Abstract: The present disclosure relates to a method and system for assessing quality of incremental heterogeneous data by a data quality assessing system. The data quality assessing system determines an incremental heterogeneous data from at least one data source, obtains details associated with the incremental heterogeneous data from the at least one data source, identifies type of the incremental heterogeneous data based on the details and pattern templates, selects one or more data quality tests from a plurality of data quality tests for the incremental heterogeneous data based on the identified type of the incremental heterogeneous data and determines a score for the incremental heterogeneous data based on the one or more data quality tests and user defined parameters to assess quality of heterogeneous incremental data.

Abstract: The present disclosure relates to a method and system for assessing quality of incremental heterogeneous data by a data quality assessing system. The data quality assessing system determines an incremental heterogeneous data from at least one data source, obtains details associated with the incremental heterogeneous data from the at least one data source, identifies type of the incremental heterogeneous data based on the details and pattern templates, selects one or more data quality tests from a plurality of data quality tests for the incremental heterogeneous data based on the identified type of the incremental heterogeneous data and determines a score for the incremental heterogeneous data based on the one or more data quality tests and user defined parameters to assess quality of heterogeneous incremental data.

Abstract: This disclosure relates generally to software testing, and more particularly to a system and method for optimizing testing of software production incidents. In one embodiment, the method comprises analyzing an incident ticket using a machine learning algorithm to identify one or more keywords in the incident ticket, and identifying a location of the incident ticket based on the one or more keywords, a test workspace corresponding to the incident ticket based on the location, and a plurality of specific test cases corresponding to the incident ticket based on the test workspace. The identification leads to a first scenario and a second scenario. In the first scenario, the method further comprises initiating a learning process based on intelligence gathered from a manual processing of the incident ticket. In the second scenario, the method further comprises executing the plurality of specific test cases in a test environment.

Abstract: This disclosure relates generally to software testing, and more particularly to a system and method for optimizing testing of software production incidents. In one embodiment, the method comprises analyzing an incident ticket using a machine learning algorithm to identify one or more keywords in the incident ticket, and identifying a location of the incident ticket based on the one or more keywords, a test workspace corresponding to the incident ticket based on the location, and a plurality of specific test cases corresponding to the incident ticket based on the test workspace. The identification leads to a first scenario and a second scenario. In the first scenario, the method further comprises initiating a learning process based on intelligence gathered from a manual processing of the incident ticket. In the second scenario, the method further comprises executing the plurality of specific test cases in a test environment.