It is a codeless test automation platform that is leveraged by the unique visual modeling method. With the help of TestCraft, the testing professionals can create automated test frameworks with no programming skills and codes.
Applitools is a next-gen test automation platform powered by artificial intelligence. It is referred to as the world’s most intelligent test automation tool whose cross-device testing accelerates functionality and contributes to seamless integration. From providing end-to-end software testing and monitoring, the algorithms of Applitools are adaptive in nature.
Sealights is a cloud-based testing platform created with the vision to provide effective maintenance of automated tests. It benefits the QA professionals to speed up the testing process and identify the scope of risk to overcome the same within time.
Clearly, AI has become much more mainstream as compared to what its significance was a decade ago. Since AI is corroborating to be a windfall of the coming-age technologies, AI in quality assurance is also gathering a lot of interest lately. While many enterprises have already started investing in AI-driven testing tools to proliferate automation testing, some are still giving this approach a second thought. Nothing could be better than for a business that can seemingly reduce human efforts. Hence, letting smart machines do the job would be an intelligent decision.
Stephen Hawking, the world-famous physicist, and author once quoted – Intelligence is the ability to adapt to change. And AI is exactly giving you the opportunity to transform the quality testing to the core.
If you are looking forward to gaining significant knowledge about how AI-driven testing tools can be customized as per the requirements, get in touch with the skilled software developers of Appventurez who can help you build AI testing solutions and allied projects within the stipulated time.
Co-Founder and AVP Technology at Appventurez Mobitech. A tech enthusiast who has broad expertise in delivering end to end software solutions. He is an expert technocrat ho has in-depth knowledge and is highly experienced in delivering solutions for Android, Xamarin, Ethereum Smart Contracts, ASP.net.
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