The AI engine operates with the common API. The common API supports i) any of multiple different training sources and/or prediction sources installed on ii) potentially different sets of customer computing hardware in a plurality of on-premises' environments, where the training sources, prediction sources as well as the set of customer computing hardware may differ amongst the on-premises' environments. The common API via its cooperation with a library of base classes is configured to allow users and third-party developers to interface with the AI-engine modules of the AI engine in an easy and predictable manner through the three or more base classes available from the library. The common API via its cooperation with the library of base classes is configured to be adaptable to the different kinds of training sources, prediction sources, and the different sets of hardware found a particular on-premises environment.

A method and system for migrating Extensible Markup Language (XML) schemas between releases of a computing application. The method provides first and second versions of an XML document by the computing application, each version having a different schema. The first version is migrated to the second version using a migration step. The method uses a Dependency injection Framework to abstract the characteristics of the at least one migration step. The method also transforms the first schema to the second schema, based on the abstracted characteristics of the at least one migration step, in such a way that the first version of the XML document is migrated into the second version of the XML document. The method migrates the first version into the second version in such a way that the second version can access application data from the first version.

Systems and methods, as well as computing architecture for implementing the same, for decoy injection into an application. The systems and methods include splitting a standard test phase operation into two complementary phases, and add new unit tests to the process, dedicated to testing the proper coverage of the decoys and avoiding non-regression of the original code.

A new feature can be defined for an application. Data pertaining to layer information, feature information and dependency information for the application can be processed and, based on the processing, an identification can be made of a highest layer of a plurality of layers of the application containing at least a second feature upon which the new feature is dependent. A recommendation can be generated. The recommendation can indicate to place the new feature in the highest layer of the plurality of layers containing the at least the second feature upon which the new feature is dependent. The recommendation can be communicated to a user interface, which can render the recommendation on a display.

Methods and systems are disclosed that automate and institutionalize many aspects of the process of creating software. Embodiments automate aspects of pricing, software creation, and delivery using a manufacturing-styled approach to development that reuses existing code and other existing software design features.

This disclosure provides a method, a computing system and a computer program product for optimizing computer-readable commands. The method includes detecting an intention of a set of commands based on a semantic model. The semantic model represents the set of commands and contexts of execution of the set of commands. The method further includes obtaining a predetermined semantic sub-model associated with the intention and updating the semantic model with the predetermined semantic sub-model. The method further includes generating a script of commands based on the updated semantic model.

Systems and methods, as well as computing architecture for implementing the same, for decoy injection into an application. The systems and methods include splitting a standard test phase operation into two complementary phases, and add new unit tests to the process, dedicated to testing the proper coverage of the decoys and avoiding non-regression of the original code.

A method for automatically enhancing security and fixing security vulnerabilities in the source code of a computer program in an object oriented run time environment includes evaluating the source code file of a monitored computer program. The source code file includes a plurality of class files. Each session includes two or more session segments. A security assessment on each of the plurality of class files is performed to identify one or more potential security issues associated with the plurality of class files. One or more security controls configured to address the identified potential security issues are automatically injected into a source code of one or more class files identified as having potential security issues. The automatically modified source code file of the monitored computer program is deployed to the run-time environment.

Techniques for generating plug-in application recipe (PIAR) extensions are disclosed. A PIAR management application discovers a particular data type within one or more data values for a particular field of a plug-in application, where the particular data type is (a) different from a data type of the particular field as reported by the plug-in application and (b) narrower than the data type of the particular field while complying with the data type of the particular field. The PIAR management application identifies one or more mappings between (a) the particular data type and (b) one or more data types for fields accepted by actions of plug-in applications. The PIAR management application presents a user interface including one or more candidate PIAR extensions based on the mapping(s). Based on a user selection of a candidate PAIR extension, the PIAR management application executes a PIAR that includes the selected PIAR extension.

Quantum entanglement protection is disclosed. An entanglement checker receives, from a requestor, a request associated with a first qubit. In response to receiving the request, the entanglement checker accesses qubit entanglement information that identifies an entanglement status of the first qubit. The entanglement checker determines, based on the qubit entanglement information, the entanglement status of the first qubit, and sends a response to the requestor based on the entanglement status.