Intelligent Test Automation and Analytics

With the rapid rise in technology, there has been a tremendous change in the way software testing works. The latest move is the use of AI (Artificial Intelligence) in software test automation.

Artificial Intelligence is changing the entire world of technology, and it is now ready to revolutionize the gamut of software testing. This use of AI in testing is also called as the area of Intelligent Test Automation and Analytics.

In automation testing, AI algorithms will help in automatic script generation, prediction of bugs and make changes for improving coverage. All these parts of software testing take a lot of time and effort. Furthermore, the entire process of working with the code will be transformed. The intelligent test automation system will hence change the testing environment altogether.

 

Why do we need AI in Test Automation

The organizations today are moving towards being digital very quickly. The digital transformation is indeed essential today, but it is not so easy to achieve. The advent of cloud, DevOps, Internet of Things (IoT), etc. have made complex to work with automation testing. As a result, the software and apps have to be improved continuously and quickly making the task of software testing even more complex.

To tackle the complex and fragmented software testing process, AI can help hugely. The process of manual testing is cumbersome and long making it necessary for us to go digital.

AI offers us many new tools and technologies like analytics and machine learning. These will help in carrying out the actual test automation. AI tools will provide guidelines and recommendations for carrying out the tests while learning and improving the software development phases. They would predict the business needs and results assisting the coding teams for working efficiently.

 

Features of Intelligent Test Automation

  • Essentially, the software or app needs the testing, the perspective of a user and intelligent automation system is the best solution for this. The system will include tools to access the application like a user and test the application. This automation testing involves using the app after taking control of the app on a physical machine and trying the workflows. These tools will also collect analytics with this mechanism.
  • AI-based Test automation will have an intelligent test-coverage generation system that can provide all the paths to be covered. The AI-based algorithms will help in selecting ways and looking for bugs. Bug hunting will become quicker increasing the productivity of testing teams to a great extent. With a combination of coverage algorithms and bug hunting, analytics and AI will help in hunting defects based on the user journeys.
  • AI-based test automation system has cognitive abilities with its insights and smart techniques that will help in optimizing testing efforts. The reliability and quality of software will significantly improve. AI uses Test data as the key for an efficient system of intelligent test automation. This system will enable 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 will 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.

 

Benefits of Intelligent Test Automation and Analytics

  • Machine learning and AI will help in improving the software quality by reducing the efforts by Predictive analysis or Defect Analysis. This can look for defects in areas most prone to the defects using data analytics tools. The traceability of bugs improves by mapping defects to the requirements and specifications documents. AI uses risk-based approach and prioritization for making defect discovery efficient.
  • Analytics allows the 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 greater amount of clear insights helping in tracking the results for testers and designers. The feedback mechanism allows improving the speed of slow processes and correcting the errors quickly.
  • The testing procedures for 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 in the testing phase of Software development. This makes the role of test engineers and analysts even more strategic. The test engineers will have to play a key role in guiding the test process for the best way possible. The testing teams will become effective with the involvement of AI in the process. The Intelligent test automation along with the analytics tools and techniques will help greatly in software development. This will also allow the testing leads to perform strategic activities for a higher business value for a future term.

 

How can TestUnity help you in intelligent test automation?

TestUnity has expertise in all testing domains and processes. However, automation is our core expertise area and we are continuously excelling on it. Artificial Intelligence is a new technical area that needs more research and development, and that’s why we too are focusing on it. As a tester in this particular area both the knowledge on machine learning as well automation testing are required.

Hence, we are working on some pilot projects to analyze different case studies on intelligent test automation. The case studies are based on real time scenarios which we have worked upon previously and will guide us to open new dimensions of ideas.

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