Methods, apparatus, systems, and articles of manufacture are disclosed to identify code semantics. An example apparatus includes processor circuitry to perform at least one of first, second, or third operations to instantiate validated repository parse circuitry to identify embedding values corresponding to validated code, syntax analysis circuitry to identify syntax information based on statistical recurrence metrics of the embedding values, bidirectional model circuitry to train a forward semantic model and a backward semantic model based on (a) semantic information corresponding to the syntax information and (b) divisional segmentation information corresponding to the syntax information, and target repository mining circuitry to generate target code model input fragments including learned syntactic information, learned semantic information, and learned divisional segmentation information, the target code model input fragments to facilitate inference with the forward semantic model and the backward semantic model.

A GUI testing device may be configured to execute a testing state machine for interacting with a software application to generate an initial screen of a GUI. The GUI testing device may be configured to determine a current state in the testing state machine based upon a matching trigger target in the initial screen to a given state. The current state may include an operation, and the operation may associate with a trigger target to operate on. The trigger may include a source state, a destination state, and a trigger target. The operation may include a user input operation, and an operation trigger target. The GUI testing device may be configured to perform the operation on the matching trigger target in the initial screen to generate a next screen of the GUI, and advance from the current state to a next state based upon the trigger.

Methods and systems are described herein for integrating model development control systems and model validation platforms. For example, the methods and systems discussed herein recite the creation and use of a model validation platform. This platform operates outside of the environment of the independently validated models as well as the native platform into which the independently validated models may be incorporated. The model validation platform may itself include a model that systematically validates other independently validated models. The model validation platform may then provide users substantive analysis of a model and its performance through one or more user interface tools such as side-by-side comparisons, recommended adjustments, and/or a plurality of adjustable model attributes for use in validating an inputted model.

A system for facilitating analysis of a software product is provided. The system includes processing circuitry that collects data logs from various technologies associated with at least one stage of a software development life cycle (SDLC) of the software product. The processing circuitry identifies entities associated with each collected data log, and standardizes the collected data logs such that each collected data log is standardized based on standard data formats associated with the identified entities. Each entity corresponds to at least one stage of the SDLC of the software product. Further, the processing circuitry updates data models associated with the entities based on the standardized data logs and generates a unified data model that is indicative of a correlation between the entities. Based on the correlation indicated by the unified data model, the processing circuitry executes an automated action.

A method for testing software applications in a system under test (SUT) includes building a reference model of the SUT that defines a computer-based neural network. The method includes training the reference model using input data and corresponding output data generated by the SUT, selecting an output value within a domain of possible output values of the SUT representing an output that is not represented in the output data used to train the reference model, applying the selected output value to the reference model, and tracing the selected output through the reference model to identify test input values that when input to the reference model, produce the selected output value. The method can further include using the identified test input values to test the system under test.

A debugging tool and method for statically verifying programs that invoke web-based services through API calls is provided. The tool receives source code that comprises one or more invocation of web APIs for requesting web-based services. The tool also receives a set of web API specifications. The tool extracts a set of request information for each web API invocation in the source code, the set of request information including a usage string of an URL endpoint. The tool verifies whether the set of request information complies with the received web API specifications and reports a result of the verification.

Provided is an onboard ECU for controlling an onboard apparatus installed in a vehicle, including a storage unit that stores a control program for controlling the onboard apparatus and an inspection program for performing an operation check of the onboard apparatus or the onboard ECU, and a control unit that executes the control program or the inspection program, the storage unit storing validity information indicating whether the inspection program is valid or invalid being, and the control unit referring to the validity information stored in the storage unit, executing the inspection program if the inspection program is valid, and executing the control program if the inspection program is invalid.

Disclosed herein are system, method, and computer program product embodiments for providing an architecture to support a semantic validation technique. The system includes a governance console that carries out data management functionalities to support the validation. Such functionalities include generating, storing and publishing validation profiles that are used by a validation service for validating an asset, a validation reporter that receives and stores validation reports and performs notification functions to notify relevant individuals of the validation results, as well as a profile runner and associations manager that directly support the validation service.

Computing systems, devices, and associated methods of detecting application regression in a distributed computing system are disclosed herein. In one embodiment, a method includes receiving data representing telemetry records from one or more hosts of the distributed computing system. At least some of the telemetry records are exception records individually indicating an operation by a user application has failed during execution. The method also includes determining a failure rate of executing the operation by the user application while compensating for a workload of the user application in the distributed computing system. A comparison is performed between the determined failure rate and a threshold. Based on the performed comparison, a regression notification can be generated to indicate that application regression has occurred notwithstanding the workload of the user application in the distributed computing system.

A system for returning synthetic database query results. The system may include a memory unit for storing instructions, and a processor configured to execute the instructions to perform operations comprising: receiving a query input by a user at a user interface; determining, based on natural language processing, a type of the query input; determining, based on the received query input and a database language interpreter, an output data format; returning, based on a generation model and the output data format, a result of the query input; providing, to a plurality of training models and based on the determined query type, the query input and the result; and training the training models, based on the query input and the result.