Automation of software test case generation and implementation

Abstract

The techniques disclosed herein enhance software testing through automated test case generation and implementation. This is accomplished through utilizing a natural language interface and a generative probabilistic model, such as a large language model. A first user input can provide descriptions of a software feature to be tested and various use cases for the software feature. Accordingly, the probabilistic model generates a plurality of test cases for evaluating various aspects of the software feature in accordance with the first user input. The natural language descriptions can be accompanied by a contextual input to constrain the probabilistic model and ensure consistent outputs. Subsequently, a second user input selects a test case from the generated test cases to be implemented. In response, the probabilistic model generates a software implementation of the selected test case. The software implementation is then executed with log data extracted from the testing environment for processing results.