In intelligent test automation, AI algorithms will help in automatic script generation, prediction of bugs and make changes for improving coverage. It is a matter of fact that all these parts of software testing take a lot of time and effort. In
intelligent test automation, the entire process of working with the code is likely to change. As a result,the intelligent test automation system changes the testing environment altogether.
Why do we need AI in Test Automation
The organizations today are moving towards being digital very quickly which is indeed essential. However, it is not so easy to achieve. Along with that, the advent of Cloud, DevOps, Internet of Things (IoT), etc. have made it complex to work with automation testing.
As a result, the software and apps needs to be improved continuously and quickly. This makes the task of software testing even more complex.
Interestingly,to tackle the complex and fragmented software testing process, intelligent test automation can help hugely. As the process of manual testing is cumbersome and time-consuming,it is necessary for us to go digital.
AI offers many new tools and technologies like analytics and machine learning. These help in carrying out the actual test automation. Besides,AI tools provide guidelines and recommendations for carrying out the tests while learning and improving the software development phases. It help to predict the business needs and results in assisting the coding teams for working efficiently.
Features of Intelligent Test Automation
-Essentially, for testing software or app, understanding the perspective of a user is important. An intelligent test automation system is the best solution for this. The system include tools to access the application like a user and test the application.
Besides,this automation testing uses the app after taking control of the app on a physical machine. The testing also tries on the workflows. Besides, these tools also collect analytics with this mechanism.
AI-based Test automation have an intelligent test-coverage generation system. This provides all the paths to be covered.Also, the AI-based algorithms help in selecting ways and looking for bugs. Thus,bug hunting become quicker which increase the productivity of testing teams to a great extent. Hence,with a combination of AI algorithms and bug finding, analytics and AI help in finding defects.
-Moreover,AI-based test automation system has cognitive abilities with its insights and smart techniques that help in optimizing testing efforts. This significantly helps in improving the reliability and quality of software.
-AI uses Test data as the key to an efficient system of intelligent test automation. This system enable the optimization of test cases and better UI testing by decreasing the heavy analysis required.
-The analytics engine improves the mechanism of selecting the trade-off between bug hunting and coverage. But the testing is a continuous process of testing and learning and predicting. Thus, AI algorithms keep a watch on your results for appropriate learning and trends. Continuous testing and predictive trends will reduce or avoid any delay in the system.
Read more – What are the Software testing current trends?
Benefits of Intelligent Test Automation and Analytics
-Machine learning and AI help in improving the software quality by reducing the efforts by Predictive analysis or Defect Analysis. This can look for the most defects prone areas using data analytics tools.
-The traceability of bugs improves by mapping defects to the requirements and specifications documents. AI uses a risk-based approach and prioritization for making defect discovery efficient.
-Analytics allows software testing to overcome the challenges due to traditional/legacy systems. The testing procedures improve by using the analytics for creating test history and performance charts. There is a more significant amount of clear insights helping in tracking the results for testers and designers. Also,the feedback mechanism allows improving the speed of slow processes and correcting the errors quickly.
-The testing procedures for the future can be created using the visual depiction of test performance. The sophisticated software testing tools become more efficient, and productivity of testing teams gets a boost.
Future of Intelligent Test Automation
Intelligent Test Automation and Analytics is going to make a significant impact on the testing phase of Software development. This makes the role of test engineers and analysts even more strategic. The test engineers have to play a key role in guiding the test process for the best way possible.
Furthermore,the testing teams become effective with the involvement of AI in the process. The Intelligent test automation along with the analytics tools and techniques help greatly in software development. This also allow the testing leads to perform strategic activities for a higher business value for a future term.
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