Regression test suite study has been research topic for decades. In this paper we investigate the Regression test suite prioritization using residual test coverage algorithm for white box testing and introduce new statistical technique for black box testing. Our contribution in this paper is mainly problem solution to breaking the tie in residual coverage algorithm. Further we introduce new metric which can be used for prioritizing the regression test suite for black box testing. Towards the end part of the paper we get down to implementation details explaining how this can be done in the industrial or research project. The intended readers of this paper are developers and testers in the research field and practitioners of software engineering in the large scale industrial projects.

After applying techniques such as test design generation using combinatorial approach, test case selection, test suite minimization and prioritization, further reduction in execution time of the test suite is possible. This is necessary when there is scarcity of resources and time to execute the test suites. We investigate the statistical techniques which can be employed for analysis and reduction of test execution time further. The approach can help in extrapolating the execution time of test suite when the regression team wants to augment the test suite. The statistical techniques can aid in choosing the best test setup in terms of operating system, tool and Java virtual machine combination for given test setup. Further, these techniques are not the activities that need to be carried out before every execution of the test suite. Once the best combination is chosen the same can be used unless there is change in one of the layers of test setup. The activities mentioned in the paper can be part of test setup planning and maintenance phase.