System, method, and computer program for generative artificial intelligence automation flows

A method, comprising:

• at a computer system:
• processing a user input text prompt describing a new requirement for a new functionality of an application, using generative artificial intelligence, to generate one or more test cases for the new functionality of the application, the one or more test cases being a set of actions, which when performed, indicate whether the application satisfies the new requirement for the new functionality of the application;
• determining key words of the one or more test cases;
• applying dictionary-based tokenization to the key words to build a tokenized dictionary-of automation assets;
• learning user interface components of the application from documentation defining the application;
• automatically generating one or more automation scripts for each of the user interface components of the application using a preconfigured template automation script associated with the user interface component;
• processing the tokenized dictionary and the one or more automation scripts using a machine learning model to build an end-to-end automation flow for testing the application in accordance with the one or more test cases generated for the application;
• executing the end-to-end automation flow for testing that the application satisfies the new requirement;
• and
• outputting a result of the testing that indicates whether the application satisfies the new requirement.

A system, comprising:

• a non-transitory memory storing instructions;
• and
• one or more processors in communication with the non-transitory memory that execute the instructions to:
• process a user input text prompt describing a new requirement for a new functionality of an application, using generative artificial intelligence, to generate one or more test cases for the new functionality of the application, the one or more test cases being a set of actions, which when performed, indicate whether the application satisfies the new requirement for the new functionality of the application;
• determine key words of the one or more test cases;
• apply dictionary-based tokenization to the key words to build a tokenized dictionary of automation assets;
• learn user interface components of the application from documentation defining the application;
• automatically generate one or more automation scripts for each of the user interface components of the application using a preconfigured template automation script associated with the user interface component;
• process the tokenized dictionary and the one or more automation scripts using a machine learning model to build an end-to-end automation flow for testing the application in accordance with the one or more test cases generated for the application;
• execute the end-to-end automation flow for testing that the application satisfies the new requirement;
• and
• output a result of the testing that indicates whether the application satisfies the new requirement.