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.

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 method for determining a relative importance of a selected test in a plurality of tests, comprising a computational device for receiving one or more characteristics relating to an importance of the code, an importance of each of the plurality of tests, or both; and for determining the relative importance of the selected test according to said characteristics.

Program analysis is provided. An intermediate representation of a program is generated. A set of structured inputs is provided to the program. The set of structured inputs are derived from the intermediate representation. The program is executed using the set of structured inputs. A set of action steps is performed in response to observing a violation of a policy during execution of the program using the structured inputs.

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.

A computer-implemented method includes downloading respective instances of an enterprise mobile application to a plurality of mobile devices. The instances of the enterprise mobile applications, while executing on respective mobile devices, capture, for each session, a session log that includes indications of ordered user actions occurring during the session, and optionally time intervals between user actions and/or user attributes. Captured session logs stored at and are mined by one or more servers to discover a particular pattern or sequence of user actions that occurred across multiple, different user sessions. If the number and/or rate of occurrences of the particular pattern is greater than a threshold, a new test case corresponding to the pattern is automatically generated and added to a suite of test cases for the UI functionality of the enterprise mobile application. The updated test suite may be automatically executed on a test version of the enterprise mobile application.

Techniques for auto-intrusive data pattern and test case generation for negative service testing are described. A test engine obtains negative test information specifying negative test input examples or schemas associated with tests that are expected to fail. A test generator generates multiple test cases based on the negative test information. A test execution orchestrator splits each test case up into actions that are inserted into queues, where workflow execution agents perform the tests by reading from the queues and interacting with services. The tests may also include adjusting a rate of transactions allowed between top-level services and/or downstream services. Results from the testing are analyzed by a test analysis engine and used to inform the services or the test originator of test cases where the expected failures did not arise.

A method to discover an input sequence for an unknown binary program is provided. The method may include obtaining a first input sequence for an unknown binary program. The method may also include generating multiple mutated input sequences from the first input sequence and executing the unknown binary program with the first input sequence and/or the mutated input sequences as the input. The method may further include recording one or more branch counts and execution traces of the executions of the unknown binary program and selecting an execution trace that is different or has a different branch count from the other execution traces of the unknown binary program. A branch in the selected execution trace may be negated to generate a symbolic path condition and the symbolic path condition may be solved to discover a second input sequence for the unknown binary program.