Multi-Objective Test Case Prioritization based on Epistatic Particle Swarm Optimization

doi:10.23940/ijpe.18.10.p20.24412448

Abstract

Abstract:

To address the Multi-Objective Test Case Prioritization (MOTCP) problem, an Epistatic Particle Swarm Optimization (EPSO) algorithm is presented. The epistasis in biology is introduced into the new algorithm, and the particles are updated based on the crossover of Epistatic Test Case Segment (ETS) in the test case sequence. The average coverage percentage of program entity and effective execution time of the test case sequence are set as two objective fitness functions in EPSO. The experiment selects four typical open12 source projects as benchmark programs. We adopted Average Percentage of Branch Coverage (APBC) and Effective Execution Time (EET) as objective fitness. The four classical Java testing projects results show that the EPSO is more effective and more diverse than single-point PSO and order PSO. The EPSO algorithm efficiently solves the MOTCP problem by promoting early detection of software defects and reducing software testing costs in regression testing.

Submitted on July 8, 2018; Revised on August 10, 2018; Accepted on September 12, 2018
References: 18

Jiaze Sun, Jingmin Chen, and Gang Wang. Multi-Objective Test Case Prioritization based on Epistatic Particle Swarm Optimization [J]. Int J Performability Eng, 2018, 14(10): 2441-2448.

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References 0

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