How AI & ML Are Going To Impact the Future of Software (1).pdf
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When it comes to utilizing AI in software testing, efficiency, speed of execution and greater accuracy is where AI stands out. In this article, you will get to know how AI and ML are going to impact the future of software testing. Read more https://testingxpertsuk.blogspot.com/2022/07/how-ai-ml-are-going-to-impact-future-of.html
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How AI & ML Are Going To Impact the Future of Software (1).pdf
- 1. CONCLUSION If you are looking forward to implementing AI testing for your specific project, then do get connected with a top-notch software testing services company that will provide you with tactical testing solutions that are in line with your project specific requirements. To know more about our services please email us at [email protected] www.TestingXperts.com USA | CANADA | UK | NETHERLANDS | SOUTH AFRICA | INDIA | SINGAPORE © 2022 TestingXperts, All Rights Reserved © www.testingxperts.com The platform of Artificial Intelligence (AI) and Machine Learning (ML) is evolving at a rapid pace and is gaining huge momentum in the IT industry. There are already many industries that are getting immensely benefitted through the strategic implementation of AI and ML. When it comes to utilizing AI in software testing, efficiency, speed of execution and greater accuracy is where AI stands out. In this article, you will get to know how AI and ML are going to impact the future of software testing. How AI & ML Are Going To Impact the Future of Software Testing Following are the five factors that will justify how Artificial Intelligence (AI) and Machine Learning (ML) will constructively impact the software testing domain: 1. Improved reliability and accuracy: There will be scenarios where QA engineers will not be able to detect certain bugs. From a QA engineer’s perspective, it is generally accepted, as manual testing may contain a certain amount of errors/defects. But, the brand’s image can be at stake, if a consumer notices these bugs/defects/errors before project stakeholders do. To solve this issue, Machine Learning (ML) can be bought into use. The source analysis can be grasped by training the systems so that the acquired knowledge can be applied in the future. More accurate results can be yielded by using this AI testing technology. Apart from that, the probability of human errors is eradicated, test run time is reduced and every possible defect can be found. QA team need not bother about large amounts of data. More reliable tests can be produced through the AI and ML platforms. 2. Time to market is streamlined: Manual testing is considered to be a time-consuming process and upon that maintaining a speedy test process is considered to be a daunting task. Time-consuming and repetitive tasks can be facilitated by integrating AI/ML platforms and thus automating them. Developers are benefitted, as investigating large data sets and writing test scripts can be managed quickly and effectively. 3. Enhanced test coverage: The overall depth and scope of QA tests are improved through the platform of AI, which in turn leads to a considerable improvement in the software quality. Proper access to data sets, file content, memory etc., is obtained by test automation so that the software’s working status is ascertained to know whether it is working as intended or not. Over a thousand test cases related to each test case that offers coverage can be easily executed by implementing AI testing. If there is a change that has been made in a certain section of the code, then in order to test those changes, a minimum number of tests can precisely be identified by the right AI/ML tools. The current test coverage can be properly inspected by these tools and thus specific areas can be pointed out that are at risk or have less coverage. 4. Visual validation is automated: Image-based testing is one of the popular testing methods used by the AI/ML platform. It uses automated visual validation tools. This testing method is performed to ensure that the UI appears to the user in a correct manner. Through visual testing, users can be certain of the UI attributes such as the right shape, color, position, size etc. AI/ML-based visual validation tools can be leveraged to perform testing in an optimal manner. A simple visual bug in the application can be automatically detected by performing a simple ML test. The visual correctness is also validated. 5. The sheer value of automating test cases: Spidering technique is used by the Machine Learning platform to write automated test cases. The specific AI/ML tool needs to be pointed out to the application and the crawling process will begin automatically. Some useful data such as the page load time, HTML of the page and screenshots are obtained as the tool crawls. The cycle is continuously repeated until enough amounts of data are captured so that the ML models get trained in order to comprehend the patterns in the application. Improved reliability and accuracy Time to market is streamlined Enhanced test coverage Visual validation is automated The sheer value of automating test cases 01 02 03 04 05