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.

Building a first layer model of a three-layer model based on attributes that are sensitive features is provided. A first dimension reduction of sensitive features removes each sensitive feature having an indicator that it is present in a patch and does not contribute to one or more of three probabilities. A second dimension reduction of insensitive features is performed using vectorizing and using one-hot encoding. The remaining insensitive features are main features. One or more second layer models of the three-layer model is built based on the main features. The third layer model is built based on a verification dataset and the first layer model. Regression test coverage is recommended based on prediction result of the third layer model, and wherein regression tests are selected. The training dataset is updated based on probability calculations of the first layer model.

A server that is a program providing device includes: a provision processing unit that provides a program part constituting a control program being a program to be executed in a controller; an authentication unit that authenticates an operation simulation module being a program for simulatively performing operation in accordance with the program part on a basis of a result of verification on whether or not the operation simulation module can simulate operation of the controller to be performed by execution of the program part; and an operation checking unit that checks operation of the program part by using the authenticated operation simulation module.

A system and method for automatically generating scenario patterns are disclosed. The system includes a processor; and a memory operatively connected to the processor via a communication interface. The processor receives a request to create new features in connection with development and/or testing of one or more applications; automatically generates scenario patterns corresponding to the new features by utilizing an automatic scenario patterns generating tool; outputs the scenario patterns in a predefined file format providing an end-2-end (E2E) view spanning multiple applications, state models and events; uploads the scenario patterns in the predefined file format onto a system different from the automatic scenario patterns generating tool; deploys corresponding application programming interface (API) on a private cloud to request the scenario patterns in the predefined file format from the system; and implements the requested scenario patterns to create the new features.

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.

A system that automatically reduces test cycle time to save resources and developer time. The present system selects a subset of tests from a full test plan that should be selected for a particular test cycle, rather than running the entire test plan. The subset of tests is intelligently selected using metrics such as tests associated with changed code and new and modified tests.

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 and a method for recommending a modification to a test package for a software under test. A release note package associated to a feature of a software is received. The release note package is analysed in real time using machine learning based models. Further, a keyword is extracted from the release note package using a keyword extraction technique. The keyword corresponds to the feature of the software. The keyword is compared with nomenclatures present in a test package using a pattern matching technique. The test package is associated to the feature of the software. Finally, a modification to the test package is recommended based on the comparison. The modification comprises addition, deletion, or updating an existing element of the test package. It may he noted that the modification is recommended using an Artificial Intelligence (AI) technique.