Abstract: Embodiments of present disclosure relates to method training data generation system for generating training data for classifying intent in conversational system. The training data generation system receives database schema and creates SQL/NoSQL queries. The training data generation system generates natural language queries for the SQL/NoSQL queries. Further, the training data generation system generates training data for intents associated with the natural language queries and provides to classification models associated with conversational system for classification of intents. Embodiments of present disclosure relates to method and conversational system for providing natural language response for query. The conversational system receives query from user and classifies intent of the query and provides relevant response by mapping the query with the SQL/NoSQL queries generated by the training data generation system.

Abstract: Disclosed herein is a method and system for determining safety compliance level of a software product. Safety requirements to be complied by the software product are extracted from safety standards. The extracted safety requirements are mapped with corresponding software product artifacts associated with the software product. Compliance of each of the one or more software product artifacts is determined based on the mapping. Thereafter, a score is assigned to of the software product artifacts that are in compliance with the safety requirements. Finally, the safety compliance level of the software product is determined based on the score assigned to the software product artifacts. In an embodiment, the above method helps in an easy interpretation of the standard safety procedures using a Natural Language Processing (NLP) and a Semantic analysis technique. Also, the method minimizes the chances of rejections and/or multiple iterations during safety certification of the software product.

Abstract: Disclosed herein is a method and system for determining safety compliance level of a software product. Safety requirements to be complied by the software product are extracted from safety standards. The extracted safety requirements are mapped with corresponding software product artifacts associated with the software product. Compliance of each of the one or more software product artifacts is determined based on the mapping. Thereafter, a score is assigned to of the software product artifacts that are in compliance with the safety requirements. Finally, the safety compliance level of the software product is determined based on the score assigned to the software product artifacts. In an embodiment, the above method helps in an easy interpretation of the standard safety procedures using a Natural Language Processing (NLP) and a Semantic analysis technique. Also, the method minimizes the chances of rejections and/or multiple iterations during safety certification of the software product.