Abstract: In some implementations, a device may determine that a computing environment is to be deployed. The computing environment may be associated with an infrastructure configuration. The device may obtain, from a repository, one or more definition files for the infrastructure configuration. The repository may include definition files associated with multiple infrastructure configurations. The device may cause deployment of the computing environment based on the one or more definition files.

Abstract: A build of a software solution that is cooperatively performed is automated. A broadcasting computing entity broadcasts an announcement of the functional operation of which the broadcasting computing entity is capable. Each of a plurality of listening computing entities connected to the network receives the announcement and compares the functional operation with a list of operational needs. After determining itself in need of performance of such a functional operation, a subscribing one of the plurality of listening computing entities transmits a response to the request indicating subscription to the output of the functional operation. The broadcasting computing entity then performs the functional operation, thereby generating and transmitting to the subscribing one of the plurality of listening computing entities the output of the functional operation. The subscribing one of the plurality of listening computing entities then performs an action using the output of the functional operation received.

Abstract: A system is provided for automated intelligent testing of desktop applications through machine learning. In particular, the system may combine process automation technologies with artificial intelligence/machine learning capabilities to perform end-to-end testing of both simple and complex applications. The system may comprise a test automation component that may produce real runtime outputs of the testing process, and an artificial intelligence engine that may produce predicted outputs based on historical data. The system may analyze both the real runtime outputs with the predicted outputs and determine a delta between the real runtime outputs and the predicted outputs. The results of the analysis may be backpropagated to the artificial intelligence engine to iteratively improve the machine learning model over time. In this way, the system may provide an efficient way to test computer applications.

Abstract: An apparatus includes a processor configured to generate each of intermediate representation codes corresponding to each of a plurality of loop structures obtained that corresponds to a neural network computation based on an input specification file of hardware; schedule instructions included in each of the intermediate representation codes corresponding to the plurality of loop structures; select, based on latency values predicted according to scheduling results of the intermediate representation codes, any one code among the intermediate representation codes; and allocate, based on a scheduling result of the selected intermediate representation code, instructions included in the selected intermediate representation code to resources of the hardware included in the apparatus.

Abstract: A method includes: receiving input code that comprises a loop that operates on a first array of elements and a second array of elements, wherein during an iteration of the loop a first operation is performed on an element of the first array of elements, or a second operation is performed on an element of the second array of elements; generating a first compound operation that operates on a predetermined number of elements of the first array of elements, the first compound operation resulting in a first intermediate vector; generating a second compound operation that operates on the predetermined number of elements of the second array of elements, the second compound operation resulting in a second intermediate vector; interleaving the first intermediate vector and the second intermediate vector and storing the interleaved result in a temporary vector; and summing the interleaved result in the temporary vector using an order-preserving sum.

Abstract: Disclosed is a no-code software development platform having at least one user-facing administrative interface for application development and at least one data-facing client interface, the at least one administrative interface adapted for assembling at least one interconnecting functional unit substantially pre-coded to perform selected tasks, the tasks at least one or more of instructed by the user and instructed by data received through the at least one data facing client interface. The at least one interconnecting functional unit has at least one or more of at least one pre-coded form application and at least one pre-coded card application designed to perform interconnected workflow functions and to provide the capability to form software applications. At least one plugin interface operationally couples with at least one client data source via the at least one data facing client interface. A declarative language software program operably couples the at least one interconnecting functional unit.

Abstract: A method, system and computer readable medium for running a legacy application on a non-legacy device. Operating parameters of the non-legacy device when running the legacy application are set based on one or more pre-determined heuristics for adjustment of operating parameters of the newer system when running the legacy application on the non-legacy device from one or more performance metrics and other performance information.

Abstract: System and methods are described for implementing controlled updates of containers in a distributed application deployment environment. In one implementation, a method comprises: receiving a request to update a target container of a plurality of containers within a deployed computing unit; identifying an updated configuration object to be applied to the target container based on the request; receiving or computing a health indicator representative of performance of the deployed computing unit or other containers to which the updated configuration object has been applied; and assigning the updated configuration object to the target container based on the health indicator.

Abstract: An example computer system for managing test coverage for a workflow can include: one or more processors; and non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to create: a workflow execution engine programmed to execute the workflow, including a plurality activities, using a test case; an activity identification engine programmed to identify each time one of the plurality of activities is invoked by the workflow using the test case; and a test coverage engine programmed to determine the test coverage for the workflow based upon identification by the activity identification engine of the plurality of activities invoked by the workflow.

Abstract: Systems and methods for building management utilizing adaptive edge processing are disclosed. The building system can store gateway components on storage devices. The gateway components can facilitate communication with a cloud platform and facilitate communication with a physical building device. The building system can identify a computing system of the building that is in communication with the physical building device. The physical building device can store one or more data samples. The building system can deploy the gateway components to the computing system responsive to identifying that the computing system is in communication with the physical building device. The gateway components can cause the computing system to communicate with the physical building device to receive the one or more data samples and cause the computing system to communicate the one or more data samples to the cloud platform.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to generate code as a plug-in in a cloud computing environment. An example system includes at least one memory, programmable circuitry, and machine readable instructions to program the programmable circuitry to introspect code in a library to obtain introspection data, the library corresponding to a resource that is to be deployed in a cloud infrastructure environment, generate a model based on the introspection data, the model to be a representation of the resource, cross-reference the model with a resource meta-model, the resource meta-model to map characteristics of the resource represented by the model to an actual state of the resource, and generate a plug-in based on the cross-referenced model.

