Abstract: The present disclosure relates to a method and a system for enabling self-maintainable test automation. In one embodiment, the system creates a test automation suite using historical test scenarios and automatically updates the test automation suite based on functional changes in one or more related applications. The system determines one or more reusable automation units and one or more test data units that are affected by the functional changes identified in test scenarios received as input and accordingly updates the one or more reusable automation units and one or more test data units. Thus, the system enables self-maintainable test automation, thereby eliminating the effort and expertise required to create automation test suite, build automation scripts, and modify automation scripts for future enhancements.

Abstract: The present disclosure relates to a method and a system for predicting stability of a project. A predictive analysis system receives data related to defect parameters, developer parameters, test case parameters, requirement parameters and production defect parameters of a project from one or more data sources. Thereafter, the predictive analysis system determines plurality of trends based on the received data. Further, the predictive analysis system determines a project release prediction value for the project based on at least one of the plurality of trends. Further, the predictive analysis system determines an impact value of the project based on at least one of the plurality of trends. Finally, the predictive analysis system determines a prediction result value based on the project release prediction value and the impact value for predicting the stability of the project.

Abstract: The present disclosure relates to a method and a system for optimizing stability of a project. A predictive analysis system receives data related to defect parameters, developer parameters, test case parameters, requirement parameters and production defect parameters of a project from data sources. Thereafter, the predictive analysis system determines plurality of trends based on the received data. Further, the predictive analysis system determines a project release prediction value for the project based on the plurality of trends. Further, the predictive analysis system determines an impact value of the project based on the plurality of trends. Further, the predictive analysis system determines a prediction result value based on the project release prediction value and the impact value for predicting the stability of the project. Further, the predictive analysis system dynamically performs one or more actions based on the prediction result value for optimizing the stability of the project.

Abstract: The present disclosure relates to a method and a system for enabling self-maintainable test automation. In one embodiment, the system creates a test automation suite using historical test scenarios and automatically updates the test automation suite based on functional changes in one or more related applications. The system determines one or more reusable automation units and one or more test data units that are affected by the functional changes identified in test scenarios received as input and accordingly updates the one or more reusable automation units and one or more test data units. Thus, the system enables self-maintainable test automation, thereby eliminating the effort and expertise required to create automation test suite, build automation scripts, and modify automation scripts for future enhancements.

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.