Abstract: A system and method for conversion of monolithic services to micro-services is provided. One or more features related to service associated with domain implemented by monolithic service source code are identified. Features are identified for creating first feature set. One or more features are determined related to dependencies and cross-dependencies amongst one or more service functions associated with service, implemented by monolithic service source code, and between service functions and entities associated with domain expressed in the monolithic service source code. One or more features related to dependencies and cross-dependencies are determined for creating second feature set. Relationship between features present in first feature set and second feature set is determined. The relationship is representative of similarity of the features present in first feature set and second feature set.

Abstract: A system and a method for automating unit test case including generating a codebase extract by analysing an application source code, identifying one or more potential executable paths within a selected class or method based on an analysis of the codebase extract, executing one or more statements associated with each of the one or more potential paths using a symbolic execution technique and identifying one or more calls to external dependencies associated with the respective paths, and subsequently, a hint for spying and mocking respective external dependencies is generated, as a by-product of symbolic execution on identification of the external dependencies, whereby each hint is updated with respective ideal return value, and a unit test case for testing a selected class or method is generated by processing the hints with test data and an exclusion list.

Abstract: A system and method for conversion of monolithic services to micro-services is provided. One or more features related to service associated with domain implemented by monolithic service source code are identified. Features are identified for creating first feature set. One or more features are determined related to dependencies and cross-dependencies amongst one or more service functions associated with service, implemented by monolithic service source code, and between service functions and entities associated with domain expressed in the monolithic service source code. One or more features related to dependencies and cross-dependencies are determined for creating second feature set. Relationship between features present in first feature set and second feature set is determined. The relationship is representative of similarity of the features present in first feature set and second feature set.

Abstract: A system and method for optimizing solution identification for a problem is provided. The invention comprises generating multiple solutions for the problem by selecting population of datasets from a search space. Further, fitness of each generated solution is determined by utilizing a fitness approximation model for selecting solutions from the generated solutions for generating a next generation of solutions until the solutions converge based on a pre-determined convergence target. Further, solutions from the converged solutions are filtered by utilizing a filtering model for performing a concrete evaluation. The concrete evaluation is performed for identifying productive solutions and unproductive solutions from the solutions filtered from the converged solutions. Further, a feedback based on the concrete evaluation is provided. Furthermore, the solution identification for a problem is carried out iteratively until a termination condition is met.

Abstract: A system and a method for automating unit test case generation is disclosed. The present invention provides generating a codebase extract by analysing an application source code. Further, one or more potential executable paths within a selected class or method are identified based on an analysis of the codebase extract. Furthermore, one or more statements associated with each of the one or more potential paths are executed using a symbolic execution technique and one or more calls to external dependencies associated with the respective paths are identified. Subsequently, a hint for spying and mocking respective external dependencies is generated, as a by-product of symbolic execution on identification of the external dependencies. Yet further, each hint is updated with respective ideal return value. Finally, a unit test case for testing a selected class or method is generated by processing the hints with test data and an exclusion list.

Abstract: A system for improving software code quality using artificial intelligence is provided. The system comprises a training data extraction module to extract learning data files from a source control management system and an integrated development environment for preparing training data. The system further comprises a machine learning model trainer that conducts training of an artificial neural network. The system further comprises a machine learning recommendation module that queries the trained artificial neural network to check for recommendations for improving quality of one or more new software codes and one or more modified software codes. The system also comprises a remediation module that determines one or more coding standard violations in the one or more new software codes and one or more modified software codes. The quality of the one or more new software codes and one or more modified software codes is improved by applying the recommendations.

Abstract: A system for improving software code quality using artificial intelligence is provided. The system comprises a training data extraction module to extract learning data files from a source control management system and an integrated development environment for preparing training data. The system further comprises a machine learning model trainer that conducts training of an artificial neural network. The system further comprises a machine learning recommendation module that queries the trained artificial neural network to check for recommendations for improving quality of one or more new software codes and one or more modified software codes. The system also comprises a remediation module that determines one or more coding standard violations in the one or more new software codes and one or more modified software codes. The quality of the one or more new software codes and one or more modified software codes is improved by applying the recommendations.