Abstract
Software testing being one of the major phases of the software development life cycle is critical in delivering reliable software products. Traditional manual GUI testing has severe limitations like insufficient test coverage, labor intensiveness, complexity involved and cost ineffectiveness. "How much testing is enough or sufficient?" still remains a challenging question. Coverage analysis helps to guide the test engineers regarding test coverage and is used extensively to determine the effectiveness of selected testing methodology. The problems mentioned above related to manual GUI testing prompted the need for automation of GUI testing and coverage analysis. With the rapid emergence of GUI based context free applications, automated testing tools seems less effective. Recently, it has been learnt that concurrent and distributed machines based cluster can be used to reduce the required effort to test GUI in context of time. Proposed system use two different evolutionary algorithms concurrently to gain multiple objectives, based on the fitness functions of maximization of GUI test path coverage (measured as the number of events in an event sequence tested by a particular test case) and simultaneously minimizing the number of test cases. For coverage analysis, two of the very well-known multi objective algorithms; NSGA II and MOPSO from evolutionary domain have been employed. Experiments results of coverage analysis show that accuracy to the tune of 85% was achieved in MOPSO and NSGA II. This high level of accuracy is an indicator of usefulness of proposed fitness function. Both algorithms execute more than 90% of test paths.