A method, a computer program product, and a system for allocating a variable into storage class memory during compilation of a program. The method includes selecting a variable recorded in a symbol table during compilation and computing a variable size of the variable by analyzing attributes related to the variable. The method further includes computing additional attributes relating to the variable. The method also includes computing a control flow graph and analyzing the control flow graph and the additional attributes to determine an allocation location for the variable. The method further includes allocating the variable into a storage class memory based on the analysis performed.

A method, a computer program product, and a system for allocating a variable into storage class memory during compilation of a program. The method includes selecting a variable recorded in a symbol table during compilation and computing a variable size of the variable by analyzing attributes related to the variable. The method further includes computing additional attributes relating to the variable. The method also includes computing a control flow graph and analyzing the control flow graph and the additional attributes to determine an allocation location for the variable. The method further includes allocating the variable into a storage class memory based on the analysis performed.

A method for model production includes acquiring a related operation for model production from a user interface layer of a model production system, and determining a software platform of the model production system; acquiring a model service corresponding to the related operation by invoking an application programming interface (API) corresponding to the related operation, wherein the API is located between the user interface layer and other layer in the model production system; performing the model service by invoking local resources of the software platform with a tool of the software platform adapted to the model service, to generate a target model; and applying the target model in a target usage scene.

A method for model production includes acquiring a related operation for model production from a user interface layer of a model production system, and determining a software platform of the model production system; acquiring a model service corresponding to the related operation by invoking an application programming interface (API) corresponding to the related operation, wherein the API is located between the user interface layer and other layer in the model production system; performing the model service by invoking local resources of the software platform with a tool of the software platform adapted to the model service, to generate a target model; and applying the target model in a target usage scene.

Web-based robotic process automation (RPA) designer systems that allow RPA developers to design and implement web serverless automations, user interface (UI) automations, and other automations are disclosed. Such web-based RPA designer systems may allow a developer to sign in through the cloud and obtain a list of template projects, developer-designed projects, services, activities, etc. Thus, RPA development may be centralized and cloud-based, reducing the local processing and memory requirements on a user's computing system and centralizing RPA designer functionality, enabling better compliance. Automations generated by the web-based RPA designer systems may be deployed and executed in virtual machines (VMs), containers, or operating system sessions.

Embodiments of the present disclosure relate to applications and platforms for configuring machine learning models for training and deployment using graphical components in a development environment. For example, systems and methods are disclosed that relate to determining one or more machine learning models and one or more processing operations corresponding to the one or more machine learning models. Further, a model component may be generated using the one or more machine learning models, the one or more processing operations, and one or more extension libraries in which the one or more extension libraries indicate one or more deployment parameters related to the one or more machine learning models. The model component may accordingly provide data that may be used to be able to use and deploy the one or more machine learning models.

A computing device for interpreting a data model algorithm includes an object searcher, an interpreter, and a translator. The object searcher is configured to search for attributes within datasets generated from at least one method of an instantiation of the data model algorithm in a development mode workflow. The interpreter is configured to evaluate the attributes, identify attributes having a use type, identify the type information of the identified attribute, and create data schema using the identified attributes and type information. The use type can be determined based on attribute values or an interface type associated with an identified attribute. The translator is configured to compare the data schema with another data schema in response to selecting the data model algorithm for inclusion in a production mode workflow.

A computing device for interpreting a data model algorithm includes an object searcher, an interpreter, and a translator. The object searcher is configured to search for attributes within datasets generated from at least one method of an instantiation of the data model algorithm in a development mode workflow. The interpreter is configured to evaluate the attributes, identify attributes having a use type, identify the type information of the identified attribute, and create data schema using the identified attributes and type information. The use type can be determined based on attribute values or an interface type associated with an identified attribute. The translator is configured to compare the data schema with another data schema in response to selecting the data model algorithm for inclusion in a production mode workflow.

A system and method of the disclosure relates to structuring at least one resource of an application programming interface (API) for a server. In the method, a plurality of field-level elements may be defined. Each of the plurality of field-level elements may be associated with a respective property. Also, first and second message-level elements may be defined. The first message-level element may be based on a first field-level element of the plurality of field-level elements, and the second message-level element may be based on the first field-level element. The API may be associated with a first resource having properties resolved based on at least the first and second message-level elements.

A transfer learning system is used for the development of neural transformer models pertaining to software engineering tasks. The transfer learning system trains source code domain neural transformer models with attention in various configurations on a large corpus of unsupervised training dataset of source code programs and/or source code-related natural language text. A web service provides the trained models for use in developing a model that may be fine-tuned on a supervised training dataset associated with a software engineering task thereby generating a tool to perform the software engineering task.