Systems and methods for automated test data microservices are provided. Test versions of software (such as an Application Programming Interface (API)) may be configured to automatically generate test data and to call a microservice to manage the test data. The microservice may automatically add and remove the test data from an operational data store to facilitate the testing process and to automatically perform setup and teardown stages of the testing process.

Systems and methods for testing components or scenarios with execution history are disclosed. A method may include: receiving, at a testing interface and from an application or program executed by a user electronic device, an identification of a test and one or more data layers of a plurality of data layers in pod to test, the plurality of data layers including a data collection layer, a data ingestion layer, a data messaging layer, a data enrichment layer, and a data connect layer; receiving, by the testing interface, a selection of testing parameters or values for the identified test; retrieving, by the testing interface, the identified test; executing, by the testing interface, the identified test on the identified one or more data layers using the selected testing parameters or values; retrieving, by testing interface, results of the execution of the test; and outputting, by the testing interface, the results.

A computer-implemented method, according to one embodiment, includes: receiving, at a computer, a request to facilitate a testing environment, where the request specifies a number and type of resources to be included in the testing environment. A database which lists available resources in systems and/or devices that are in communication with the computer is inspected and the available resources are compared to the number and type of resources specified in the request to be included in the testing environment. In response to determining that a valid combination of the available resources meets the number and type of resources specified in the request to be included in the testing environment, the database is updated to indicate that each of the resources in the valid combination are in use. Moreover, the request is satisfied by returning information about the resources in the valid combination.

A system for classifying tasks based on workflow patterns detected on workflows through a real time video feed that shows steps being performed to accomplish a plurality of tasks. Each task is associated with a different set of steps. The system accesses a first set of steps known to be performed to accomplish a first task on the webpages. The first set of steps is represented by a first set of metadata. The system extracts a second set of metadata from the video feed. The second set of metadata represents a second set of steps to perform a second task. The system determines whether the second set of metadata corresponds to the first set of metadata. If it is determined that the second set of metadata corresponds to the first set of metadata, the system classifies the second task in a class to which the first task belongs.

Various aspects of the disclosure relate to test automation systems with pre-compilers to validate various steps associated with a test script. An artificial intelligence (AI)-based pre-compiler may use natural language processing (NLP) to validate various steps associated with a test script associated with an application. Other aspects of this disclosure relate to automated encryption and mocking of test input data associated with test scripts.

A system for detecting errors in task workflows from a real time video feed records. The video feed that shows a plurality of steps being performed to accomplish a plurality of tasks through an automation process system. The system splits the video feed into a plurality of video recordings which are valid breakpoints determined through cognitive Machine Learning Engine, where each video recording shows a single task. For each task from among the plurality of tasks, the system determines whether the task fails and the exact point of failure for that task. If the system determines that the task fails, the system determines a particular step where the task fails. The system flags the particular step as a failed step. The system reports the flagged step for troubleshooting.

Systems, methods and media are directed to automatically generating a recommendation. Data describing a configuration of a production infrastructure is received, the production infrastructure running the system operating in the production environment. One or more metrics data values indicative of a performance of the system operating in the production environment is retrieved. Expected performance values of the system are received. An augmented decisioning engine compares the metrics data values with the expected performance values. The augmented decisioning engine is trained to provide a recommended configuration of the production infrastructure. Based on the comparing, the augmented decisioning engine is trained to improve subsequent recommendations of configuration of the production infrastructure through a feedback process. The augmented decisioning engine is adjusted based on an indication of whether the configuration of production infrastructure satisfies a threshold metric data value in response to the production infrastructure running the system operating in a production environment.

Various aspects of the disclosure relate to test automation systems with pre-compilers to validate various steps associated with a test script. An artificial intelligence (AI)-based pre-compiler may use natural language processing (NLP) to validate various steps associated with a test script associated with an application. Other aspects of this disclosure relate to automated encryption and mocking of test input data associated with test scripts.

A computer system determines stack usage. An intercept function is executed to store a stack marker in a stack, wherein the intercept function is invoked when a program enters or exits each function of a plurality of functions of the program. A plurality of stack markers are identified in the stack and a memory address is determined for each stack marker during execution of the program to obtain a plurality of memory addresses. The plurality of memory addresses are analyzed to identify a particular memory address associated with a greatest stack depth. A stack usage of the program is determined based on the greatest stack depth. Embodiments of the present invention further include a method and program product for determining stack usage in substantially the same manner described above.

Computing systems, for example, multi-tenant systems deploy software artifacts in data centers created in a cloud platform using a cloud platform infrastructure language that is cloud platform independent. The system receives an artifact version map that identifies versions of software artifacts for data center entities of the data center and a cloud platform independent master pipeline that includes instructions for performing operations related to services on the data center, for example, deploying software artifacts, provisioning computing resources, and so on. The system receives a deployment manifest that provides declarative specification of deployment strategies for deploying software artifacts in data centers. The system implements a deployment operator that executes on a cluster of computing systems of the cloud platform to implement the deployment strategies.