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Why it's Important to Beta Test Your AI Applications

AI Applications tester

The scope of Artificial Intelligence (AI) spans across various industries and includes such technologies as image processing, analytics, voice recognition and more. AI applications work in real-time and often help drive the business decision making process. To ensure these applications perform their best, it's important to test them thoroughly prior to deployment. Looking at the versatility of AI applications, testing can be complex. Beta testing is a critical technique that can be utilized for testing. 

Understanding Beta Testing

User Acceptance Testing (UAT) is the last phase of the software testing process and can be categorized into 2 phases: alpha testing and beta testing.

  • Alpha testing is completed by testers before releasing the product in a UAT environment. The application is deployed when the alpha phase is executed successfully.
  • Beta testing is the final process where real-time users participate in testing and provide feedback for further improvement. This sampling from the intended users is used for the final product. Beta testing is widely used today.

Introduction to AI Applications

If an application is built in AI, it should be developed by well-driven data models. All  applications are then categorized by intents, and each intent is trained for different aspects. Thorough product tests require basic to complex user expressions to train all the applications, as well as each intent. 

To work on each intent expression, it's difficult to train a model quickly. We generally automate the expressions and their related results and run/execute them.

Beta Testing AI Applications

Once an AI application is developed, tests should be executed in beta environments, where a set of real-time users walk through the complete application and provide feedback.

Users of a beta test take up some sample user expressions. They will modify the product accordingly based on provided observations through feedback forms to the development team.

Example for Beta Testing in AI Applications

Most mobile products are first released to employees of a company who are not involved in the development of the application. Their inputs on usage are gathered and updates are made before the updated application is released into the market.

Individuals involved in the beta test won't know the development or testing process, and they won’t undertake any documented or procedure-oriented test scenarios. This allows for unbiased feedback on the application experience and user friendliness. 

As most companies follow the agile development process, the process of testing a mobile app and bringing to market goes as follows:

  1. Beta testing is performed
  2. Immediate feedback is provided from the end users
  3. Feedback is incorporated and developed in the next sprints
  4. Updates are released in the very next deployment

Benefits of Beta Testing for AI Applications

Since beta testing gathers real-time suggestions and improvements from end users, it creates a more effective and useful end product. Here are some additional benefits from beta testing AI applications: 

  • Scope and use of product increases
  • Builds confidence of stakeholders and product owners
  • User friendliness can be achieved for end users
  • Defined agile process with tangible updates and timelines

Automation By Any Means

SriVaniAbout Author

Srivani Devaravajjala is a Test Lead at V-Soft consulting and has more than 9 years of IT experience in the QA stream. She is a certified Scrum Master. She has sound testing knowledge in Web & Mobile App, GUI, Functional, Integration, System, Ad-hoc, Usability, Database, Smoke, Regression and Retesting. In her quality testing career, she attained skills in Selenium IDE, WebDriver and QTP automation Testing Tools.


Topics: AI, QA TCOE, Testing AI Applications

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