Abstract: A collection of multiple software applications may be tested when a patch is issued. However, an emergency patch for a time-sensitive incident may not allow for full regression or functional testing. Provided herein are techniques for performing a multiple application smoke test. Access information, login information, and a success indicator are obtained for each of a plurality of software applications. A test plan including two or more test packages is determined. Each test package indicates a subset of applications and includes access information, login information, and success indicators corresponding to the subset. The test packages are executed in parallel, including authenticating, loading, and validating. Logs are generated and a user interface is provided to present the logs and whether validation of the application interfaces passed or failed, and a failure reason for failed tests.

Abstract: A collection of multiple software applications may be tested when a patch is issued. However, an emergency patch for a time-sensitive incident may not allow for full regression or functional testing. Provided herein are techniques for performing a multiple application smoke test. Access information, login information, and a success indicator are obtained for each of a plurality of software applications. A test plan including two or more test packages is determined. Each test package indicates a subset of applications and includes access information, login information, and success indicators corresponding to the subset. The test packages are executed in parallel, including authenticating, loading, and validating. Logs are generated and a user interface is provided to present the logs and whether validation of the application interfaces passed or failed, and a failure reason for failed tests.

Abstract: Technologies are provided for automated exploratory testing using machine learning. In response to receiving an identifier for an application to be tested, a machine learning model can be generated that can be used to automate exploratory testing of the application. The machine learning model can be generated based on test scenarios associated with the application and user interface controls of the application. The machine learning model can comprise one or more data structures that model relationships between user interface control values and application functionality defined by the test scenarios. The machine learning model can be used to generate exploratory testing operations targeting the application. In at least some embodiments, the machine learning model comprises an artificial neural network comprising input layer nodes associated with user interface controls and/or hidden layer nodes associated with application test scenarios.

Abstract: A system, method, and computer-readable medium for performing a product query operation, comprising: receiving user input from a user; identifying keywords contained within the user input; comparing keywords to vector representations of words and also to a document vector representation of product specifications in a backend product repository, each vector representation of a word being associated with a particular query type; identifying component details based upon the comparing; and, providing a list of products corresponding to the component details to the user in real time.

Abstract: A system, method, and computer-readable medium for performing a product query operation, comprising: receiving user input from a user; identifying keywords contained within the user input; comparing keywords to vector representations of words and also to a document vector representation of product specifications in a backend product repository, each vector representation of a word being associated with a particular query type; identifying component details based upon the comparing; and, providing a list of products corresponding to the component details to the user in real time.