Abstract: The disclosure relates to system and method for facilitating designing of an Internet of Things (IoT) infrastructure for deploying an IoT application. The method includes determining a Manhattan distance between each of a plurality of existing requirements and a new requirement, identifying one or more of the plurality of existing requirements corresponding to a minimum Manhattan distance, determining a relevancy score for each of the one or more identified existing requirements based on a similarity between the each of the one or more identified existing requirements and the new requirement, and providing one or more IoT components and one or more IoT designs corresponding to a similar existing requirement for facilitating designing of the IoT infrastructure. The similar existing requirement comprises one of the one or more identified existing requirement with a maximum relevancy score.

Abstract: The disclosure relates to system and method for facilitating designing of an Internet of Things (IoT) infrastructure for deploying an IoT application. The method includes determining a Manhattan distance between each of a plurality of existing requirements and a new requirement, identifying one or more of the plurality of existing requirements corresponding to a minimum Manhattan distance, determining a relevancy score for each of the one or more identified existing requirements based on a similarity between the each of the one or more identified existing requirements and the new requirement, and providing one or more IoT components and one or more IoT designs corresponding to a similar existing requirement for facilitating designing of the IoT infrastructure. The similar existing requirement comprises one of the one or more identified existing requirement with a maximum relevancy score.

Abstract: The present disclosure is related to field of testing, disclosing method and system for automating generation of test data and associated configuration data for testing. A test data generating system retrieves test requirement data from a test plan related to an Application Under Test and translates the test requirement data into plurality of vectors. Subsequently, each of the plurality of vectors are provided to a trained Artificial Neural Network (ANN) to identify context associated with the plurality of vectors based on probabilities generated for each vector. The probabilities are generated by the trained ANN using the input. Further, at least one automation module is selected from a plurality of automation modules stored in a database based on the context, which is then executed to generate test data and configuration data for testing. The present disclosure makes the process of generating test data and configuration data, fast, efficient, accurate and reliable.