Abstract: The system and method of the present disclosure uses the artificial intelligence in the model driven framework to bring productivity benefits in the model driven application development. It comprises an easy to use user interface to share context-specific questions to a user and to capture their responses. The system performs its user interactions based on the user's responses and the output from the recommendation module which are based on application models, user interaction history and the system usage pattern. The system interprets user inputs as one or more actions to be executed with the help of usage patterns in the model database and executes them and performs error identification and recovery when required. The results of execution or error recovery along with recommendations for subsequent actions are communicated back to the user through recommendation module.

Abstract: Conventional methods for application development are rigid, complex, costly and ineffective in implementing new and changing technologies. The embodiments herein provide a model driven system and method for development of micro service applications. A set of user interface components and one or more domain components for a plurality of micro services designs of a plurality of applications pertaining to one or more technologies are modeled and validated. Further, a plurality of codes are generated according to the one or more modeled domain components of the plurality of micro services designs of the plurality of applications satisfying one or more requirements. The plurality of generated codes are further customized and validated to ensure design and code consistency. The plurality of customized codes are further containerized to provide a plurality of containerized applications which are deployed into a target deployment environment for execution using a plurality of configuration files.

Abstract: Majority of the existing approaches handling NFRs limit themselves to model analysis whereas the method disclosed herein includes model driven code generation for NFR optimized models. The method and system disclosed herein provides a model driven approach to contradictory NFR resolution, which generates the NFR code into the functional application code. Embodiments herein provide automated and optimized code generation for contradictory NFRs. The method uses separate but related NFR and mitigation models. Further performs model to code transformation only after the Mitigation model is calculated based on NFR models. The method relies on model to code transformation for generating the application code and other artifacts for an optimized NFR. The approach provided is applicable till the application is deployed in an optimized environment.

Abstract: Conventional methods for application development are rigid, complex, costly and ineffective in implementing new and changing technologies. The embodiments herein provide a model driven system and method for development of micro service applications. A set of user interface components and one or more domain components for a plurality of micro services designs of a plurality of applications pertaining to one or more technologies are modeled and validated. Further, a plurality of codes are generated according to the one or more modeled domain components of the plurality of micro services designs of the plurality of applications satisfying one or more requirements. The plurality of generated codes are further customized and validated to ensure design and code consistency. The plurality of customized codes are further containerized to provide a plurality of containerized applications which are deployed into a target deployment environment for execution using a plurality of configuration files.

Abstract: Majority of the existing approaches handling NFRs limit themselves to model analysis whereas the method disclosed herein includes model driven code generation for NFR optimized models. The method and system disclosed herein provides a model driven approach to contradictory NFR resolution, which generates the NFR code into the functional application code. Embodiments herein provide automated and optimized code generation for contradictory NFRs. The method uses separate but related NFR and mitigation models. Further performs model to code transformation only after the Mitigation model is calculated based on NFR models. The method relies on model to code transformation for generating the application code and other artifacts for an optimized NFR. The approach provided is applicable till the application is deployed in an optimized environment.

Abstract: This disclosure relates generally to a system and method to generate model driven applications using an artificial intelligence. The system and method uses the artificial intelligence in the model driven framework to bring productivity benefits in the model driven application development. It comprises an easy to use user interface to share context-specific questions to a user and to capture their responses. The system performs its user interactions based on the user's responses and the output from the recommendation module which are based on application models, user interaction history and the system usage pattern. The system interprets user inputs as one or more actions to be executed with the help of usage patterns in the model database and executes them and also performs error identification and recovery when required. The results of execution or error recovery along with recommendations for subsequent actions are communicated back to the user through recommendation module.

Abstract: A method and system for automated UI testing through model driven techniques where the UI test case model is generated based on the selected UI model content. The system selects the UI model corresponding to which test case model is created and populated into a test case editor using a test case model creation module. In one embodiment, the test case model before being populated into the test case editor is created as a sequence of UI Actions based on a structure pattern of the selected UI model. In another embodiment, the test case editor displays and edits the test case model. A validation module, based on rules and constraints then validates the test case model against the corresponding UI model. Thereafter, a script generation module generates a test case script from the test case model for the selected UI model.

Abstract: A method and system for automated UI testing through model driven techniques where the UI test case model is generated based on the selected UI model content. The system selects the UI model corresponding to which test case model is created and populated into a test case editor using a test case model creation module. In one embodiment, the test case model before being populated into the test case editor is created as a sequence of UI Actions based on a structure pattern of the selected UI model. In another embodiment, the test case editor displays and edits the test case model. A validation module, based on rules and constraints then validates the test case model against the corresponding UI model. Thereafter, a script generation module generates a test case script from the test case model for the selected UI model.