A method begins by generating application system state transitions from inputted requirements and parameters. For a current implementation of generating application code, the method continues by entering a loop. The loop begins by generating a current intermediate result based on a previous implementation and in accordance with current application code development factors. The loop continues by generating at least one test case based on the one or more of the application system state transitions. The loop continues by testing the current intermediate result in accordance with the at least one test case. When the testing is unfavorable, the loop continues by modifying one or more of: the one or more of the plurality of application system state transitions, the one or more of the parameters, and the one or more implementation tools. The loop then continues by repeating the loop using the modified current application code development factors.

A method, system and computer program product for detecting potential failures in a continuous delivery pipeline. A machine learning model is created to predict whether changed portion of codes under development at various stages of the continuous delivery pipeline will result in a pipeline failure. After creating the machine learning model, log file(s) may be received that were generated by development tool(s) concerning a changed portion of code under development at a particular stage of the continuous delivery pipeline. The machine learning model provides relationship information between the log file(s) and the changed portion of code. A message is then generated and displayed based on this relationship information, where the message may provide a prediction or a recommendation concerning potential failures in the continuous delivery pipeline. In this manner, the potential failures in the continuous delivery pipeline may be prevented without requiring context switching.

A method, system and computer program product for detecting potential failures in a continuous delivery pipeline. A machine learning model is created to predict whether changed portion of codes under development at various stages of the continuous delivery pipeline will result in a pipeline failure. After creating the machine learning model, log file(s) may be received that were generated by development tool(s) concerning a changed portion of code under development at a particular stage of the continuous delivery pipeline. The machine learning model provides relationship information between the log file(s) and the changed portion of code. A message is then generated and displayed based on this relationship information, where the message may provide a prediction or a recommendation concerning potential failures in the continuous delivery pipeline. In this manner, the potential failures in the continuous delivery pipeline may be prevented without requiring context switching.

A system and computer-implemented method for translating diagramming data from a diagramming tool into implementation data for direct implementation by an implementation tool for implementing a component, and for translating the implementation data back into the diagramming data for direct visualization by the diagramming tool. The diagramming tool generates the diagramming data. A translating tool receives the diagramming data, reads, validates, and translates it directly into the implementation data, and saves the implementation data in an export file. The implementation tool receives the export file and uses the implementation data to implement the component. The translating tool can also translate the implementation data directly back into the diagramming data, and save the diagramming data in an import file. The diagramming tool receives the import file and uses the diagramming data to visualize the diagram of the component. The component may be physical or virtual, and part of information technology infrastructure.

Provided are systems and methods for transforming an operation-centric API into a graph-based API. In one example, a method may include receiving a description of an application programming interface (API), translating the description into a proxy model that comprises a list of a plurality of operations performed by the API, executing one or more heuristic programs on the proxy model to determine a plurality of entities associated with the list of operations and relationships among the plurality of entities, generating a graph API based on the plurality of entities and the relationships among the plurality of entities, wherein the graph API comprises a plurality of nodes representing the plurality of entities and edges between the plurality of nodes representing the relationships between the plurality of entities, and storing the graph API in a storage.

A system for CI/CD of AI/ML based components includes a cloud infrastructure which receives multiple new requirements from a vehicle designer or a developer, with the new requirements adapted for artificial intelligence/machine learning (AI/ML) based components of a vehicle. A dataset is provided. A metamorphic relations (MR) module receives input information from the dataset and sends MR information to the dataset. A components requirements database includes the new requirements in addition to existing requirements for the AI/ML based components. The MR module also receives components requirements data from the components requirements database and sends the MR information to the components requirements database. An AI/ML algorithm analyzes the input information from the dataset and prepares an updated component dataset.

A system and method automatically ensures consistency among a design model, an interface specification and one or more tests that test the design model. The system may include a broker adapted to construct the interface specification. The interface specification identifies the interface of the design model, e.g., its external inputs, external outputs, and initialization settings. It may also identify the outputs, inputs, and initialization setting objects of the tests. Proposed changes to any one of the design model's interface, the interface specification and the interfaces of the tests may be captured by the broker, and applied to the other two.

Disclosed are techniques for two-way synchronization of infrastructure-as-code templates and instances, including a method comprising detecting changes to a run-time state of a system and, in response to detecting a change, triggering an update of a current run-time state model. The method may further comprise, in response to updating the run-time state model, comparing the updated model to a current model using a template in a local repository instantiated as the current model. The method may further comprise, in response to the comparison determining a structural difference between the updated model and the current model, merging the updated model and the current model into a new model; and updating a local clone of a repository of the template with the new model. The method may further comprise, in response to the comparison determining no structural difference between the updated model and the current model, pushing changes to a remote repository.

The present technology adds code to a top level build configuration file of a configuration program that will gather metrics for each invocation of a build. These metrics are sent to a commonly accessible metric server for future analysis. The metrics are collected for a distributed engineering team over several machines. Compilation time metrics may then be collected for each compilation event and those metrics are analyzed by a common aggregator.

A method of evaluating compatibility of a first system component of a vehicle, the method comprising the steps of: providing a database comprising: vehicle platform configuration information comprising configuration information about two or more vehicle models, wherein each vehicle model comprises one or more aspect domains, wherein each aspect domain comprises one or more system components, wherein each system component comprises configuration information; wherein at least two system components of two different vehicle models belong to the same aspect domain; determining a compatibility result between said first system component and at least one other system component in said vehicle platform by comparing the configuration information of said first system component to the configuration information of said at least one other system component; and returning a compatibility result based on if said first system component is determined to be compatible with said at least one other system component.