Abstract: Conversations with software applications have been an integral part of day-to-day activities. For a smooth conversations, it is necessary for a software to automatically help in understanding another software. A method and system for enabling conversational reverse engineering and understanding of software application has been provided. The present disclosure proposes a solutions which is configured to build a dynamic knowledge base for a bot to learn from an input application source code. The system and method are further configured to provide functional/domain context-based question interpretation and mapping of the information to the dynamically built knowledge base. A user query is converted to a proprietary question model. Further, a question verb, primary entity and secondary entity are extracted through natural language processing.

Abstract: Code translation is an evolving field and due to advancements in the infrastructure and compute power. The existing methods for code translation are time and effort intensive. A method and system for translation of codes based on the semantic similarity have been provided. A machine learning model is developed, that understands and encapsulates the semantics of the code in the source side and translates the semantic equivalent code which is more maintainable and efficient compared to one to one translation. The system is configured to group a plurality of statements present in the source code together into blocks of code and comprehend the semantics of the block. The system is also trained to understand syntactically different but semantically similar statements. While understanding the semantics of the block and translating, the unused/duplicate code etc. gets eliminated. The translated code is better architected and native to the target environment.

Abstract: Natural language elements are present in both the executable lines and non-executable lines of the code. Rich information hidden within them are often ignored in code analysis as extraction of meaningful insights from its raw form is not straight forward. A system and method extracting natural language elements from an application source code is provided. The disclosure provides a method for performing detailed analytics on the natural language elements, classify those using deep learning networks and create meaningful insights. The system understands the different type of natural language elements, comment patterns present in the application source code and segregates the authentic comments having valuable insights, version comments, data element level comments from other non-value adding comments.

Abstract: A Mainframe batch system have various type of source components and each component has its own purpose and functionality within the environment. Existing refactoring are still dependent on manual skills and competency. A method and system for automated refactoring of mainframe based batch systems to cloud native environment is provided. The present disclosure proposes an intelligent automation model that comprehends every aspect of the existing Mainframe batch system, all its inherent source elements along with its dependencies. With this holistic understanding of all the elements of the Mainframe batch system, the system converts the information within them into a proprietary conceptual model. This model has all the information about the source elements is converted into the target architecture which is cloud native. The combination of the model with the externalizable command based templates (EBCT) aligns the conversion to any target technology feasible.

Abstract: To understand/reverse engineer the code, knowledge of cryptic terms (variable names) present in the code is mandatory. The reverse engineering to understand the code is a very complex task which has infinite variations. The present disclosure provides a method and system for identifying meaningful terms in a domain context from a plurality of cryptic forms of a variable name in a program code. The present disclosure provides a machine learning model that understands the cryptic form of a variable name and relates the co-occurring cryptic terms and expands them. These expanded forms of cryptic terms directly aid in understanding of each term and its usage in a more accurate way. This knowledge is used in many downstream task of reverse engineering the program code. This disclosure links the multiple usages of the same variable and aims to reduce the gap of naming convention mismatches introduced by developers.

Abstract: Code translation is an evolving field and due to advancements in the infrastructure and compute power. The existing methods for code translation are time and effort intensive. A method and system for translation of codes based on the semantic similarity have been provided. A machine learning model is developed, that understands and encapsulates the semantics of the code in the source side and translates the semantic equivalent code which is more maintainable and efficient compared to one to one translation. The system is configured to group a plurality of statements present in the source code together into blocks of code and comprehend the semantics of the block. The system is also trained to understand syntactically different but semantically similar statements. While understanding the semantics of the block and translating, the unused/duplicate code etc. gets eliminated. The translated code is better architected and native to the target environment.

Abstract: Most of the existing production applications in different domains are still running on. Mainframe applications in production receive data from various resources and process these data within. Understanding the structure of input data and output data is extremely important. A method and system for machine learning based understanding of a plurality of data elements in a mainframe program code has been provided. The method discloses a machine learning model that understands the structure of data elements in a Mainframe program code. The model considered is a graph neural network based architecture model. The disclosed method replicates memory mapping happening in the application program environment. The method understands the structure of the data element and the impact created by each data element on other data elements in the application and interfacing applications. The disclosed solution serves as a building block in problems such as code translation, reverse engineering etc.

