A testing scenario (forming part of a computing environment executing a plurality of applications) is initiated to characterize performance of the applications. During the execution of the testing scenario, various performance metrics associated with the applications are monitored. Thereafter, data characterizing the performance metrics is provided (e.g., displayed, loaded into memory, stored on disk, transmitted to a remote computing system, etc.). The testing scenario is generated by monitoring service calls being executed by each of a plurality of automates across the applications, generating a service request tree based on the monitored service calls for all of the applications, and removing cyclic dependencies in the service request tree such that reusable testing components are only used once. Related apparatus, systems, techniques and articles are also described.

Devices, systems, and methods for providing an engine control system configured with a two-part test equipment monitor where at least one part is selectively removable are disclosed. An engine control system for an aircraft includes an electronic control unit (ECU). The ECU is configured to implement a production support equipment module and a selectively removable test support equipment module. The production support equipment module enables restricted data monitoring of the engine control system. The test support equipment module enables a comprehensive interface with the engine control system when installed with the ECU.

A data exchange that provides self-service data provisioning is provided. The data exchange may include a raw data layer, a model data layer, a plurality of workspaces and a testing environment. The raw data layer may be a landing zone for raw data records received from systems of record. The raw data layer may receive a plurality of raw data records, model and process the data records and transfer the data records to the model data layer. The model data layer may be a data layer that includes data modeled to data exchange specifications and enables queries to be executed on the data included in the model data layer. Each workspace may be allocated to a consumer. The consumer may query the plurality of data records within the model data layer. The testing environment may test scripts to ensure that the scripts conform to a predetermined set of testing specifications.

A system for detecting malicious software, comprising at least one hardware processor adapted to: execute a tested software object in a plurality of computing environments each configured according to a different hardware and software configuration; monitor a plurality of computer actions performed in each of the plurality of computing environments when executing the tested software object; identify at least one difference between the plurality of computer actions performed in a first of the plurality of computing environments and the plurality of computer actions performed in a second of the plurality of computing environments; and instruct a presentation of an indication of the identified at least one difference on a hardware presentation unit.

In some examples, a system processes event data and video frames produced by a program during execution of the program, the event data representing user actions with respect to a graphical user interface (GUI) of the program. The system identifies an area of the GUI that corresponds to a respective user action of the user actions, wherein identifying the area of the GUI uses a first video frame before an event corresponding to the respective user action, and a second video frame after the event corresponding to the respective user action. The system identifies, based on the identified area, a test object representing a user interface (UI) element, and generates a test script for testing the program, the test script including the test object.

Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls. Such tests may be used to train the machine learning model.

Distributed ledger-based networks (DLNs) employ self-executing codes, also known as smart contracts, to manage interactions occurring on the networks, which may result in the generation of a massive amount of DLN state data representing the interactions and participants thereof. The instant disclosure discloses systems, apparatus and methods that allow interactions to occur on the DLNs without modification to stored data, thereby improving the storage capabilities of the networks.

An example method of operation may include automatically receiving information from a storage area in response to a signal, which information may include test results for a computer product evaluated by a plurality of test stages. The method may also include retrieving deployment parameters for the computer product, determining whether the test results satisfy the deployment parameters for the computer product, and automatically authorizing deployment of the computer product based on whether the test results satisfy the deployment parameters.

A non-transitory computer-readable recording medium storing an acceleration test program for causing a computer to execute a process, the process includes selecting a cooperation application that operates in cooperation with a test target application that is a target application of an acceleration test by accelerating an operation of an application, determining an acceleration degree of an operation in an acceleration mode in which an operation of an application is accelerated in comparison to a normal mode, and disabling an acceleration of an operation of a non-cooperation application that does not cooperate with the test target application during an acceleration of operations of the test target application and the cooperation application based on the acceleration degree.

An intelligent determination of code change review assignments and subsequent secured access to the determined assignments. Code changes undergo code change complexity determination which is based on (i) a level of importance of the module(s) in which the changes occur, (ii) the volume of metadata files impacted by the code changes, and (iii) the dependency of the code changes on external modules. A distributed trust computing network is implemented and a code change smart contract which relies on smart contract rules is used to determine and allocate code change review assignments. In this regard, data blocks within a distributed ledger define individual segments/portions of the code change file with each data block identifying a code change review assignment.