Various techniques for emulating edge locations in cloud-based networks are described. An example method includes generating an emulated edge location in a region. The emulated edge location can include one or more first computing resources in the region. A host in the region may launch a virtualized resource a portion of the one or more first computing resources. Output data that was output by the virtualized resource in response to input data can be received and reported to a user device, which may provide a request to migrate the virtualized resource to a non-emulated edge location. The non-emulated edge location may include one or more second computing resources that are connected to the region by an intermediary network. The virtualized resource can be migrated from the first computing resources to at least one second computing resource in the non-emulated edge location.

A vehicle may include a primary system for generating data to control the vehicle and a secondary system that validates the data and/or other data to avoid collisions. For example, the primary system may localize the vehicle, detect an object around the vehicle, predict an object trajectory, and generate a trajectory for the vehicle. The secondary system may localize the vehicle, detect an object around the vehicle, predict an object trajectory, and determine a likelihood of a collision of the vehicle with the object. A simulation system may generate simulation scenarios that test aspects of the primary system and the secondary system. Simulation scenarios may include simulated vehicle control data that causes the primary system to generate a driving trajectory and simulated object data that causes the secondary system to determine a collision.

In some embodiments, a method includes generating, based on distributed ledger data associated with a first distributed ledger-based network (DLN), distributed ledger data associated with a second DLN. The first DLN and the second DLN each is a fork and the distributed ledger data associated with the first DLN include account data associated with a set of accounts. The method includes generating a request to initiate a transaction between a first account and a second account. The method includes authenticating the transaction based on a protocol associated with the second DLN and without using a private cryptographic key of the first account. The method includes sending a signal indicating the transaction was authenticated and storing information associated with the transaction in the distributed ledger data associated with the second DLN.

Exemplary embodiments may perform feasibility analysis to determine whether it is possible to generate a lookup table that conforms to an error tolerance given a specification of a function or a set of data points that the lookup table attempts to approximate, an indication of breakpoint positions, and a specification of a data type for table values. Where it is determined that it is feasible to generate the lookup table, the lookup table may be automatically programmatically generated. Suggestions of how to modify the breakpoint positions and/or error tolerance may be provided. In addition, a visualization of approximation error and error tolerance, such as a visualization showing a feasibility margin, may be output. New data points may be processed to update table values for an already generated lookup table.

Embodiments provide systems, methods, and computer-readable storage media for automated and objective testing of applications or processes. Graphical representations of the application may be analyzed to derive attribute data and identify flows (e.g., possible processing paths that may be accessed during utilization of the application by a user). Test cases may be automatically generated based on the attribute data and the identified flows. Additionally, testing scripts for testing the portions of the application corresponding to each identified flow may be generated using machine learning logic. Once generated, the testing scripts may be executed against the application to test different portions of the application functionality (or processes). Execution of the testing scripts may be monitored to generate feedback used to train the machine learning logic. Reports may be generated based on the monitoring and provided to users to enable the users to resolve any errors encountered during the testing.

An operation verifying apparatus of a first embodiment acquires a log indicating the content of a sequence of operations performed on a predetermined device, identifies corresponding functions from the log, and automatically generates a program based on the identified functions. Input data, which is to serve as an argument of each of these functions, is set. Execution sets as well as test scenarios are each structured by combining a program and input data. Then each execution set is continuously executed. As a result, an operation test using a test program is executed.

Aspects of the present disclosure involve systems, methods, devices, and the like for creating an application lifecycle management platform for big data applications. In one embodiment the lifecycle management platform can include a multiple-layer container file that integrates multiple big-data tools/platforms. The system may create a generic template application, create a build environment for the generic template application, create a test environment for the generic template application, and run the built generic template application in the test environment prior to the user writing any new code in the generic template application. In one embodiment, the test environment includes a container management system or virtual machine that launches the big data application (which may be the generic template application before a developer edits the file) on a separate big-data server cluster.

A method and system for remote testing of a plurality of devices is disclosed. The method may include receiving a request from a client system to perform testing on a set of remote devices. The local system and the client system are connected via a first network connection and the plurality of remote devices are connected to the local system via a second network connection. The method may further include receiving an input from the client system with reference to a test-suite to perform a testing action on the set of remote devices, generating a test command corresponding to the input, and transmitting the test command to each of the set of remote devices. The method may further include receiving feedback from each of the set of remote devices and transmitting the feedback to the client system.

The present disclosure provides techniques for configuring and provisioning a tenant for testing microservices in a multi-tenant instance. Code is committed for a modified microservice, and a configuration is received for a production tenant of the multi-tenant instance. The configuration is updated to include a reference to the updated microservice, and then provided to a provisioner that provisions a test tenant based on the configuration. The microservices for the test tenant are compared with versions in a code version management system and updated, then a reference to the test tenant is provided to a developer to test the modified microservice. The test tenant may be deprovisioned after a predetermined amount of time, by a command of the developer, or other automated method.

This disclosure relates generally to the field of source code processing, and, more particularly to a method and system for identification of redundant function-level slicing calls. The method disclosed generates program dependence graphs (PDGs) based on a slicing criteria and a function corresponding to the function-level slicing call. Further the method classifies the function-level slicing call into redundant or non-redundant by traversing the PDGs and checking if a predefined condition is satisfied or not. The function-level slicing calls are classified as redundant if the check is not satisfied and are classified as non-redundant if the check is satisfied. The disclosed method can be used in identifying redundant function-level slicing calls in applications such as automated false positive elimination (AFPE), automated test case generation and so on.