Applying Particle Swarm Optimization to Prioritizing Test Cases for Embedded Real Time Software Retesting

Abstract

In recent years, complex embedded systems are used in every device that is infiltrating our daily lives. Since most of the embedded systems are multi-tasking real time systems, the task interleaving issues, dead lines and other factors needs software units retesting to follow the subsequence changes. Regression testing is used for the software maintenance that revalidates the old functionality of the software unit. Testing is one of the most complex and time-consuming activities, in which running of all combination of test cases in test suite may require a large amount of efforts. Test case prioritization techniques can take advantage that orders test cases, which attempts to increase effectiveness in regression testing. This paper proposes to use particle swarm optimization (PSO) algorithm to prioritize the test cases automatically based on the modified software units. Regarding to the recent investigations, PSO is a multi-object optimization technique that can find out the best positions of the objects. The goal is to prioritize the test cases to the new best order, based on modified software components, so that test cases, which have new higher priority, can be selected in the regression testing process. The empirical results show that by using the PSO algorithm, the test cases can be prioritized in the test suites with their new best positions effectively and efficiently.