Abstract: A build of a software solution that is cooperatively performed is automated. A broadcasting computing entity broadcasts a request to perform each of the functional operations in the selected set. Each of a plurality of listening computing entities connected to the network receives the request and determines capability of performing each of the functional operations in the selected set. After determining itself capable, a capable one of the plurality of listening computing entities transmits a response to the request indicating such capability, and then performs each of the functional operations in the selected set, thereby generating and transmitting to the broadcasting computing entity the output of the selected set of functional operations. The broadcasting computing entity then performs an action using the output of the functional operation received.

Abstract: A build of a software solution that is cooperatively performed is automated. A broadcasting computing entity broadcasts a request to perform each of the functional operations in the selected set. Each of a plurality of listening computing entities connected to the network receives the request and determines capability of performing each of the functional operations in the selected set. After determining itself capable, a capable one of the plurality of listening computing entities transmits a response to the request indicating such capability, and then performs each of the functional operations in the selected set, thereby generating and transmitting to the broadcasting computing entity the output of the selected set of functional operations. The broadcasting computing entity then performs an action using the output of the functional operation received.

Abstract: A build of a software solution that is cooperatively performed is automated. A broadcasting computing entity broadcasts an announcement of the functional operation of which the broadcasting computing entity is capable. Each of a plurality of listening computing entities connected to the network receives the announcement and compares the functional operation with a list of operational needs. After determining itself in need of performance of such a functional operation, a subscribing one of the plurality of listening computing entities transmits a response to the request indicating subscription to the output of the functional operation. The broadcasting computing entity then performs the functional operation, thereby generating and transmitting to the subscribing one of the plurality of listening computing entities the output of the functional operation. The subscribing one of the plurality of listening computing entities then performs an action using the output of the functional operation received.

Abstract: A build of a software solution that is cooperatively performed is automated. A broadcasting computing entity broadcasts an announcement of the functional operation of which the broadcasting computing entity is capable. Each of a plurality of listening computing entities connected to the network receives the announcement and compares the functional operation with a list of operational needs. After determining itself in need of performance of such a functional operation, a subscribing one of the plurality of listening computing entities transmits a response to the request indicating subscription to the output of the functional operation. The broadcasting computing entity then performs the functional operation, thereby generating and transmitting to the subscribing one of the plurality of listening computing entities the output of the functional operation. The subscribing one of the plurality of listening computing entities then performs an action using the output of the functional operation received.

Abstract: A method for merging buffers and associated operations includes receiving a compute graph for a reconfigurable dataflow computing system and conducting a buffer allocation and merging process responsive to determining that a first operation specified by a first operation node is a memory indexing operation and that the first operation node is a producer for exactly one consuming node that specifies a second operation. The buffer allocation and merging process may include replacing the first operation node and the consuming node with a merged buffer node within the graph responsive to determining that the first operation and the second operation can be merged into a merged indexing operation and that the resource cost of the merged node is less than the sum of the resource costs of separate buffer nodes. A corresponding system and computer readable medium are also disclosed herein.

Abstract: A build of a software solution that is cooperatively performed is automated. A broadcasting computing entity broadcasts a request to perform each of the functional operations in the selected set. Each of a plurality of listening computing entities connected to the network receives the request and determines capability of performing each of the functional operations in the selected set. After determining itself capable, a capable one of the plurality of listening computing entities transmits a response to the request indicating such capability, and then performs each of the functional operations in the selected set, thereby generating and transmitting to the broadcasting computing entity the output of the selected set of functional operations. The broadcasting computing entity then performs an action using the output of the functional operation received.

Abstract: Systems and methods for providing vehicle software deployment plans that include one or more fallback deployment plans are disclosed. In some embodiments, a vehicle software deployment system determines a deployment plan for deploying one or more software applications one or more electronic control units (ECUs) of a vehicle. Additionally, for one or more failure scenarios, the vehicle software deployment system determines one or more respective fallback deployment plans, such as a deployment plan that assumes one of the ECUs of the vehicle has failed. A deployment plan bundle is provided to the vehicle, comprising a primary deployment plan as well as the one or more fallback deployment plans. In the event that one of the failure scenarios takes place with respect to the vehicle, a deployment agent of the vehicle automatically deploys one of the fallback deployment plans that was provided with the deployment bundle.

Abstract: In various examples, techniques for performing software code verification are described. Systems and methods are disclosed for generating, using intermediate code and user input, a call graph that represents source code for software. For instance, the call graph represents at least functions (e.g., internal functions, external functions, etc.) associated with the software, calls (e.g., direct calls, call pointers, etc.) between the functions, and register information associated with the functions (e.g., variables used by the functions, assembly code used by the functions, etc.). The systems and methods may further use the call graph to perform software code verification by verifying rules from design specifications for the software and/or rules from various certification standards.

Abstract: The techniques disclosed herein enable systems to reduce the time required to terminate a set of microservices for an application while ensuring high availability and preventing request failures. This is accomplished through a termination manager which retrieves request queues for the microservices to analyze outstanding requests that require processing prior to termination. Based on the outstanding requests, the termination manager constructs call graphs for each request. The call graphs capture the operational flow of the associated request by defining a sequence of microservices whose functionality is invoked by the request. From an initial analysis, the termination manager can determine that some of the microservices do not appear in the call graphs, indicating that the microservices are not needed to process the outstanding requests. Accordingly, the unneeded microservices are terminated.