Test Case Prioritization using Particle Swarm Optimization Algorithm with three Parameters (T-0731) (MFN 6191)

Abstract

Regression testing determines correctness of software under test and checks for bugs that have emerged with change in software. Running all test cases is time consuming and expensive. Thus, software testers tend to order test cases such that highest priority ones are run first for regression testing. The prioritization of test cases helps in early fault detection and increases in effectiveness in meeting certain performance goals. In our research, we propose the weighted objective function working alongside particle swarm algorithm for test case prioritization. The aim of our work is to make an effective strategy to prioritize test cases in regression testing by making use of three factors for prioritization. We obtain these factors by a survey and are: cumulative fault severity, fault detection and time factor. An experiment has been performed on 2 medium sized projects and compared results with the works of Manika et al. The results showed that our proposed algorithm is more effective for test case prioritization.