COMPARATIVE ANALYSIS OF TEST CASE PRIORITIZATION TECHNIQUES

Abstract

Software Testing is one of the phases of software development lifecycle that consumes a lot of time and effort. Once the software is delivered, it moves to the maintenance phase. In maintenance phase, it becomes very expensive to run all test cases available in the regression test suite to confirm the correct working of the software application or program in terms of budget and time. Also, even if a minor change is made to the program, it becomes very expensive if we have to run all available test cases to verify that no new errors have been introduced due to the change made. So, prioritization of the test cases becomes mandatory. Test Case Prioritization strategies help in overcoming these problems by prioritizing the test cases so that several parameters like statement coverage, fault detection rate etc are maximized. In this research work, a comparative analysis has been done amongst various strategies of test case prioritization by implementing them to prioritize the test cases for a software application. Based on the analysis done, a new strategy has been proposed in this thesis which uses map reduce algorithm and cuckoo search algorithm to cover all parameters for prioritizing test cases. Three strategies i.e. version-specific test case prioritization, genetic algorithm and cuckoo search were compared and based on the comparison of the results , the new strategy has been proposed. The proposed strategy covers all the possible parameters on the basis of which test cases can be prioritized and also, maximizes the code coverage of the application so that maximum number of defects can be identified from the software in mimimum amount of time. The proposed stategy is also suitable for test case prioritization of large software systems where the regression test suite contains hundreds and thousands of test cases.