An occlusion metric is computed for a target object in a 3D multi-object simulation. The target object is represented in 3D space by a collision surface and a 3D bounding box. In a reference surface defined in 3D space, a bounding box projection is determined for the target object with respect to an ego location. The bounding box projection is used to determine a set of reference points in 3D space. For each reference point of the set of reference points, a corresponding ray is cast based on the ego location, and it is determined whether the ray is an object ray that intersects the collision surface of the target object. For each such object ray, it is determined whether the object ray is occluded. The occlusion metric conveys an extent to which the object rays are occluded.

Methods and apparatus are described by which a rich, time-correlated information set is captured during automated testing of an application in a way that allows the application developer to understand the state of the application under test (AUT), the browser interacting with the AUT, and/or the device interacting with the AUT, as it/they changed over time. Mechanisms or features associated with browsers and/or device operating systems are exploited to capture such information, not only for the purpose of better understanding individual test runs, but also to enable the use of analytics over data sets.

A computer-implemented method of evaluating the performance of a full or partial autonomous vehicle (AV) stack in simulation, the method comprising: applying an optimization algorithm to a numerical performance function defined over a scenario space, wherein the numerical performance function quantifies the extent of success or failure of the AV stack as a numerical score, and the optimization algorithm searches the scenario space for a driving scenario in which the extent of failure of the AV stack is substantially maximized, wherein the optimization algorithm evaluates multiple driving scenarios in the search space over multiple iterations, by running a simulation of each driving scenario in a simulator, in order to provide perception inputs to the AV stack, and thereby generate at least one simulated agent trace and a simulated ego trace reflecting autonomous decisions taken in the AV stack in response to the simulated perception inputs, wherein later iterations of the multiple iterations are guided by the results of previous iterations of the multiple iterations, with the objective of finding the driving scenario for which the extent of failure of the AV stack is maximized.

A method of information processing includes obtaining a traffic request for a target service. The traffic request includes a request tag. The method further includes determining a target environment that matches with the request tag and obtaining first deployment information of the target service. The first deployment information of the target service indicates one or more test versions of the target service to be tested, and one or more corresponding environments respectively deployed for the one or more test versions of the target service. The method also includes determining, from the one or more test version, a target test version that is deployed to the target environment and accessing the target service in the target test version in the target environment. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also contemplated.

A distributed database system, a data disaster backup exercise method and a non-transitory computer-readable storage medium are disclosed. The distributed database system may include a local computer room (110) and an offsite computer room (120), where the local computer room (110) includes a local management node (111) and a local database cluster (112), the offsite computer room (120) includes an offsite management node (121), an offsite exercise database cluster (123) and an offsite synchronization database cluster (122); where the local database cluster (112) and the offsite synchronization database cluster (122) are both connected with the local management node (111); the offsite exercise database cluster (123) is configured for: establishing a first connection with the offsite management node (121); and receiving a test service sent by a service layer.

Techniques for implementing rollback of infrastructure changes in an infrastructure orchestration service are described. In certain examples, an infrastructure orchestration service is disclosed that manages both provisioning and deploying of infrastructure assets within a cloud environment. The service receives a plan comprising a set of instructions associated with a set of infrastructure assets of an execution target and identifies a first state of the set of infrastructure assets. The service executes the set of instructions in the plan to achieve a second state for the set of infrastructure assets. Based in part on the executing, the service receives a trigger for rolling back the plan to restore the set of infrastructure assets in the plan to the first state and executes a rollback plan for the plan. The service then transmits a result associated with the execution of the rollback plan.

The present disclosure is directed to systems, methods and devices for assisting with testing automated industrial process routines. The addition of a software automation object to a test execution user interface may be received. The software automation object may be added to the test execution user interface from a software object library comprising a plurality of software objects. Each of the software automation objects may include an automated control device layer, a human machine interface layer, and a testing layer. A request to initiate an operational test of the software automation object in the test execution user interface may be received. Upon receiving the request, the operational test may be executed, and test results for the operational test of the automation software object may be displayed on the test execution user interface.

A computing device may access a target code for implementing an application. The device may identify addresses for one or more functions or one or more variables associated with the target code. The device may generate an interval tree comprising a root node and one or more function nodes. The device may in response to the target code invoking a function or variable: generate an intercept function configured to intercept communication between the target code and a call address for the at least one of the one or more functions or the one or more variables invoked by the target code. The device may intercept data communicated between the target code and the call address. The device may store the intercepted data as a function node in the interval tree. The device may transmit the interval tree to a user device.

An automated video game testing framework and method includes communicatively coupling an application programming interface (API) to an agent in a video game, where the video game includes a plurality of in-game objects that are native to the video game. The agent is managed as an in-game object of the video game. A test script is executed to control the agent, via the API, to induce gameplay and interrogate a behavior of a test object. The test object is identified from the plurality of in-game objects based on a query that specifies an object attribute of the test object.

A vehicle can capture data that can be converted into a synthetic scenario for use in a simulator. Objects can be identified in the data and attributes associated with the objects can be determined. The data can be used to generate a synthetic scenario of a simulated environment. The scenarios can include simulated objects that traverse the simulated environment and perform actions based on the attributes associated with the objects, the captured data, and/or interactions within the simulated environment. In some instances, the simulated objects can be filtered from the scenario based on attributes associated with the simulated objects and can be instantiated and/or destroyed based on triggers within the simulated environment. The scenarios can be used for testing and validating interactions and responses of a vehicle controller within the simulated environment.