How AI Can Be Leveraged In All Aspects Of Testing

How AI Can Be Leveraged In All Aspects Of Testing

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  How AI CanBe Leveraged In All Aspects Of QA Testing
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  QA has becomean essential practice for businesses that are in the digital space. To achieve digital transformation businesses should embrace the latest technologies in their software development process and build a strong data engineering foundation to fuel innovation. In this article, let’s look at how Artificial Intelligence (AI) can be leveraged in various aspects of Quality Assurance (QA) and Quality Engineering (QE), increase speed in software development and help businesses achieve digital transformation.
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  According to thelatest World Quality Report 2018-19 AI is going to be among the biggest trends in QA & testing for the next two to three years, and organizations will need to develop a strategy around it. As per the key findings from the report, ensuring end-user satisfaction in digital and DevOps transformation is the top QA priority. Adopting Artificial Intelligence and automating software testing has become inevitable, specially to get up to speed on the state of QA. AI in testing can be perceived in two ways – leveraging AI in QA activities, and testing AI-based applications or products. In this article we will see the first type of application in detail. Whether it’s applying AI to testing, or integrating AI with existing systems, APIs and legacy applications, there is a range of challenges that need to be overcome.
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  Automate with AI-PoweredTesting 1. AI-based testing for DevOps & Agile teams By integrating AI with your existing Continuous Integration (CI) or Continuous Development (CD) process, you can significantly reduce time-to-market as there is no need for a team to manage the entire testing infrastructure. This in fact helps creating an amazing agile team.
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  2. Write Tests– Faster, Better And Cheaper Developing patterns and test cases to test how an application performs, aka application under test accurately is time taking if it is done by a human. AI can automatically write tests for an application or system by spidering, i.e., collects data, capture screenshots and more. Hence, AI-based testing cut costs and save time.
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  The challenge thatwe commonly see in the software development and testing process is our human inability to fully understand and review the requirements. The intelligent assistants understand software requirements and limitations of complex systems, which would support in better requirements gathering than a human. AI also helps in collecting test requirements based on the latest trends and marketing competitiveness. Example: To develop an eCommerce site, AI can help collect and review requirements based on competition. 3. Requirements Gathering - Better Than The Best Human
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  4. Exploratory TestingMade Easy Since AI is trained on the collective knowledge of all people that work in the team, they help in identifying various scenarios effortlessly. AI not just performs testing, but also used as background tools that capture test data, user behaviours by navigating through an application or system and records default test cases.
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  5. Find SystemErrors And New Patterns Of Failure When it comes to analyzing logs, AI is already here. With AI, data-mining logs for errors and performance, and identifying the root cause of problems is made easy. Each call can have multiple sub-calls, where AI can seamlessly track and identify which part is consuming more time. This could have been quite a challenging task for a human. Al provides an opportunity to restrict unauthorized access.
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  6. Reusing TestCases AI helps in creating well-written test cases and reuse these test cases much faster and better compared to humans. Because the ML-based tool crawls system or an application, collects crucial data by capturing screenshots, measuring load time, analysing basic UI elements and more.
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  7. Faster DecisionMaking In this DevOps world, most test decisions actually take less than a second. It means people need to think faster, better and smarter. Leveraging AI, hundreds of applications can be tested faster.
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  8. UI Regression- Visual UI Testing And Monitoring To make sure the recent code changes have no effect on the existing features is a painful thing. The functionality of an application and user satisfaction plays a crucial role, because for a user the backend API doesn’t matter. What matters the most is the User Connect. Machines can be accurate than humans and analyse outcomes of regression testing most effectively and effortlessly.
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  Conclusion AI is goingto be smarter than human testers. AI can do a various range of testing tasks and cover all aspects of testing much better than humans.