Fuzzy and ANN based model for Test case prioritization for Regression testing

Abstract

This research article performs the prioritization of the test case to test the software system after the occurrence of changes for Regression testing. The test expert here will categorize the sets as Optimistic test cases and Pessimistic test cases as formatted data for preprocessing by the Fuzzy rules. The optimistic test cases ensure that they are considered for regression testing by the tester. They are allowed to go into the next phase for deciding the prioritization. The test case is expected to have the details of case_id, case_name, case_details, predicted_result, obtained_result, seconds_time, and status. The ANN model deployed, gives the ranking to only Optimistic test cases by ensuring its capability to a dynamic environment. The efficiency of the regression testing on the proposed ANN model is evaluated by representing the faults, statements, and paths using the average percentage. The results provide a superior value above 95% when compared to the other methods taken in literature survey. The future scope of this ANN-based model can be used for prioritizing, selecting, and categorizing every cycle using reinforcement learning methods.