Abstract: Vector-based hybrid search for chatbots is provided. A system receives, via a chatbot, a user query. The system generates, using an artificial intelligence model, a vector representation based on a combination of the user query, historical queries, and corresponding responses and identify a cached response corresponding to the user query in a semantic cache using the vector representation. The system executes, based on the cached response, a hybrid search operation including retrieval of first and second results having a first and second accuracy value by execution of a first and second search process on a first and second data source. The system selects one of the first or the second results based on modeling the first and the second accuracy value and displays, responsive to the user query, an output corresponding to the selected results.

Abstract: The present disclosure involves systems, software, and computer implemented methods for deep machine learning in software test automation. One example method includes capturing images of a web page during successful executions of a test script that tests the web page. A convolutional neural network (CNN) is trained using the captured images. The CNN is configured to determine whether an image input matches previously captured images. A determination is made that a particular execution of the test script has failed. A first image of the web page is captured at a time of the test script execution failure. The first image is provided to the CNN. An output of the CNN is received that indicates whether the first image matches previously captured images. A source of the test script execution failure is determined based on the output received from the CNN.