Abstract: The application source code generally does not have any uniformity, defined executional sequence or documented information on the underlying complexity. It varies based on the requirement, domain and from each developer to developer. The slightest mistake in analyzing/modifying it will have a huge impact for the existing functionalities and interfacing applications. A method and system for inferencing code logic out of an application source code has been provided. This starts with the conversion of the raw form of code into logically linked blocks. These logical blocks in-turn represent standard meaningful representation of the code logic and are processed into vectors. Further, the processed vectors are fed into pre-trained machine learning models from which the code logic is predicted. Further the system and method can also be extended to various other applications in application maintenance, aiding the SME, inducting a new resource, documentation etc.

Abstract: The present disclosure relates to a method for automated extraction of rules in a software application code. The method discloses extracting rules embedded in the software application source codes based on a control flow analysis and a data flow analysis. Further, the extracted rules are translated into a target defined format based on mapping of parameters associated with the extracted rules with a pre-stored meta data. The translated rules are analyzed to obtain a validated set of rules.

Abstract: The application source code generally does not have any uniformity, defined executional sequence or documented information on the underlying complexity. It varies based on the requirement, domain and from each developer to developer. The slightest mistake in analyzing/modifying it will have a huge impact for the existing functionalities and interfacing applications. A method and system for inferencing code logic out of an application source code has been provided. This starts with the conversion of the raw form of code into logically linked blocks. These logical blocks in-turn represent standard meaningful representation of the code logic and are processed into vectors. Further, the processed vectors are fed into pre-trained machine learning models from which the code logic is predicted. Further the system and method can also be extended to various other applications in application maintenance, aiding the SME, inducting a new resource, documentation etc.

Abstract: The ability to comprehend the context of a given programming artifact and extracting the underlying functionality is a complex task extending beyond just syntactic and semantic analysis of code. All existing automation capabilities, hence heavily depend on manual involvement of domain experts. Even recent approaches leveraging Machine Learning Capabilities are supervised techniques, whereby the dependency on domain experts still remains—in preparing suitable training sets. A method and system for automated classification of variables using unsupervised distribution agnostic clustering has been provided. The present disclosure focuses to tap the flexibility of the code and presents a domain agnostic approach using unsupervised machine learning which automatically extracts the context from source code, by classifying the underlying elements of the code. The method and system do not require any manual intervention and opens a wide range of opportunities in reverse engineering and variable level analysis space.

Abstract: Natural language elements are present in both the executable lines and non-executable lines of the code. Rich information hidden within them are often ignored in code analysis as extraction of meaningful insights from its raw form is not straight forward. A system and method extracting natural language elements from an application source code is provided. The disclosure provides a method for performing detailed analytics on the natural language elements, classify those using deep learning networks and create meaningful insights. The system understands the different type of natural language elements, comment patterns present in the application source code and segregates the authentic comments having valuable insights, version comments, data element level comments from other non-value adding comments.

Abstract: The ability to comprehend the context of a given programming artifact and extracting the underlying functionality is a complex task extending beyond just syntactic and semantic analysis of code. All existing automation capabilities, hence heavily depend on manual involvement of domain experts. Even recent approaches leveraging Machine Learning Capabilities are supervised techniques, whereby the dependency on domain experts still remains—in preparing suitable training sets. A method and system for automated classification of variables using unsupervised distribution agnostic clustering has been provided. The present disclosure focuses to tap the flexibility of the code and presents a domain agnostic approach using unsupervised machine learning which automatically extracts the context from source code, by classifying the underlying elements of the code. The method and system do not require any manual intervention and opens a wide range of opportunities in reverse engineering and variable level analysis space.

Abstract: Legacy codes of software applications are required to be modernized and migrated to the latest technology. Migration of legacy codes requires extraction of hidden rules comprised in the application code and translating them to meaningful output which is cumbersome. Thus an intelligence in the entire rule extraction and translation process is required for reducing the complexity and risk. The present disclosure provides automated extraction of rules embedded in software application code using machine learning technique(s) (MLT). In the present disclosure, rules embedded in the software application source codes are extracted based on a control flow and data flow analysis. Further, the extracted rules are translated into a target defined format based on mapping of parameters associated with extracted rules with a pre-stored meta data wherein the mapped parameters are classified into one or more categories using the MLT. The translated rules are analyzed to obtain a validated set of rules.