Abstract: Provided is a method and system (108) for building and leveraging a knowledge fabric (110) in a Software Development Lifecycle (SDLC). A plurality of SDLC artifacts are received from a plurality of heterogeneous data sources (102). The plurality of SDLC artifacts are then correlated to build an end-to-end correlation and are clustered to generate an SDLC knowledge fabric (110). This includes extracting semantic and contextual data from the plurality of SDLC artifacts using Natural Language Processing (NLP) and deep text analytics and transforming the extracted semantic and contextual data to knowledge graphs. One or more actionable items (112) are then derived using the SDLC knowledge fabric (110) and the one or more actionable items (112) are used to improve overall process efficiency and accelerate software delivery in the SDLC.

Abstract: Disclosed is a method and system for clustering a continuous stream of software development lifecycle (SDLC) artifacts. The method and system receives and preprocesses SDLC artifacts. Subsequently, a first set of topics is generated based on application of topic modelling and each SDLC artifact is clustered into a topic of the first set of topics. In the absence of topic count, a coherence process analyzes relationships and patterns within the data to determine the topic count. In case of a skewed distribution of records in the topics, skewed keywords are eliminated before rerunning the coherence process. Upon detecting an updated stream of SDLC artifacts, a second set of topics is generated. Thereafter, a similarity between the first set of topics and the second set of topics is evaluated, to perform re-clustering of the SDLC artifacts. The method and system also enables SMEs to verify and provide feedback for dynamic clustering.

Abstract: Disclosed is a method and system for clustering a continuous stream of software development lifecycle (SDLC) artifacts. The method and system receives and preprocesses SDLC artifacts. Subsequently, a first set of topics is generated based on application of topic modelling and each SDLC artifact is clustered into a topic of the first set of topics. In the absence of topic count, a coherence process analyzes relationships and patterns within the data to determine the topic count. In case of a skewed distribution of records in the topics, skewed keywords are eliminated before rerunning the coherence process. Upon detecting an updated stream of SDLC artifacts, a second set of topics is generated. Thereafter, a similarity between the first set of topics and the second set of topics is evaluated, to perform re-clustering of the SDLC artifacts. The method and system also enables SMEs to verify and provide feedback for dynamic clustering.

Abstract: Provided is a method and system (108) for building and leveraging a knowledge fabric (110) in a Software Development Lifecycle (SDLC). A plurality of SDLC artifacts are received from a plurality of heterogeneous data sources (102). The plurality of SDLC artifacts are then correlated to build an end-to-end correlation and are clustered to generate an SDLC knowledge fabric (110). This includes extracting semantic and contextual data from the plurality of SDLC artifacts using Natural Language Processing (NLP) and deep text analytics and transforming the extracted semantic and contextual data to knowledge graphs. One or more actionable items (112) are then derived using the SDLC knowledge fabric (110) and the one or more actionable items (112) are used to improve overall process efficiency and accelerate software delivery in the SDLC.